Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge.

PubMed

Langkammer, Christian; Schweser, Ferdinand; Shmueli, Karin; Kames, Christian; Li, Xu; Guo, Li; Milovic, Carlos; Kim, Jinsuh; Wei, Hongjiang; Bredies, Kristian; Buch, Sagar; Guo, Yihao; Liu, Zhe; Meineke, Jakob; Rauscher, Alexander; Marques, José P; Bilgic, Berkin

2018-03-01

The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully. Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions. Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance. Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria. Magn Reson Med 79:1661-1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Map and map database of susceptibility to slope failure by sliding and earthflow in the Oakland area, California

USGS Publications Warehouse

Pike, R.J.; Graymer, R.W.; Roberts, Sebastian; Kalman, N.B.; Sobieszczyk, Steven

2001-01-01

Map data that predict the varying likelihood of landsliding can help public agencies make informed decisions on land use and zoning. This map, prepared in a geographic information system from a statistical model, estimates the relative likelihood of local slopes to fail by two processes common to an area of diverse geology, terrain, and land use centered on metropolitan Oakland. The model combines the following spatial data: (1) 120 bedrock and surficial geologic-map units, (2) ground slope calculated from a 30-m digital elevation model, (3) an inventory of 6,714 old landslide deposits (not distinguished by age or type of movement and excluding debris flows), and (4) the locations of 1,192 post-1970 landslides that damaged the built environment. The resulting index of likelihood, or susceptibility, plotted as a 1:50,000-scale map, is computed as a continuous variable over a large area (872 km2) at a comparatively fine (30 m) resolution. This new model complements landslide inventories by estimating susceptibility between existing landslide deposits, and improves upon prior susceptibility maps by quantifying the degree of susceptibility within those deposits. Susceptibility is defined for each geologic-map unit as the spatial frequency (areal percentage) of terrain occupied by old landslide deposits, adjusted locally by steepness of the topography. Susceptibility of terrain between the old landslide deposits is read directly from a slope histogram for each geologic-map unit, as the percentage (0.00 to 0.90) of 30-m cells in each one-degree slope interval that coincides with the deposits. Susceptibility within landslide deposits (0.00 to 1.33) is this same percentage raised by a multiplier (1.33) derived from the comparative frequency of recent failures within and outside the old deposits. Positive results from two evaluations of the model encourage its extension to the 10-county San Francisco Bay region and elsewhere. A similar map could be prepared for any area where the three basic constituents, a geologic map, a landslide inventory, and a slope map, are available in digital form. Added predictive power of the new susceptibility model may reside in attributes that remain to be explored?among them seismic shaking, distance to nearest road, and terrain elevation, aspect, relief, and curvature.

ANFIS modeling for the assessment of landslide susceptibility for the Cameron Highland (Malaysia)

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet; Sezer, Ebru; Gokceoglu, Candan; Buchroithner, Manfred F.

2010-05-01

Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. In landslide literature, there are several approaches such as probabilistic, bivariate and multivariate statistical models, fuzzy and artificial neural network models etc. However, a neuro-fuzzy application on the landslide susceptibility assessment has not been encountered in the literature. For this reason, this study presents the results of an adaptive neuro-fuzzy inference system (ANFIS) using remote sensing data and GIS for landslide susceptibility analysis in a part of the Cameron Highland areas in Malaysia. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments, NDVI and land cover were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, curvature, distance from drainage, lithology, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to produce the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were constructed. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values were calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 97% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient agreement between the obtained susceptibility map and the existing data on landslide areas. Qualitatively, the model yields reasonable results which can be used for preliminary land-use planning purposes. As a final conclusion, the results revealed that the ANFIS modeling is a very useful and powerful tool for the regional landslide susceptibility assessments. However, the results to be obtained from the ANFIS modeling should be assessed carefully because the overlearning may cause misleading results. To prevent overlerning, the numbers of membership functions of inputs and the number of training epochs should be selected optimally and carefully.

SU-F-I-24: Feasibility of Magnetic Susceptibility to Relative Electron Density Conversion Method for Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ito, K; Kadoya, N; Chiba, M

2016-06-15

Purpose: The aim of this study is to develop radiation treatment planning using magnetic susceptibility obtained from quantitative susceptibility mapping (QSM) via MR imaging. This study demonstrates the feasibility of a method for generating a substitute for a CT image from an MRI. Methods: The head of a healthy volunteer was scanned using a CT scanner and a 3.0 T MRI scanner. The CT imaging was performed with a slice thickness of 2.5 mm at 80 and 120 kV (dual-energy scan). These CT images were converted to relative electron density (rED) using the CT-rED conversion table generated by a previousmore » dual-energy CT scan. The CT-rED conversion table was generated using the conversion of the energy-subtracted CT number to rED via a single linear relationship. One T2 star-weighted 3D gradient echo-based sequence with four different echo times images was acquired using the MRI scanner. These T2 star-weighted images were used to estimate the phase data. To estimate the local field map, a Laplacian unwrapping of the phase and background field removal algorithm were implemented to process phase data. To generate a magnetic susceptibility map from the local field map, we used morphology enabled dipole inversion method. The rED map was resampled to the same resolution as magnetic susceptibility, and the magnetic susceptibility-rED conversion table was obtained via voxel-by-voxel mapping between the magnetic susceptibility and rED maps. Results: A correlation between magnetic susceptibility and rED is not observed through our method. Conclusion: Our results show that the correlation between magnetic susceptibility and rED is not observed. As the next step, we assume that the voxel of the magnetic susceptibility map comprises two materials, such as water (0 ppm) and bone (-2.2 ppm) or water and marrow (0.81ppm). The elements of each voxel were estimated from the ratio of the two materials.« less

Combining Quantitative Susceptibility Mapping with Automatic Zero Reference (QSM0) and Myelin Water Fraction Imaging to Quantify Iron-Related Myelin Damage in Chronic Active MS Lesions.

PubMed

Yao, Y; Nguyen, T D; Pandya, S; Zhang, Y; Hurtado Rúa, S; Kovanlikaya, I; Kuceyeski, A; Liu, Z; Wang, Y; Gauthier, S A

2018-02-01

A hyperintense rim on susceptibility in chronic MS lesions is consistent with iron deposition, and the purpose of this study was to quantify iron-related myelin damage within these lesions as compared with those without rim. Forty-six patients had 2 longitudinal quantitative susceptibility mapping with automatic zero reference scans with a mean interval of 28.9 ± 11.4 months. Myelin water fraction mapping by using fast acquisition with spiral trajectory and T2 prep was obtained at the second time point to measure myelin damage. Mixed-effects models were used to assess lesion quantitative susceptibility mapping and myelin water fraction values. Quantitative susceptibility mapping scans were on average 6.8 parts per billion higher in 116 rim-positive lesions compared with 441 rim-negative lesions ( P < .001). All rim-positive lesions retained a hyperintense rim over time, with increasing quantitative susceptibility mapping values of both the rim and core regions ( P < .001). Quantitative susceptibility mapping scans and myelin water fraction in rim-positive lesions decreased from rim to core, which is consistent with rim iron deposition. Whole lesion myelin water fractions for rim-positive and rim-negative lesions were 0.055 ± 0.07 and 0.066 ± 0.04, respectively. In the mixed-effects model, rim-positive lesions had on average 0.01 lower myelin water fraction compared with rim-negative lesions ( P < .001). The volume of the rim at the initial quantitative susceptibility mapping scan was negatively associated with follow-up myelin water fraction ( P < .01). Quantitative susceptibility mapping rim-positive lesions maintained a hyperintense rim, increased in susceptibility, and had more myelin damage compared with rim-negative lesions. Our results are consistent with the identification of chronic active MS lesions and may provide a target for therapeutic interventions to reduce myelin damage. © 2018 by American Journal of Neuroradiology.

Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Pradhan, Biswajeet; Lofman, Owe; Revhaug, Inge; Dick, Oystein B.

2012-08-01

The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the prediction capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest prediction capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2013-02-01

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

NASA Astrophysics Data System (ADS)

Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

2010-05-01

This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

Weights of Evidence Method for Landslide Susceptibility Mapping in Takengon, Central Aceh, Indonesia

NASA Astrophysics Data System (ADS)

Pamela; Sadisun, Imam A.; Arifianti, Yukni

2018-02-01

Takengon is an area prone to earthquake disaster and landslide. On July 2, 2013, Central Aceh earthquake induced large numbers of landslides in Takengon area, which resulted in casualties of 39 people. This location was chosen to assess the landslide susceptibility of Takengon, using a statistical method, referred to as the weight of evidence (WoE). This WoE model was applied to indicate the main factors influencing the landslide susceptible area and to derive landslide susceptibility map of Takengon. The 251 landslides randomly divided into two groups of modeling/training data (70%) and validation/test data sets (30%). Twelve thematic maps of evidence are slope degree, slope aspect, lithology, land cover, elevation, rainfall, lineament, peak ground acceleration, curvature, flow direction, distance to river and roads used as landslide causative factors. According to the AUC, the significant factor controlling the landslide is the slope, the slope aspect, peak ground acceleration, elevation, lithology, flow direction, lineament, and rainfall respectively. Analytical result verified by using test data of landslide shows AUC prediction rate is 0.819 and AUC success rate with all landslide data included is 0.879. This result showed the selective factors and WoE method as good models for assessing landslide susceptibility. The landslide susceptibility map of Takengon shows the probabilities, which represent relative degrees of susceptibility for landslide proneness in Takengon area.

Landslide susceptibility mapping for a part of North Anatolian Fault Zone (Northeast Turkey) using logistic regression model

NASA Astrophysics Data System (ADS)

Demir, Gökhan; aytekin, mustafa; banu ikizler, sabriye; angın, zekai

2013-04-01

The North Anatolian Fault is know as one of the most active and destructive fault zone which produced many earthquakes with high magnitudes. Along this fault zone, the morphology and the lithological features are prone to landsliding. However, many earthquake induced landslides were recorded by several studies along this fault zone, and these landslides caused both injuiries and live losts. Therefore, a detailed landslide susceptibility assessment for this area is indispancable. In this context, a landslide susceptibility assessment for the 1445 km2 area in the Kelkit River valley a part of North Anatolian Fault zone (Eastern Black Sea region of Turkey) was intended with this study, and the results of this study are summarized here. For this purpose, geographical information system (GIS) and a bivariate statistical model were used. Initially, Landslide inventory maps are prepared by using landslide data determined by field surveys and landslide data taken from General Directorate of Mineral Research and Exploration. The landslide conditioning factors are considered to be lithology, slope gradient, slope aspect, topographical elevation, distance to streams, distance to roads and distance to faults, drainage density and fault density. ArcGIS package was used to manipulate and analyze all the collected data Logistic regression method was applied to create a landslide susceptibility map. Landslide susceptibility maps were divided into five susceptibility regions such as very low, low, moderate, high and very high. The result of the analysis was verified using the inventoried landslide locations and compared with the produced probability model. For this purpose, Area Under Curvature (AUC) approach was applied, and a AUC value was obtained. Based on this AUC value, the obtained landslide susceptibility map was concluded as satisfactory. Keywords: North Anatolian Fault Zone, Landslide susceptibility map, Geographical Information Systems, Logistic Regression Analysis.

Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

NASA Astrophysics Data System (ADS)

Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

2017-10-01

In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.

PubMed

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis☆

PubMed Central

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-01-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster–Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty–sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights. PMID:25843987

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

Application of LiDAR Date to Assess the Landslide Susceptibility Map Using Weights of Evidence Method - AN Example from Podhale Region (southern Poland)

NASA Astrophysics Data System (ADS)

Kamiński, Mirosław

2016-06-01

Podhale is a region in southern Poland, which is the northernmost part of the Central Carpathian Mountains. It is characterized by the presence of a large number of landslides that threaten the local infrastructure. In an article presents application of LiDAR data and geostatistical methods to assess landslides susceptibility map. Landslide inventory map were performed using LiDAR data and field work. The Weights of Evidence method was applied to assess landslides susceptibility map. Used factors for modeling: slope gradient, slope aspect, elevation, drainage density, faults density, lithology and curvature. All maps were subdivided into different classes. Then were converted to grid format in the ArcGIS 10.0. The conditional independence test was carried out to determine factors that are conditionally independent of each other with landslides. As a result, chi-square test for further GIS analysis used only five factors: slope gradient, slope aspect, elevation, drainage density and lithology. The final prediction results, it is concluded that the susceptibility map gives useful information both on present instability of the area and its possible future evolution in agreement with the morphological evolution of the area.

Usefulness of quantitative susceptibility mapping for the diagnosis of Parkinson disease.

PubMed

Murakami, Y; Kakeda, S; Watanabe, K; Ueda, I; Ogasawara, A; Moriya, J; Ide, S; Futatsuya, K; Sato, T; Okada, K; Uozumi, T; Tsuji, S; Liu, T; Wang, Y; Korogi, Y

2015-06-01

Quantitative susceptibility mapping allows overcoming several nonlocal restrictions of susceptibility-weighted and phase imaging and enables quantification of magnetic susceptibility. We compared the diagnostic accuracy of quantitative susceptibility mapping and R2* (1/T2*) mapping to discriminate between patients with Parkinson disease and controls. For 21 patients with Parkinson disease and 21 age- and sex-matched controls, 2 radiologists measured the quantitative susceptibility mapping values and R2* values in 6 brain structures (the thalamus, putamen, caudate nucleus, pallidum, substantia nigra, and red nucleus). The quantitative susceptibility mapping values and R2* values of the substantia nigra were significantly higher in patients with Parkinson disease (P < .01); measurements in other brain regions did not differ significantly between patients and controls. For the discrimination of patients with Parkinson disease from controls, receiver operating characteristic analysis suggested that the optimal cutoff values for the substantia nigra, based on the Youden Index, were >0.210 for quantitative susceptibility mapping and >28.8 for R2*. The sensitivity, specificity, and accuracy of quantitative susceptibility mapping were 90% (19 of 21), 86% (18 of 21), and 88% (37 of 42), respectively; for R2* mapping, they were 81% (17 of 21), 52% (11 of 21), and 67% (28 of 42). Pair-wise comparisons showed that the areas under the receiver operating characteristic curves were significantly larger for quantitative susceptibility mapping than for R2* mapping (0.91 versus 0.69, P < .05). Quantitative susceptibility mapping showed higher diagnostic performance than R2* mapping for the discrimination between patients with Parkinson disease and controls. © 2015 by American Journal of Neuroradiology.

Preliminary soil-slip susceptibility maps, southwestern California

USGS Publications Warehouse

Morton, Douglas M.; Alvarez, Rachel M.; Campbell, Russell H.; Digital preparation by Bovard, Kelly R.; Brown, D.T.; Corriea, K.M.; Lesser, J.N.

2003-01-01

This group of maps shows relative susceptibility of hill slopes to the initiation sites of rainfall-triggered soil slip-debris flows in southwestern California. As such, the maps offer a partial answer to one part of the three parts necessary to predict the soil-slip/debris-flow process. A complete prediction of the process would include assessments of “where”, “when”, and “how big”. These maps empirically show part of the “where” of prediction (i.e., relative susceptibility to sites of initiation of the soil slips) but do not attempt to show the extent of run out of the resultant debris flows. Some information pertinent to “when” the process might begin is developed. “When” is determined mostly by dynamic factors such as rainfall rate and duration, for which local variations are not amenable to long-term prediction. “When” information is not provided on the maps but is described later in this narrative. The prediction of “how big” is addressed indirectly by restricting the maps to a single type of landslide process—soil slip-debris flows. The susceptibility maps were created through an iterative process from two kinds of information. First, locations of sites of past soil slips were obtained from inventory maps of past events. Aerial photographs, taken during six rainy seasons that produced abundant soil slips, were used as the basis for soil slip-debris flow inventory. Second, digital elevation models (DEM) of the areas that were inventoried were used to analyze the spatial characteristics of soil slip locations. These data were supplemented by observations made on the ground. Certain physical attributes of the locations of the soil-slip debris flows were found to be important and others were not. The most important attribute was the mapped bedrock formation at the site of initiation of the soil slip. However, because the soil slips occur in surficial materials overlying the bedrocks units, the bedrock formation can only serve as a surrogate for the susceptibility of the overlying surficial materials. The maps of susceptibility were created from those physical attributes learned to be important from the inventories. The multiple inventories allow a model to be created from one set of inventory data and evaluated with others. The resultant maps of relative susceptibility represent the best estimate generated from available inventory and DEM data. Slope and aspect values used in the susceptibility analysis were 10-meter DEM cells at a scale of 1:24,000. For most of the area 10-meter DEMs were available; for those quadrangles that have only 30-meter DEMs, the 30-meter DEMS were resampled to 10-meters to maintain resolution of 10-meter cells. Geologic unit values used in the susceptibility analysis were five-meter cells. For convenience, the soil slip susceptibility values are assembled on 1:100,000-scale bases. Any area of the 1:100,000-scale maps can be transferred to 1:24,000-scale base without any loss of accuracy. Figure 32 is an example of part of a 1:100,000-scale susceptibility map transferred back to a 1:24,000-scale quadrangle.

Quantitative susceptibility mapping (QSM) of white matter multiple sclerosis lesions: interpreting positive susceptibility and the presence of iron

PubMed Central

Wisnieff, Cynthia; Ramanan, Sriram; Olesik, John; Gauthier, Susan; Wang, Yi; Pitt, David

2014-01-01

Purpose Within multiple sclerosis (MS) lesions iron is present in chronically activated microglia. Thus, iron detection with MRI might provide a biomarker for chronic inflammation within lesions. Here, we examine contributions of iron and myelin to magnetic susceptibility of lesions on quantitative susceptibility mapping (QSM). Methods Fixed MS brain tissue was assessed with MRI including gradient echo data, which was processed to generate field (phase), R2* and QSM. Five lesions were sectioned and evaluated by immunohistochemistry for presence of myelin, iron and microglia/macrophages. Two of the lesions had an elemental analysis for iron concentration mapping, and their phospholipid content was estimated from the difference in the iron and QSM data. Results Three of the five lesions had substantial iron deposition that was associated with microglia and positive susceptibility values. For the two lesions with elemental analysis, the QSM derived phospholipid content maps were consistent with myelin labeled histology. Conclusion Positive susceptibility values with respect to water indicate the presence of iron in MS lesions, though both demyelination and iron deposition contribute to QSM. PMID:25137340

Computed inverse MRI for magnetic susceptibility map reconstruction

PubMed Central

Chen, Zikuan; Calhoun, Vince

2015-01-01

Objective This paper reports on a computed inverse magnetic resonance imaging (CIMRI) model for reconstructing the magnetic susceptibility source from MRI data using a two-step computational approach. Methods The forward T2*-weighted MRI (T2*MRI) process is decomposed into two steps: 1) from magnetic susceptibility source to fieldmap establishment via magnetization in a main field, and 2) from fieldmap to MR image formation by intravoxel dephasing average. The proposed CIMRI model includes two inverse steps to reverse the T2*MRI procedure: fieldmap calculation from MR phase image and susceptibility source calculation from the fieldmap. The inverse step from fieldmap to susceptibility map is a 3D ill-posed deconvolution problem, which can be solved by three kinds of approaches: Tikhonov-regularized matrix inverse, inverse filtering with a truncated filter, and total variation (TV) iteration. By numerical simulation, we validate the CIMRI model by comparing the reconstructed susceptibility maps for a predefined susceptibility source. Results Numerical simulations of CIMRI show that the split Bregman TV iteration solver can reconstruct the susceptibility map from a MR phase image with high fidelity (spatial correlation≈0.99). The split Bregman TV iteration solver includes noise reduction, edge preservation, and image energy conservation. For applications to brain susceptibility reconstruction, it is important to calibrate the TV iteration program by selecting suitable values of the regularization parameter. Conclusions The proposed CIMRI model can reconstruct the magnetic susceptibility source of T2*MRI by two computational steps: calculating the fieldmap from the phase image and reconstructing the susceptibility map from the fieldmap. The crux of CIMRI lies in an ill-posed 3D deconvolution problem, which can be effectively solved by the split Bregman TV iteration algorithm. PMID:22446372

Mapping Landslides Susceptibility in a Traditional Northern Nigerian City

NASA Astrophysics Data System (ADS)

Oluwafemi, Olawale A.; Yakubu, Tahir A.; Muhammad, Mahmud U.; Shitta, Nyofo; Akinwumiju, Akinola S.

2018-05-01

As a result of dearth of relevant information about Landslides Susceptibility in Nigeria, the monitoring and assessment appears intractable. Hence, the study developed a Remote Sensing approach to mapping landslides susceptibility, landuse and landcover analysis in Jos South LGA, Plateau State, Nigeria. Field Observation, SPOT 5 2009 and 2012, ASTER DEM 2009, Geological Map 2006, Topographical Map 1966 were used to map Landslide Susceptibility and Landuse /Lancover Analysis in the study area. Geospatial Analytical Operations employed using ArcGIS 10.3 and Erdas Imagine 2014 include Spatial Modeling, Vectorization, Pre-lineament Extraction, Image Processing among others. Result showed that 72.38 % of the study area is underlain by granitic rocks. The landuse/cover types delineated for the study area include floodplain (29.27 %), farmland (23.96 %), sparsely vegetated land (15.43 %), built up area (13.65 %), vegetated outcrop (8.48 %), light vegetation (5.37 %), thick vegetation (2.39 %), water body (0.58 %), plantation (0.50 %) and mining pond (0.37 %). Landslide Susceptibility Analysis also revealed that 87 % of the study area is relatively at low to very low risk of landslide event. While only 13 % of the study area is at high to very high risk of landslide event. The study revealed that the susceptibility of landslide event is very low in the study area. However, possible landslide event in the hot spots could be pronounced and could destabilize the natural and man-made environmental systems of the study area.

Computed inverse resonance imaging for magnetic susceptibility map reconstruction.

PubMed

Chen, Zikuan; Calhoun, Vince

2012-01-01

This article reports a computed inverse magnetic resonance imaging (CIMRI) model for reconstructing the magnetic susceptibility source from MRI data using a 2-step computational approach. The forward T2*-weighted MRI (T2*MRI) process is broken down into 2 steps: (1) from magnetic susceptibility source to field map establishment via magnetization in the main field and (2) from field map to MR image formation by intravoxel dephasing average. The proposed CIMRI model includes 2 inverse steps to reverse the T2*MRI procedure: field map calculation from MR-phase image and susceptibility source calculation from the field map. The inverse step from field map to susceptibility map is a 3-dimensional ill-posed deconvolution problem, which can be solved with 3 kinds of approaches: the Tikhonov-regularized matrix inverse, inverse filtering with a truncated filter, and total variation (TV) iteration. By numerical simulation, we validate the CIMRI model by comparing the reconstructed susceptibility maps for a predefined susceptibility source. Numerical simulations of CIMRI show that the split Bregman TV iteration solver can reconstruct the susceptibility map from an MR-phase image with high fidelity (spatial correlation ≈ 0.99). The split Bregman TV iteration solver includes noise reduction, edge preservation, and image energy conservation. For applications to brain susceptibility reconstruction, it is important to calibrate the TV iteration program by selecting suitable values of the regularization parameter. The proposed CIMRI model can reconstruct the magnetic susceptibility source of T2*MRI by 2 computational steps: calculating the field map from the phase image and reconstructing the susceptibility map from the field map. The crux of CIMRI lies in an ill-posed 3-dimensional deconvolution problem, which can be effectively solved by the split Bregman TV iteration algorithm.

A variational image-based approach to the correction of susceptibility artifacts in the alignment of diffusion weighted and structural MRI.

PubMed

Tao, Ran; Fletcher, P Thomas; Gerber, Samuel; Whitaker, Ross T

2009-01-01

This paper presents a method for correcting the geometric and greyscale distortions in diffusion-weighted MRI that result from inhomogeneities in the static magnetic field. These inhomogeneities may due to imperfections in the magnet or to spatial variations in the magnetic susceptibility of the object being imaged--so called susceptibility artifacts. Echo-planar imaging (EPI), used in virtually all diffusion weighted acquisition protocols, assumes a homogeneous static field, which generally does not hold for head MRI. The resulting distortions are significant, sometimes more than ten millimeters. These artifacts impede accurate alignment of diffusion images with structural MRI, and are generally considered an obstacle to the joint analysis of connectivity and structure in head MRI. In principle, susceptibility artifacts can be corrected by acquiring (and applying) a field map. However, as shown in the literature and demonstrated in this paper, field map corrections of susceptibility artifacts are not entirely accurate and reliable, and thus field maps do not produce reliable alignment of EPIs with corresponding structural images. This paper presents a new, image-based method for correcting susceptibility artifacts. The method relies on a variational formulation of the match between an EPI baseline image and a corresponding T2-weighted structural image but also specifically accounts for the physics of susceptibility artifacts. We derive a set of partial differential equations associated with the optimization, describe the numerical methods for solving these equations, and present results that demonstrate the effectiveness of the proposed method compared with field-map correction.

Susceptibility and triggering scenarios at a regional scale for shallow landslides

NASA Astrophysics Data System (ADS)

Gullà, G.; Antronico, L.; Iaquinta, P.; Terranova, O.

2008-07-01

The work aims at identifying susceptible areas and pluviometric triggering scenarios at a regional scale in Calabria (Italy), with reference to shallow landsliding events. The proposed methodology follows a statistical approach and uses a database linked to a GIS that has been created to support the various steps of spatial data management and manipulation. The shallow landslide predisposing factors taken into account are derived from (i) the 40-m digital terrain model of the region, an ˜ 15,075 km 2 extension; (ii) outcropping lithology; (iii) soils; and (iv) land use. More precisely, a map of the slopes has been drawn from the digital terrain model. Two kinds of covers [prevalently coarse-grained (CG cover) or fine-grained (FG cover)] were identified, referring to the geotechnical characteristics of geomaterial covers and to the lithology map; soilscapes were drawn from soil maps; and finally, the land use map was employed without any prior processing. Subsequently, the inventory maps of some shallow landsliding events, totaling more than 30,000 instabilities of the past and detected by field surveys and photo aerial restitution, were employed to calibrate the relative importance of these predisposing factors. The use of single factors (first level analysis) therefore provides three different susceptibility maps. Second level analysis, however, enables better location of areas susceptible to shallow landsliding events by crossing the single susceptibility maps. On the basis of the susceptibility map obtained by the second level analysis, five different classes of susceptibility to shallow landsliding events have been outlined over the regional territory: 8.9% of the regional territory shows very high susceptibility, 14.3% high susceptibility, 15% moderate susceptibility, 3.6% low susceptibility, and finally, about 58% very low susceptibility. Finally, the maps of two significant shallow landsliding events of the past and their related rainfalls have been utilized to identify the relevant pluviometric triggering scenarios. By using 205 daily rainfall series, different triggering pluviometric scenarios have been identified with reference to CG and FG covers: a value of 365 mm of the total rainfall of the event and/or 170 mm/d of the rainfall maximum intensity and a value of 325 mm of the total rainfall of the event and/or 158 mm/d of the rainfall maximum intensity are able to trigger shallow landsliding events for CG and FG covers, respectively. The results obtained from this study can help administrative authorities to plan future development activities and mitigation measures in shallow landslide-prone areas. In addition, the proposed methodology can be useful in managing emergency situations at a regional scale for shallow landsliding events triggered by intense rainfalls; through this approach, the susceptibility and the pluviometric triggering scenario maps will be improved by means of finer calibration of the involved factors.

A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping

PubMed Central

Feizizadeh, Bakhtiar; Shadman Roodposhti, Majid; Jankowski, Piotr; Blaschke, Thomas

2014-01-01

Landslide susceptibility mapping (LSM) is making increasing use of GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. We have developed a new multi-criteria decision analysis (MCDA) method for LSM and applied it to the Izeh River basin in south-western Iran. Our method is based on fuzzy membership functions (FMFs) derived from GIS analysis. It makes use of nine causal landslide factors identified by local landslide experts. Fuzzy set theory was first integrated with an analytical hierarchy process (AHP) in order to use pairwise comparisons to compare LSM criteria for ranking purposes. FMFs were then applied in order to determine the criteria weights to be used in the development of a landslide susceptibility map. Finally, a landslide inventory database was used to validate the LSM map by comparing it with known landslides within the study area. Results indicated that the integration of fuzzy set theory with AHP produced significantly improved accuracies and a high level of reliability in the resulting landslide susceptibility map. Approximately 53% of known landslides within our study area fell within zones classified as having “very high susceptibility”, with the further 31% falling into zones classified as having “high susceptibility”. PMID:26089577

Comparing physically-based and statistical landslide susceptibility model outputs - a case study from Lower Austria

NASA Astrophysics Data System (ADS)

Canli, Ekrem; Thiebes, Benni; Petschko, Helene; Glade, Thomas

2015-04-01

By now there is a broad consensus that due to human-induced global change the frequency and magnitude of heavy precipitation events is expected to increase in certain parts of the world. Given the fact, that rainfall serves as the most common triggering agent for landslide initiation, also an increased landside activity can be expected there. Landslide occurrence is a globally spread phenomenon that clearly needs to be handled. The present and well known problems in modelling landslide susceptibility and hazard give uncertain results in the prediction. This includes the lack of a universal applicable modelling solution for adequately assessing landslide susceptibility (which can be seen as the relative indication of the spatial probability of landslide initiation). Generally speaking, there are three major approaches for performing landslide susceptibility analysis: heuristic, statistical and deterministic models, all with different assumptions, its distinctive data requirements and differently interpretable outcomes. Still, detailed comparison of resulting landslide susceptibility maps are rare. In this presentation, the susceptibility modelling outputs of a deterministic model (Stability INdex MAPping - SINMAP) and a statistical modelling approach (generalized additive model - GAM) are compared. SINMAP is an infinite slope stability model which requires parameterization of soil mechanical parameters. Modelling with the generalized additive model, which represents a non-linear extension of a generalized linear model, requires a high quality landslide inventory that serves as the dependent variable in the statistical approach. Both methods rely on topographical data derived from the DTM. The comparison has been carried out in a study area located in the district of Waidhofen/Ybbs in Lower Austria. For the whole district (ca. 132 km²), 1063 landslides have been mapped and partially used within the analysis and the validation of the model outputs. The respective susceptibility maps have been reclassified to contain three susceptibility classes each. The comparison of the susceptibility maps was performed on a grid cell basis. A match of the maps was observed for grid cells located in the same susceptibility class. In contrast, a mismatch or deviation was observed for locations with different assigned susceptibility classes (up to two classes' difference). Although the modelling approaches differ significantly, more than 70% of the pixels reveal a match in the same susceptibility class. A mismatch by two classes' difference occurred in less than 2% of all pixels. Although the result looks promising and strengthens the confidence in the susceptibility zonation for this area, some of the general drawbacks related to the respective approaches still have to be addressed in further detail. Future work is heading towards an integration of probabilistic aspects into deterministic modelling.

Quantitative Oxygenation Venography from MRI Phase

PubMed Central

Fan, Audrey P.; Bilgic, Berkin; Gagnon, Louis; Witzel, Thomas; Bhat, Himanshu; Rosen, Bruce R.; Adalsteinsson, Elfar

2014-01-01

Purpose To demonstrate acquisition and processing methods for quantitative oxygenation venograms that map in vivo oxygen saturation (SvO2) along cerebral venous vasculature. Methods Regularized quantitative susceptibility mapping (QSM) is used to reconstruct susceptibility values and estimate SvO2 in veins. QSM with ℓ1 and ℓ2 regularization are compared in numerical simulations of vessel structures with known magnetic susceptibility. Dual-echo, flow-compensated phase images are collected in three healthy volunteers to create QSM images. Bright veins in the susceptibility maps are vectorized and used to form a three-dimensional vascular mesh, or venogram, along which to display SvO2 values from QSM. Results Quantitative oxygenation venograms that map SvO2 along brain vessels of arbitrary orientation and geometry are shown in vivo. SvO2 values in major cerebral veins lie within the normal physiological range reported by 15O positron emission tomography. SvO2 from QSM is consistent with previous MR susceptometry methods for vessel segments oriented parallel to the main magnetic field. In vessel simulations, ℓ1 regularization results in less than 10% SvO2 absolute error across all vessel tilt orientations and provides more accurate SvO2 estimation than ℓ2 regularization. Conclusion The proposed analysis of susceptibility images enables reliable mapping of quantitative SvO2 along venograms and may facilitate clinical use of venous oxygenation imaging. PMID:24006229

Evaluation of Landslide Mapping Techniques and LiDAR-based Conditioning Factors

NASA Astrophysics Data System (ADS)

Mahalingam, R.; Olsen, M. J.

2014-12-01

Landslides are a major geohazard, which result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping can help communities to plan and prepare for these damaging events. Mapping landslide susceptible locations using GIS and remote sensing techniques is gaining popularity in the past three decades. These efforts use a wide variety of procedures and consider a wide range of factors. Unfortunately, each study is often completed differently and independently of others. Further, the quality of the datasets used varies in terms of source, data collection, and generation, which can propagate errors or inconsistencies into the resulting output maps. Light detection and ranging (LiDAR) has proved to have higher accuracy in representing the continuous topographic surface, which can help minimize this uncertainty. The primary objectives of this paper are to investigate the applicability and performance of terrain factors in landslide hazard mapping, determine if LiDAR-derived datasets (slope, slope roughness, terrain roughness, stream power index and compound topographic index) can be used for predictive mapping without data representing other common landslide conditioning factors, and evaluate the differences in landslide susceptibility mapping using widely-used statistical approaches. The aforementioned factors were used to produce landslide susceptibility maps for a 140 km2 study area in northwest Oregon using six representative techniques: frequency ratio, weights of evidence, logistic regression, discriminant analysis, artificial neural network, and support vector machine. Most notably, the research showed an advantage in selecting fewer critical conditioning factors. The most reliable factors all could be derived from a single LiDAR DEM, reducing the need for laborious and costly data gathering. Most of the six techniques showed similar statistical results; however, ANN showed less accuracy for predictive mapping. Keywords : LiDAR, Landslides, Oregon, Inventory, Hazard

Mapping erosion susceptibility by a multivariate statistical method: A case study from the Ayvalık region, NW Turkey

NASA Astrophysics Data System (ADS)

Akgün, Aykut; Türk, Necdet

2011-09-01

Erosion is one of the most important natural hazard phenomena in the world, and it poses a significant threat to Turkey in terms of land degredation and desertification. To cope with this problem, we must determine which areas are erosion-prone. Many studies have been carried out and different models and methods have been used to this end. In this study, we used a logistic regression to prepare an erosion susceptibility map for the Ayvalık region in Balıkesir (NW Turkey). The following were our assessment parameters: weathering grades of rocks, slope gradient, structural lineament density, drainage density, land cover, stream power index (SPI) and profile curvature. These were processed by Idrisi Kilimanjaro GIS software. We used logistic regression analysis to relate predictor variables to the occurrence or non-occurrence of gully erosion sites within geographic cells, and then we used this relationship to produce a probability map for future erosion sites. The results indicate that lineament density, weathering grades of rocks and drainage density are the most important variables governing erosion susceptibility. Other variables, such as land cover and slope gradient, were revealed as secondary important variables. Highly weathered basalt, andesite, basaltic andesite and lacustrine sediments were the units most susceptible to erosion. In order to calculate the prediction accuracy of the erosion susceptibility map generated, we compared it with the map showing the gully erosion areas. On the basis of this comparison, the area under curvature (AUC) value was found to be 0.81. This result suggests that the erosion susceptibility map we generated is accurate.

Quantitative Susceptibility Mapping of the Midbrain in Parkinson’s Disease

PubMed Central

Du, Guangwei; Liu, Tian; Lewis, Mechelle M.; Kong, Lan; Wang, Yi; Connor, James; Mailman, Richard B.; Huang, Xuemei

2017-01-01

Background Parkinson’s disease (PD) is marked pathologically by dopamine neuron loss and iron overload in the substantia nigra pars compacta. Midbrain iron content is reported to be increased in PD based on magnetic resonance imaging (MRI) R2* changes. Because quantitative susceptibility mapping is a novel MRI approach to measure iron content, we compared it with R2* for assessing midbrain changes in PD. Methods Quantitative susceptibility mapping and R2* maps were obtained from 47 PD patients and 47 healthy controls. Midbrain susceptibility and R2* values were analyzed by using both voxel-based and region-of-interest approaches in normalized space, and analyzed along with clinical data, including disease duration, Unified Parkinson’s Disease Rating Scale (UPDRS) I, II, and III sub-scores, and levodopa-equivalent daily dosage. All studies were done while PD patients were “on drug.” Results Compared with controls, PD patients showed significantly increased susceptibility values in both right (cluster size = 106 mm3) and left (164 mm3) midbrain, located ventrolateral to the red nucleus that corresponded to the substantia nigra pars compacta. Susceptibility values in this region were correlated significantly with disease duration, UPDRS II, and levodopa-equivalent daily dosage. Conversely, R2* was increased significantly only in a much smaller region (62 mm3) of the left lateral substantia nigra pars compacta and was not significantly correlated with clinical parameters. Conclusion The use of quantitative susceptibility mapping demonstrated marked nigral changes that correlated with clinical PD status more sensitively than R2*. These data suggest that quantitative susceptibility mapping may be a superior imaging biomarker to R2* for estimating brain iron levels in PD. PMID:26362242

Landslide susceptibility mapping by combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process in Dozain basin

NASA Astrophysics Data System (ADS)

Tazik, E.; Jahantab, Z.; Bakhtiari, M.; Rezaei, A.; Kazem Alavipanah, S.

2014-10-01

Landslides are among the most important natural hazards that lead to modification of the environment. Therefore, studying of this phenomenon is so important in many areas. Because of the climate conditions, geologic, and geomorphologic characteristics of the region, the purpose of this study was landslide hazard assessment using Fuzzy Logic, frequency ratio and Analytical Hierarchy Process method in Dozein basin, Iran. At first, landslides occurred in Dozein basin were identified using aerial photos and field studies. The influenced landslide parameters that were used in this study including slope, aspect, elevation, lithology, precipitation, land cover, distance from fault, distance from road and distance from river were obtained from different sources and maps. Using these factors and the identified landslide, the fuzzy membership values were calculated by frequency ratio. Then to account for the importance of each of the factors in the landslide susceptibility, weights of each factor were determined based on questionnaire and AHP method. Finally, fuzzy map of each factor was multiplied to its weight that obtained using AHP method. At the end, for computing prediction accuracy, the produced map was verified by comparing to existing landslide locations. These results indicate that the combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method are relatively good estimators of landslide susceptibility in the study area. According to landslide susceptibility map about 51% of the occurred landslide fall into the high and very high susceptibility zones of the landslide susceptibility map, but approximately 26 % of them indeed located in the low and very low susceptibility zones.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

NASA Astrophysics Data System (ADS)

Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

Landslide susceptibility assessment and validation in the framework of municipal planning in Portugal: the case of Loures Municipality.

PubMed

Guillard, Clemence; Zezere, Jose

2012-10-01

The legislation that demands the evaluation of landslide susceptibility in Portugal at the municipal level is the National Ecological Reserve (NER). A methodology for the evaluation of landslide susceptibility to be used in municipal planning is applied in Loures Municipality (169.3 km²) located north of Lisbon (Portugal). A landslide inventory was made for the whole area interpreting orthophoto maps and aerial photographs and using standard geomorphologic techniques in field work. It consists of 686 polygons, each polygon representing a rotational, a deep translational or a shallow translational slide, and is integrated into a GIS database. Landslide susceptibility is evaluated using algorithms based on statistical/probabilistic analysis (Information Value Method) over unique-condition terrain units in a raster basis. Three susceptibility models are elaborated independently according to the type of slide (rotational, deep translational, shallow translational). The landslide susceptibility maps are prepared by sorting all pixels according to the pixel susceptibility value in descending order. The robustness and accuracy of the landslide susceptibility models are evaluated by prediction-rate curves, which are used for the quantitative interpretation of the landslide susceptibility maps. Unstable slopes that have to be included into the National Ecological Reserve are extracted from the three susceptibility maps following the general rules to draw the NER that state that the area to be included in the NER should guarantee the inclusion of at least 70 % of the landslides identified in the landslide inventory. The obtained results allow us to conclude that 70 % of the future landslides should occur in these areas, classified as most susceptible to landslides corresponding to 20.3 % of the total area of Municipality. Thus, the consideration of these 20.3 % as regards prevention and protection of landslide risk could potentially reduce damage resulting from 70 % of future landslides in the Loures Municipality.

Landslides susceptibility mapping at Gunung Ciremai National Park

NASA Astrophysics Data System (ADS)

Faizin; Nur, Bambang Azis

2018-02-01

In addition to agriculture, tourism became one of primary economic income for communities around Mount Ciremai, West, Java. Unfortunately, the landscape of West Java has many potential causes to disasters, mainly landslides. Mapping of disaster susceptibility area is needed as a consideration of tourism planning. The study was conducted in Gunung Ciremai National Park, West Java. This paper propose a methodology to map landslides susceptibilities based on spatial data. Using Geographic Information System tools, several environmental parameters such as slope, land use, elevation, and lithology are scored to build a landslide susceptibility map. Then, susceptibility map is overlaid with Utilization Zone.

Quantitative Susceptibility Mapping of Human Brain Reflects Spatial Variation in Tissue Composition

PubMed Central

Li, Wei; Wu, Bing; Liu, Chunlei

2011-01-01

Image phase from gradient echo MRI provides a unique contrast that reflects brain tissue composition variations, such as iron and myelin distribution. Phase imaging is emerging as a powerful tool for the investigation of functional brain anatomy and disease diagnosis. However, the quantitative value of phase is compromised by its nonlocal and orientation dependent properties. There is an increasing need for reliable quantification of magnetic susceptibility, the intrinsic property of tissue. In this study, we developed a novel and accurate susceptibility mapping method that is also phase-wrap insensitive. The proposed susceptibility mapping method utilized two complementary equations: (1) the Fourier relationship of phase and magnetic susceptibility; and (2) the first-order partial derivative of the first equation in the spatial frequency domain. In numerical simulation, this method reconstructed the susceptibility map almost free of streaking artifact. Further, the iterative implementation of this method allowed for high quality reconstruction of susceptibility maps of human brain in vivo. The reconstructed susceptibility map provided excellent contrast of iron-rich deep nuclei and white matter bundles from surrounding tissues. Further, it also revealed anisotropic magnetic susceptibility in brain white matter. Hence, the proposed susceptibility mapping method may provide a powerful tool for the study of brain physiology and pathophysiology. Further elucidation of anisotropic magnetic susceptibility in vivo may allow us to gain more insight into the white matter microarchitectures. PMID:21224002

Landslide Susceptibility Analysis by the comparison and integration of Random Forest and Logistic Regression methods; application to the disaster of Nova Friburgo - Rio de Janeiro, Brasil (January 2011)

NASA Astrophysics Data System (ADS)

Esposito, Carlo; Barra, Anna; Evans, Stephen G.; Scarascia Mugnozza, Gabriele; Delaney, Keith

2014-05-01

The study of landslide susceptibility by multivariate statistical methods is based on finding a quantitative relationship between controlling factors and landslide occurrence. Such studies have become popular in the last few decades thanks to the development of geographic information systems (GIS) software and the related improved data management. In this work we applied a statistical approach to an area of high landslide susceptibility mainly due to its tropical climate and geological-geomorphological setting. The study area is located in the south-east region of Brazil that has frequently been affected by flood and landslide hazard, especially because of heavy rainfall events during the summer season. In this work we studied a disastrous event that occurred on January 11th and 12th of 2011, which involved Região Serrana (the mountainous region of Rio de Janeiro State) and caused more than 5000 landslides and at least 904 deaths. In order to produce susceptibility maps, we focused our attention on an area of 93,6 km2 that includes Nova Friburgo city. We utilized two different multivariate statistic methods: Logistic Regression (LR), already widely used in applied geosciences, and Random Forest (RF), which has only recently been applied to landslide susceptibility analysis. With reference to each mapping unit, the first method (LR) results in a probability of landslide occurrence, while the second one (RF) gives a prediction in terms of % of area susceptible to slope failure. With this aim in mind, a landslide inventory map (related to the studied event) has been drawn up through analyses of high-resolution GeoEye satellite images, in a GIS environment. Data layers of 11 causative factors have been created and processed in order to be used as continuous numerical or discrete categorical variables in statistical analysis. In particular, the logistic regression method has frequent difficulties in managing numerical continuous and discrete categorical variables together; therefore in our work we tried different methods to process categorical variables , until we obtained a statistically significant model. The outcomes of the two statistical methods (RF and LR) have been tested with a spatial validation and gave us two susceptibility maps. The significance of the models is quantified in terms of Area Under ROC Curve (AUC resulted in 0.81 for RF model and in 0.72 for LR model). In the first instance, a graphical comparison of the two methods shows a good correspondence between them. Further, we integrated results in a unique susceptibility map which maintains both information of probability of occurrence and % of area of landslide detachment, resulting from LR and RF respectively. In fact, in view of a landslide susceptibility classification of the study area, the former is less accurate but gives easily classifiable results, while the latter is more accurate but the results can be only subjectively classified. The obtained "integrated" susceptibility map preserves information about the probability that a given % of area could fail for each mapping unit.

Multidisciplinary approach to evaluate landslide susceptibility along highway in northern Calabria, Italy

NASA Astrophysics Data System (ADS)

Muto, Francesco; Conforti, Massimo; Critelli, Salvatore; Fabbricatore, Davide; Filomena, Luciana; Rago, Valeria; Robustelli, Gaetano; Scarciglia, Fabio; Versace, Pasquale

2014-05-01

The interaction of landslides with linear infrastructures is often the cause of disasters. In Italy landslide impact on roads, railways and buildings cause millions of Euro per year in damage and restoration as well. The proposed study is aimed to the landslide susceptibility evaluation using a multidisciplinary approach: geological and geomorphological survey, statistical analysis and GIS technique, along a section of highway "A3 (Salerno-Reggio Calabria)" between Cosenza Sud and Altilia, northern Calabria. This study is included in a wider research project, named: PON01-01503, Landslides Early Warning-Sistemi integrati per il monitoraggio e la mitigazione del rischio idrogeologico lungo le grandi vie di comunicazione - aimed at the hydrogeological risk mitigation and at the early warning along the highways. The work was first based on air-photo interpretations and field investigations, in order to realize the geological map, geomorphological map and landslide inventory map. In the study area the geomorphology is strongly controlled by its bedrock geology and tectonics. The bedrock geology consists of Neogene sedimentary rocks that cover a thick stack of allochthonous nappes. These nappes consist of crystalline rocks mainly gneiss, phyllite and schist. A total of 835 landslides were mapped and the type of movement are represented mainly by slides and complex and subordinately flow. In order to estimate and validate landslide susceptibility the landslides were divided in two group. One group (training set) was used to prepare susceptibility map and the second group (validation set) to validate the map. Then, the selection of predisposing factors was performed, according with the geological and geomorphological settings of the study area: lithology, distance from tectonic elements, land use, slope, aspect, stream power index (SPI) and plan curvature. In order to evaluate landslide susceptibility Conditional Analysis was applied to Unique Conditions Units (UCUs), that are a unique combination of the predisposing factors. Subsequently, the landslide area is determined within each UCU and the landslide density is computed. The outcome of the study was a classification of the study area into four susceptibility classes, ranked from low to very high. The results showed that the 33% of the study area is characterized by a high to very high degree of susceptibility. The validation procedure results, obtained by crossing the group of the landslide of validation set with the susceptibility map, showed that the predictive model is generally satisfactory; therefore, over 75% of the landslide of validation set is correctly classified falling in high and very high susceptibility classes. The consistency of the model is also suggested by computing the seed cell area index (SCAI) because the high and very high susceptibility classes have very low SCAI values, whereas the SCAI values of the very low and low susceptibility classes are very high. Finally, the landslide susceptibility map provides the baseline information for further evaluations of landslide hazards and related risks.

Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation with Brain Iron in Normal Aging

PubMed Central

Poynton, Clare; Jenkinson, Mark; Adalsteinsson, Elfar; Sullivan, Edith V.; Pfefferbaum, Adolf; Wells, William

2015-01-01

There is increasing evidence that iron deposition occurs in specific regions of the brain in normal aging and neurodegenerative disorders such as Parkinson's, Huntington's, and Alzheimer's disease. Iron deposition changes the magnetic susceptibility of tissue, which alters the MR signal phase, and allows estimation of susceptibility differences using quantitative susceptibility mapping (QSM). We present a method for quantifying susceptibility by inversion of a perturbation model, or ‘QSIP’. The perturbation model relates phase to susceptibility using a kernel calculated in the spatial domain, in contrast to previous Fourier-based techniques. A tissue/air susceptibility atlas is used to estimate B0 inhomogeneity. QSIP estimates in young and elderly subjects are compared to postmortem iron estimates, maps of the Field-Dependent Relaxation Rate Increase (FDRI), and the L1-QSM method. Results for both groups showed excellent agreement with published postmortem data and in-vivo FDRI: statistically significant Spearman correlations ranging from Rho = 0.905 to Rho = 1.00 were obtained. QSIP also showed improvement over FDRI and L1-QSM: reduced variance in susceptibility estimates and statistically significant group differences were detected in striatal and brainstem nuclei, consistent with age-dependent iron accumulation in these regions. PMID:25248179

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

PubMed Central

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-01-01

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides. PMID:27187430

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

PubMed

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-05-11

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

NASA Astrophysics Data System (ADS)

Oh, Hyun-Joo; Lee, Saro; Chotikasathien, Wisut; Kim, Chang Hwan; Kwon, Ju Hyoung

2009-04-01

For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.

An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping.

PubMed

Feizizadeh, Bakhtiar; Blaschke, Thomas

2014-03-04

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster-Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster-Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results.

Complex geohazard susceptibility zoning for effective landuse planning and catastroph prevention in developing countries

NASA Astrophysics Data System (ADS)

Hradecky, P.; Baron, I.

2012-04-01

The Czech Geological Survey conducted projects of geological mapping and complex geohazard susceptibility zoning in Nicaragua in the years 1997-2009. For selected areas in vicinity of major cities and towns basic geological maps at a scalle 1:50,000, maps of geomorphic features (Geomorphic Inventory Maps), Morphostructural Maps of estimated fault zones, and derived Geohazard Susceptibility maps were done. These maps were prepared during field campaigns by direct field mapping, analysis of remote-sensing data, communicating the local authorities, interwieving the local inhabitants and with very close cooperation with the local partner of the projects - the Instituto Nicaragüense de Estudios Territoriales (INETER). The resulting maps and explanatory reports presented the dangerous natural processes that occurred in each respective area in the past and proposed preventive measures in detail. Zones evaluated as highly susceptible, e.g., to (i) mass movements, (ii) large inundations, (iii) torrential flooding, (iv) seismogenic liquefaction, etc., were presented in bold colours on the maps. Such maps and reports were presented to local authorities and inhabitants of respective cities during public breefings at the end of each mapping campaign. In such a way, areas of Pacific volcanic ridge (1997-2003), Jinotega (2004), Somoto (2005), Estelí (2006), Boaco and Santa Lucia (2007, 2008), Sebaco (2008) and Jalapa (2009) were elaborated. The maps then served to the INETER for implementation into the landuse plans, evacuation routes and other preventive measures to protect and save human lives and inftrastructure. This approach could serve as a muster for a simple, cost effective and relatively fast geohazards susceptibility evaluation of any area in any developing country. The projects also paid attention to capacity building of our Nicaraguan partners. These projects of the Czech Geological Survey were conducted as the international aid of the Czech Republic to Nicaragua, financed by the Ministry of the Czech Republic

Integration of landslide susceptibility products in the environmental plans

NASA Astrophysics Data System (ADS)

Fiorucci, Federica; Reichenbach, Paola; Rossi, Mauro; Cardinali, Mauro; Guzzetti, Fausto

2015-04-01

Landslides are one of the most destructive natural hazard that causes damages to urban area worldwide. The knowledge of where a landslide could occur is essential for the strategic management of the territory and for a good urban planning . In this contest landslide susceptibility zoning (LSZ) is crucial to provide information on the degree to which an area can be affected by future slope movements. Despite landslide susceptibility maps have been prepared extensively during the last decades, there are few examples of application is in the environmental plans (EP). In this work we present a proposal for the integration of the landslide inventory map with the following landslide susceptibility products: (i) landslide susceptibility zonation , (ii) the associated error map and (iii) the susceptibility uncertainty map. Moreover we proposed to incorporate detailed morphological studies for the evaluation of landslide risk associated to local parceling plan. The integration of all this information is crucial for the management of landslide risk in urban expansions forecasts. Municipality, province and regional administration are often not able to support the costs of landslide risk evaluation for extensive areas but should concentrate their financial resources to specific hazardous and unsafe situations defined by the result of the integration of landslide susceptibility products. Zonation and detail morphological analysis should be performed taking into account the existing laws and regulations, and could become a starting point to discuss new regulations for the landslide risk management.

A landslide susceptibility map of Africa

NASA Astrophysics Data System (ADS)

Broeckx, Jente; Vanmaercke, Matthias; Duchateau, Rica; Poesen, Jean

2017-04-01

Studies on landslide risks and fatalities indicate that landslides are a global threat to humans, infrastructure and the environment, certainly in Africa. Nonetheless our understanding of the spatial patterns of landslides and rockfalls on this continent is very limited. Also in global landslide susceptibility maps, Africa is mostly underrepresented in the inventories used to construct these maps. As a result, predicted landslide susceptibilities remain subject to very large uncertainties. This research aims to produce a first continent-wide landslide susceptibility map for Africa, calibrated with a well-distributed landslide dataset. As a first step, we compiled all available landslide inventories for Africa. This data was supplemented by additional landslide mapping with Google Earth in underrepresented regions. This way, we compiled 60 landslide inventories from the literature (ca. 11000 landslides) and an additional 6500 landslides through mapping in Google Earth (including 1500 rockfalls). Various environmental variables such as slope, lithology, soil characteristics, land use, precipitation and seismic activity, were investigated for their significance in explaining the observed spatial patterns of landslides. To account for potential mapping biases in our dataset, we used Monte Carlo simulations that selected different subsets of mapped landslides, tested the significance of the considered environmental variables and evaluated the performance of the fitted multiple logistic regression model against another subset of mapped landslides. Based on these analyses, we constructed two landslide susceptibility maps for Africa: one for all landslide types and one excluding rockfalls. In both maps, topography, lithology and seismic activity were the most significant variables. The latter factor may be surprising, given the overall limited degree of seismicity in Africa. However, its significance indicates that frequent seismic events may serve as in important preparatory factor for landslides. This finding concurs with several other recent studies. Rainfall explains a significant, but limited part of the observed landslide pattern and becomes insignificant when also rockfalls are considered. This may be explained by the fact that a significant fraction of the mapped rockfalls occurred in the Sahara desert. Overall, both maps perform well in predicting intra-continental patterns of mass movements in Africa and explain about 80% of the observed variance in landslide occurrence. As a result, these maps may be a valuable tool for planning and risk reduction strategies.

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

NASA Astrophysics Data System (ADS)

Yilmaz, Işik; Marschalko, Marian; Bednarik, Martin

2013-04-01

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

Storm-Induced Slope Failure Susceptibility Mapping

DOT National Transportation Integrated Search

2018-01-01

A pilot study was conducted to characterize and map the areas susceptible to slope failure using state-wide available data. The objective was to determine whether it would be possible to provide slope-failure susceptibility mapping that could be used...

Iterative framework radiation hybrid mapping

USDA-ARS?s Scientific Manuscript database

Building comprehensive radiation hybrid maps for large sets of markers is a computationally expensive process, since the basic mapping problem is equivalent to the traveling salesman problem. The mapping problem is also susceptible to noise, and as a result, it is often beneficial to remove markers ...

Quantitative Susceptibility Mapping and R2* Measured Changes during White Matter Lesion Development in Multiple Sclerosis: Myelin Breakdown, Myelin Debris Degradation and Removal, and Iron Accumulation.

PubMed

Zhang, Y; Gauthier, S A; Gupta, A; Chen, W; Comunale, J; Chiang, G C-Y; Zhou, D; Askin, G; Zhu, W; Pitt, D; Wang, Y

2016-09-01

Quantitative susceptibility mapping and R2* are sensitive to myelin and iron changes in multiple sclerosis lesions. This study was designed to characterize lesion changes on quantitative susceptibility mapping and R2* at various gadolinium-enhancement stages. This study included 64 patients with MS with different enhancing patterns in white matter lesions: nodular, shell-like, nonenhancing < 1 year old, and nonenhancing 1-3 years old. These represent acute, late acute, early chronic, and late chronic lesions, respectively. Susceptibility values measured on quantitative susceptibility mapping and R2* values were compared among the 4 lesion types. Their differences were assessed with a generalized estimating equation, controlling for Expanded Disability Status Scale score, age, and disease duration. We analyzed 203 lesions: 80 were nodular-enhancing, of which 77 (96.2%) were isointense on quantitative susceptibility mapping; 33 were shell-enhancing, of which 30 (90.9%) were hyperintense on quantitative susceptibility mapping; and 49 were nonenhancing lesions < 1 year old and 41 were nonenhancing lesions 1-3 years old, all of which were hyperintense on quantitative susceptibility mapping. Their relative susceptibility/R2* values were 0.5 ± 4.4 parts per billion/-5.6 ± 2.9 Hz, 10.2 ± 5.4 parts per billion/-8.0 ± 2.6 Hz, 20.2 ± 7.8 parts per billion/-3.1 ± 2.3 Hz, and 33.2 ± 8.2 parts per billion/-2.0 ± 2.6 Hz, respectively, and were significantly different (P < .005). Early active MS lesions with nodular enhancement show R2* decrease but no quantitative susceptibility mapping change, reflecting myelin breakdown; late active lesions with peripheral enhancement show R2* decrease and quantitative susceptibility mapping increase in the lesion center, reflecting further degradation and removal of myelin debris; and early or late chronic nonenhancing lesions show both quantitative susceptibility mapping and R2* increase, reflecting iron accumulation. © 2016 by American Journal of Neuroradiology.

Object-based Classification for Detecting Landslides and Stochastic Procedure to landslide susceptibility maps - A Case at Baolai Village, SW Taiwan

NASA Astrophysics Data System (ADS)

Lin, Ying-Tong; Chang, Kuo-Chen; Yang, Ci-Jian

2017-04-01

As the result of global warming in the past decades, Taiwan has experienced more and more extreme typhoons with hazardous massive landslides. In this study, we use object-oriented analysis method to classify landslide area at Baolai village by using Formosat-2 satellite images. We used for multiresolution segmented to generate the blocks, and used hierarchical logic to classified 5 different kinds of features. After that, classification the landslide into different type of landslide. Beside, we use stochastic procedure to integrate landslide susceptibility maps. This study assumed that in the extreme event, 2009 Typhoon Morakot, which precipitation goes to 1991.5mm in 5 days, and the highest landslide susceptible area. The results show that study area's landslide area was greatly changes, most of landslide was erosion by gully and made dip slope slide, or erosion by the stream, especially at undercut bank. From the landslide susceptibility maps, we know that the old landslide area have high potential to occur landslides in the extreme event. This study demonstrates the changing of landslide area and the landslide susceptible area. Keywords: Formosat-2, object-oriented, segmentation, classification, landslide, Baolai Village, SW Taiwan, FS

Mass Movement Susceptibility Mapping Using Satellite Optical Imagery Compared With INSAR Monitoring: Zigui County, Three Gorges Region, China

NASA Astrophysics Data System (ADS)

Kincal, Cem; Singleton, Andrew; Liu, Peng; Li, Zhenhong; Drummond, Jane; Hoey, Trevor; Muller, Jan-Peter; Qu, Wei; Zeng, Qiming; Zhang, Jingfa; Du, Peijun

2010-10-01

Mass movements on steep slopes are a major hazard to communities and infrastructure in the Three Gorges region, China. Developing susceptibility maps of mass movements is therefore very important in both current and future land use planning. This study employed satellite optical imagery and an ASTER GDEM (15 m) to derive various parameters (namely geology; slope gradient; proximity to drainage networks and proximity to lineaments) in order to create a GIS-based map of mass movement susceptibility. This map was then evaluated using highly accurate deformation signals processed using the Persistent Scatterer (PS) InSAR technique. Areas of high susceptibility correspond well to points of high subsidence, which provides a strong support of our susceptibility map.

Landslide Inventory and Susceptibility Mapping in Tropical Areas - Southern Mexico

NASA Astrophysics Data System (ADS)

Gaidzik, K.; Ramirez-Herrera, M. T.; Regmi, N. R.; Leshchinsky, B. A.

2016-12-01

Landslides are one of the common catastrophic phenomena in the world. In regions of humid-warm tropical climate they are triggered by extreme storms causing loss of life and economic devastation. In this study we mapped susceptibility to landslides in the tropical mountains of Guerrero (South Mexico) based on the inventory of landslide features triggered by the hurricane Manuel of September 2013. Landslide inventory was produced using interpretation of satellite images and automatic identification of landslides employing the Contour Connection Method (CCM). A map of susceptibility to landslides was developed by computing probability of landslide occurrence from statistical relationships of existing landslides using LiDAR elevation model and derived landslide-causing factors using a logistic regression method. Landslide inventory includes 419 features produced by the hurricane Manuel on the area of 22 km2, and > 1,000 older features, suggesting high landslide activity in this area. Most landslides in the region are small, but some large slides exist, such as the catastrophic landslide in La Pintada that caused 71 fatalities and destroyed a large part of the village. Our results indicate that the distance to streams, human activity, presence or absence of dense vegetation and orientation of slopes (on some areas) strongly influence the spatial distribution of landslides. Results showed high susceptibility zones encompass 30% of the study area and occur mostly along topographic convergence. Applied approach identified most of the landslides within the high susceptibility zone and suggested that it is a valid applicable method to map areas susceptible to landslides in southern Mexico but also on other humid-warm tropical regions.

A comparison between univariate probabilistic and multivariate (logistic regression) methods for landslide susceptibility analysis: the example of the Febbraro valley (Northern Alps, Italy)

NASA Astrophysics Data System (ADS)

Rossi, M.; Apuani, T.; Felletti, F.

2009-04-01

The aim of this paper is to compare the results of two statistical methods for landslide susceptibility analysis: 1) univariate probabilistic method based on landslide susceptibility index, 2) multivariate method (logistic regression). The study area is the Febbraro valley, located in the central Italian Alps, where different types of metamorphic rocks croup out. On the eastern part of the studied basin a quaternary cover represented by colluvial and secondarily, by glacial deposits, is dominant. In this study 110 earth flows, mainly located toward NE portion of the catchment, were analyzed. They involve only the colluvial deposits and their extension mainly ranges from 36 to 3173 m2. Both statistical methods require to establish a spatial database, in which each landslide is described by several parameters that can be assigned using a main scarp central point of landslide. The spatial database is constructed using a Geographical Information System (GIS). Each landslide is described by several parameters corresponding to the value of main scarp central point of the landslide. Based on bibliographic review a total of 15 predisposing factors were utilized. The width of the intervals, in which the maps of the predisposing factors have to be reclassified, has been defined assuming constant intervals to: elevation (100 m), slope (5 °), solar radiation (0.1 MJ/cm2/year), profile curvature (1.2 1/m), tangential curvature (2.2 1/m), drainage density (0.5), lineament density (0.00126). For the other parameters have been used the results of the probability-probability plots analysis and the statistical indexes of landslides site. In particular slope length (0 ÷ 2, 2 ÷ 5, 5 ÷ 10, 10 ÷ 20, 20 ÷ 35, 35 ÷ 260), accumulation flow (0 ÷ 1, 1 ÷ 2, 2 ÷ 5, 5 ÷ 12, 12 ÷ 60, 60 ÷27265), Topographic Wetness Index 0 ÷ 0.74, 0.74 ÷ 1.94, 1.94 ÷ 2.62, 2.62 ÷ 3.48, 3.48 ÷ 6,00, 6.00 ÷ 9.44), Stream Power Index (0 ÷ 0.64, 0.64 ÷ 1.28, 1.28 ÷ 1.81, 1.81 ÷ 4.20, 4.20 ÷ 9.40). Geological map and land use map were also used, considering geological and land use properties as categorical variables. Appling the univariate probabilistic method the Landslide Susceptibility Index (LSI) is defined as the sum of the ratio Ra/Rb calculated for each predisposing factor, where Ra is the ratio between number of pixel of class and the total number of pixel of the study area, and Rb is the ratio between number of landslides respect to the pixel number of the interval area. From the analysis of the Ra/Rb ratio the relationship between landslide occurrence and predisposing factors were defined. Then the equation of LSI was used in GIS to trace the landslide susceptibility maps. The multivariate method for landslide susceptibility analysis, based on logistic regression, was performed starting from the density maps of the predisposing factors, calculated with the intervals defined above using the equation Rb/Rbtot, where Rbtot is a sum of all Rb values. Using stepwise forward algorithms the logistic regression was performed in two successive steps: first a univariate logistic regression is used to choose the most significant predisposing factors, then the multivariate logistic regression can be performed. The univariate regression highlighted the importance of the following factors: elevation, accumulation flow, drainage density, lineament density, geology and land use. When the multivariate regression was applied the number of controlling factors was reduced neglecting the geological properties. The resulting final susceptibility equation is: P = 1 / (1 + exp-(6.46-22.34*elevation-5.33*accumulation flow-7.99* drainage density-4.47*lineament density-17.31*land use)) and using this equation the susceptibility maps were obtained. To easy compare the results of the two methodologies, the susceptibility maps were reclassified in five susceptibility intervals (very high, high, moderate, low and very low) using natural breaks. Then the maps were validated using two cumulative distribution curves, one related to the landslides (number of landslides in each susceptibility class) and one to the basin (number of pixel covering each class). Comparing the curves for each method, it results that the two approaches (univariate and multivariate) are appropriate, providing acceptable results. In both maps the distribution of high susceptibility condition is mainly localized on the left slope of the catchment in agreement with the field evidences. The comparison between the methods was obtained by subtraction of the two maps. This operation shows that about 40% of the basin is classified by the same class of susceptibility. In general the univariate probabilistic method tends to overestimate the areal extension of the high susceptibility class with respect to the maps obtained by the logistic regression method.

A comparative quantitative analysis of magnetic susceptibility artifacts in echo planar and PROPELLER diffusion-weighted images

NASA Astrophysics Data System (ADS)

Cho, Jae-Hwan; Lee, Hae-Kag; Yang, Han-Joon; Lee, Gui-Won; Park, Yong-Soon; Chung, Woon-Kwan

2013-01-01

In this study, the authors investigated whether periodically-rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) diffusion-weighted imaging (DWI) can remove magnetic susceptibility artifacts and compared apparent diffusion coefficient (ADC) values for PROPELLER DWI and the common echo planar (EP) DWI. Twenty patients that underwent brain MRI with a metal dental implant were selected. A 3.0T MR scanner was then used to obtain EP DWI, PROPELLER DWI, and corresponding apparent diffusion coefficient (ADC) maps for a b-value of 0 and 1,000 s/mm2. The frequencies of magnetic susceptibility artifacts in four parts of the brain (bilateral temporal lobes, pons, and orbit) were selected. In the ADC maps, we measured the ADC values of both sides of the temporal lobe and the pons. According to the study results, the frequency of magnetic susceptibility artifacts in PROPELLER DW images was lower than it was in EP DW images. In ADC maps, the ADC values of the bilateral temporal lobes and the pons were all higher in PROPELLER ADC maps than in EP ADC maps. Our findings show that when a high-field MRI machine is used, magnetic susceptibility artifacts can distort anatomical structures and produce high-intensity signals. Furthermore, our findings suggest that in many cases, PROPELLER DWI would be helpful in terms of achieving a correct diagnosis.

An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic

NASA Astrophysics Data System (ADS)

Zhu, A.-Xing; Wang, Rongxun; Qiao, Jianping; Qin, Cheng-Zhi; Chen, Yongbo; Liu, Jing; Du, Fei; Lin, Yang; Zhu, Tongxin

2014-06-01

This paper presents an expert knowledge-based approach to landslide susceptibility mapping in an effort to overcome the deficiencies of data-driven approaches. The proposed approach consists of three generic steps: (1) extraction of knowledge on the relationship between landslide susceptibility and predisposing factors from domain experts, (2) characterization of predisposing factors using GIS techniques, and (3) prediction of landslide susceptibility under fuzzy logic. The approach was tested in two study areas in China - the Kaixian study area (about 250 km2) and the Three Gorges study area (about 4600 km2). The Kaixian study area was used to develop the approach and to evaluate its validity. The Three Gorges study area was used to test both the portability and the applicability of the developed approach for mapping landslide susceptibility over large study areas. Performance was evaluated by examining if the mean of the computed susceptibility values at landslide sites was statistically different from that of the entire study area. A z-score test was used to examine the statistical significance of the difference. The computed z for the Kaixian area was 3.70 and the corresponding p-value was less than 0.001. This suggests that the computed landslide susceptibility values are good indicators of landslide occurrences. In the Three Gorges study area, the computed z was 10.75 and the corresponding p-value was less than 0.001. In addition, we divided the susceptibility value into four levels: low (0.0-0.25), moderate (0.25-0.5), high (0.5-0.75) and very high (0.75-1.0). No landslides were found for areas of low susceptibility. Landslide density was about three times higher in areas of very high susceptibility than that in the moderate susceptibility areas, and more than twice as high as that in the high susceptibility areas. The results from the Three Gorge study area suggest that the extracted expert knowledge can be extrapolated to another study area and the developed approach can be used in large-scale projects. Results from these case studies suggest that the expert knowledge-based approach is effective in mapping landslide susceptibility and that its performance is maintained when it is moved to a new area from the model development area without changes to the knowledge base.

A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: the Baganza valley case study (Italian Northern Apennines)

NASA Astrophysics Data System (ADS)

Clerici, Aldo; Perego, Susanna; Tellini, Claudio; Vescovi, Paolo

2006-08-01

Among the many GIS based multivariate statistical methods for landslide susceptibility zonation, the so called “Conditional Analysis method” holds a special place for its conceptual simplicity. In fact, in this method landslide susceptibility is simply expressed as landslide density in correspondence with different combinations of instability-factor classes. To overcome the operational complexity connected to the long, tedious and error prone sequence of commands required by the procedure, a shell script mainly based on the GRASS GIS was created. The script, starting from a landslide inventory map and a number of factor maps, automatically carries out the whole procedure resulting in the construction of a map with five landslide susceptibility classes. A validation procedure allows to assess the reliability of the resulting model, while the simple mean deviation of the density values in the factor class combinations, helps to evaluate the goodness of landslide density distribution. The procedure was applied to a relatively small basin (167 km2) in the Italian Northern Apennines considering three landslide types, namely rotational slides, flows and complex landslides, for a total of 1,137 landslides, and five factors, namely lithology, slope angle and aspect, elevation and slope/bedding relations. The analysis of the resulting 31 different models obtained combining the five factors, confirms the role of lithology, slope angle and slope/bedding relations in influencing slope stability.

Non-Susceptible Landslide Areas in Italy and in the Mediterranean Region

NASA Astrophysics Data System (ADS)

Alvioli, Massimiliano; Ardizzone, Francesca; Guzzetti, Fausto; Marchesini, Ivan; Rossi, Mauro

2014-05-01

Landslide susceptibility is the likelihood of a landslide occurring in a given area. Over the past three decades, researchers, and planning and environmental organisations have worked to assess landslide susceptibility at different geographical scales, and to produce maps portraying landslide susceptibility zonation. Little effort was made to determine where landslides are not expected, where susceptibility is null, or negligible. This is surprising because planners and decision makers are also interesting in knowing where landslides are not foreseen, or cannot occur in an area. We propose a method for the definition of non-susceptible landslide areas, at the synoptic scale. We applied the method in Italy and to the territory surrounding the Mediterranean Sea and we produced two synoptic-scale maps showing areas where landslides are not expected in Italy and in the Mediterranean area. To construct the method we used digital terrain elevation and landslide information. The digital terrain consisted in the 3-arc-second SRTM DEM, the landslide information was obtained for 13 areas in Italy where landslide inventory maps were available to us. We tested three different models to determine the non-susceptible landslide areas, including a linear model (LR), a quantile linear model (QLR), and a quantile non-linear model (QNL). Model performances have been evaluated using independent landslide information represented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia - IFFI). Best results were obtained using the QNL model. The corresponding zonation of non- susceptible landslide areas was intersected in a GIS with geographical census data for Italy. The results show that the 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility was expected to be null or negligible, while the remaining 42.5% in areas where some landslide susceptibility was significant or not negligible. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non- susceptibility zonation using independent landslide information for three study areas in Spain. Results proved that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the Mediterranean area. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible and susceptible landslide areas, at the synoptic scale.

Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms.

PubMed

Razavi Termeh, Seyed Vahid; Kornejady, Aiding; Pourghasemi, Hamid Reza; Keesstra, Saskia

2018-02-15

Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the premier model in the study area. Furthermore, LVQ results revealed that slope degree, rainfall, and altitude were the most effective factors. As regards the premier model, a total area of 44.74% was recognized as highly susceptible to flooding. The results of this study can be used as a platform for better land use planning in order to manage the highly susceptible zones to flooding and reduce the anticipated losses. Copyright © 2017 Elsevier B.V. All rights reserved.

Landslides in Flanders (Belgium): Where science meets public policy

NASA Astrophysics Data System (ADS)

van den Eeckhaut, M.; Poesen, J.; Vandekerckhove, L.

2009-04-01

Although scientific research on landslides in the Flemish Ardennes (710 km²; Belgium), has been conducted over the last decades, the Flemish Government only took account of slope failure as a soil degradation process after the occurrence of several damaging landslides in the beginning of the 21st century. Here we aim to present the successful collaboration between the Physical and Regional Geography Research Group (FRG; Dept. Earth and Environmental Sciences K.U.Leuven) and the Environment, Nature and Energy Department (LNE; Flemish Government) in landslide management. We will demonstrate how geomorphologists produced practical tools for landslide management which can be directly applied by LNE as well as other local and regional authorities and planners. Since 2004 three projects on landslide inventory mapping and susceptibility assessment in the Flemish Ardennes have been funded by LNE, and a fourth one on landslide susceptibility assessment in remaining hilly regions in Flanders west of Brussels recently started. Together with a steering committee composed of stakeholders, persons from LNE supervise the research carried out by geomorphologists experienced in landslide studies. For the establishment of the landslide inventory map of the Flemish Ardennes we combined the analysis of LIDAR-derived hillshade and contour line maps with detailed field controls. Additional information was collected through interviews with local authorities and inhabitants and from analysis of newspaper articles and technical reports. Then, a statistical model, logistic regression, was applied to produce a high quality classified landslide susceptibility map. The unique part of this collaboration is that all end products are online available at user-friendly websites designed by LNE. The scientific report containing (1) general information on landslides, (2) a description of the study area, (3) an explanation of the materials and methods used, (4) a presentation of the resulting landslide inventory map and landslide susceptibility map and (5) practical information on the application of both maps for landslide risk reduction through prevention and remediation is available at (http://www.lne.be/themas/bodem/grondverschuiving/grondverschuiving). Equally important, however, are the digital landslide inventory map and landslide susceptibility map which can be consulted at ‘The geographical database of Flanders' (http://dov.vlaanderen.be). This database enables persons to easily combine the landslide inventory and landslide susceptibility maps with topographical and lithological maps allowing them to check the susceptibility to landslides throughout the Flemish Ardennes. For each landslide on the landslide inventory map, there is a corresponding file containing specific information on this landslide. After a simple mouse click on a mapped landslide, the file belonging to this landslide pops up. Finally, guidelines for assessing the impact of planned interventions (e.g. construction of buildings, roads, …) on landsliding can be consulted at (http://www.mervlaanderen.be/uploads/b332.pdf). Thus, we present here an example of how ‘Science meets policy'. The created susceptibility map is an important tool for improving land use planning, and in particular for zoning the susceptibility classes with very high, high and moderate landslide susceptibility where prevention measures are needed and human interference should be limited. The on-line availability of all project documentation opens perspectives for managing landslide-affected areas through both top-down and bottom-up initiatives.

Landslide susceptibility mapping using a neuro-fuzzy

NASA Astrophysics Data System (ADS)

Lee, S.; Choi, J.; Oh, H.

2009-12-01

This paper develops and applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. A neuro-fuzzy system is based on a fuzzy system that is trained by a learning algorithm derived from the neural network theory. The learning procedure operates on local information, and causes only local modifications in the underlying fuzzy system. The study area, Boun, suffered much damage following heavy rain in 1998 and was selected as a suitable site for the evaluation of the frequency and distribution of landslides. Boun is located in the central part of Korea. Landslide-related factors such as slope, soil texture, wood type, lithology, and density of lineament were extracted from topographic, soil, forest, and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs) and analysis results were verified using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping. Various MFs were used in this study, and after verification, the difference in accuracy according to the MFs was small, between 84.81% and 84.96%. The difference was just 0.15% and therefore the choice of MFs was not important in the study. Also, compared with the likelihood ratio model, which showed 84.94%, the accuracy was similar. Thus, the ANFIS could be applied to other study areas with different data and other study methods such as cross-validation. The developed ANFIS learns the if-then rules between landslide-related factors and landslide location for generalization and prediction. It is easy to understand and interpret, therefore it is a good choice for modeling landslide susceptibility mapping, which are also of great help for planners and engineers in selecting highly susceptible areas for further detail surveys and suitable locations to implement development. Although they may be less useful at the site-specific scale, where local geological and geographic heterogeneities may prevail, the results herein may be used as basic data to assist slope management and land use planning. For the method to be more generally applied, more landslide data are needed and more case studies should be conducted.

Imaging Cerebral Microhemorrhages in Military Service Members with Chronic Traumatic Brain Injury

PubMed Central

Liu, Wei; Soderlund, Karl; Senseney, Justin S.; Joy, David; Yeh, Ping-Hong; Ollinger, John; Sham, Elyssa B.; Liu, Tian; Wang, Yi; Oakes, Terrence R.; Riedy, Gerard

2017-01-01

Purpose To detect cerebral microhemorrhages in military service members with chronic traumatic brain injury by using susceptibility-weighted magnetic resonance (MR) imaging. The longitudinal evolution of microhemorrhages was monitored in a subset of patients by using quantitative susceptibility mapping. Materials and Methods The study was approved by the Walter Reed National Military Medical Center institutional review board and is compliant with HIPAA guidelines. All participants underwent two-dimensional conventional gradient-recalled-echo MR imaging and three-dimensional flow-compensated multi-echo gradient-recalled-echo MR imaging (processed to generate susceptibility-weighted images and quantitative susceptibility maps), and a subset of patients underwent follow-up imaging. Microhemorrhages were identified by two radiologists independently. Comparisons of microhemorrhage number, size, and magnetic susceptibility derived from quantitative susceptibility maps between baseline and follow-up imaging examinations were performed by using the paired t test. Results Among the 603 patients, cerebral microhemorrhages were identified in 43 patients, with six excluded for further analysis owing to artifacts. Seventy-seven percent (451 of 585) of the microhemorrhages on susceptibility-weighted images had a more conspicuous appearance than on gradient-recalled-echo images. Thirteen of the 37 patients underwent follow-up imaging examinations. In these patients, a smaller number of microhemorrhages were identified at follow-up imaging compared with baseline on quantitative susceptibility maps (mean ± standard deviation, 9.8 microhemorrhages ± 12.8 vs 13.7 microhemorrhages ± 16.6; P = .019). Quantitative susceptibility mapping–derived quantitative measures of microhemorrhages also decreased over time: −0.85 mm3 per day ± 1.59 for total volume (P = .039) and −0.10 parts per billion per day ± 0.14 for mean magnetic susceptibility (P = .016). Conclusion The number of microhemorrhages and quantitative susceptibility mapping–derived quantitative measures of microhemorrhages all decreased over time, suggesting that hemosiderin products undergo continued, subtle evolution in the chronic stage. PMID:26371749

The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy).

PubMed

Reichenbach, P; Busca, C; Mondini, A C; Rossi, M

2014-12-01

The spatial distribution of landslides is influenced by different climatic conditions and environmental settings including topography, morphology, hydrology, lithology, and land use. In this work, we have attempted to evaluate the influence of land use change on landslide susceptibility (LS) for a small study area located in the southern part of the Briga catchment, along the Ionian coast of Sicily (Italy). On October 1, 2009, the area was hit by an intense rainfall event that triggered abundant slope failures and resulted in widespread erosion. After the storm, an inventory map showing the distribution of pre-event and event landslides was prepared for the area. Moreover, two different land use maps were developed: the first was obtained through a semi-automatic classification of digitized aerial photographs acquired in 1954, the second through the combination of supervised classifications of two recent QuickBird images. Exploiting the two land use maps and different land use scenarios, LS zonations were prepared through multivariate statistical analyses. Differences in the susceptibility models were analyzed and quantified to evaluate the effects of land use change on the susceptibility zonation. Susceptibility maps show an increase in the areal percentage and number of slope units classified as unstable related to the increase in bare soils to the detriment of forested areas.

Hematocrit Measurement with R2* and Quantitative Susceptibility Mapping in Postmortem Brain.

PubMed

Walsh, A J; Sun, H; Emery, D J; Wilman, A H

2018-05-24

Noninvasive venous oxygenation quantification with MR imaging will improve the neurophysiologic investigation and the understanding of the pathophysiology in neurologic diseases. Available MR imaging methods are limited by sensitivity to flow and often require assumptions of the hematocrit level. In situ postmortem imaging enables evaluation of methods in a fully deoxygenated environment without flow artifacts, allowing direct calculation of hematocrit. This study compares 2 venous oxygenation quantification methods in in situ postmortem subjects. Transverse relaxation (R2*) mapping and quantitative susceptibility mapping were performed on a whole-body 4.7T MR imaging system. Intravenous measurements in major draining intracranial veins were compared between the 2 methods in 3 postmortem subjects. The quantitative susceptibility mapping technique was also applied in 10 healthy control subjects and compared with reference venous oxygenation values. In 2 early postmortem subjects, R2* mapping and quantitative susceptibility mapping measurements within intracranial veins had a significant and strong correlation ( R 2 = 0.805, P = .004 and R 2 = 0.836, P = .02). Higher R2* and susceptibility values were consistently demonstrated within gravitationally dependent venous segments during the early postmortem period. Hematocrit ranged from 0.102 to 0.580 in postmortem subjects, with R2* and susceptibility as large as 291 seconds -1 and 1.75 ppm, respectively. Measurements of R2* and quantitative susceptibility mapping within large intracranial draining veins have a high correlation in early postmortem subjects. This study supports the use of quantitative susceptibility mapping for evaluation of in vivo venous oxygenation and postmortem hematocrit concentrations. © 2018 by American Journal of Neuroradiology.

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan.

PubMed

Dou, Jie; Tien Bui, Dieu; Yunus, Ali P; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

2015-01-01

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan

PubMed Central

Dou, Jie; Tien Bui, Dieu; P. Yunus, Ali; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

2015-01-01

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner. PMID:26214691

An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping

PubMed Central

Feizizadeh, Bakhtiar; Blaschke, Thomas

2014-01-01

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results. PMID:27019609

Suitability aero-geophysical methods for generating conceptual soil maps and their use in the modeling of process-related susceptibility maps

NASA Astrophysics Data System (ADS)

Tilch, Nils; Römer, Alexander; Jochum, Birgit; Schattauer, Ingrid

2014-05-01

In the past years, several times large-scale disasters occurred in Austria, which were characterized not only by flooding, but also by numerous shallow landslides and debris flows. Therefore, for the purpose of risk prevention, national and regional authorities also require more objective and realistic maps with information about spatially variable susceptibility of the geosphere for hazard-relevant gravitational mass movements. There are many and various proven methods and models (e.g. neural networks, logistic regression, heuristic methods) available to create such process-related (e.g. flat gravitational mass movements in soil) suszeptibility maps. But numerous national and international studies show a dependence of the suitability of a method on the quality of process data and parameter maps (f.e. Tilch & Schwarz 2011, Schwarz & Tilch 2011). In this case, it is important that also maps with detailed and process-oriented information on the process-relevant geosphere will be considered. One major disadvantage is that only occasionally area-wide process-relevant information exists. Similarly, in Austria often only soil maps for treeless areas are available. However, in almost all previous studies, randomly existing geological and geotechnical maps were used, which often have been specially adapted to the issues and objectives. This is one reason why very often conceptual soil maps must be derived from geological maps with only hard rock information, which often have a rather low quality. Based on these maps, for example, adjacent areas of different geological composition and process-relevant physical properties are razor sharp delineated, which in nature appears quite rarly. In order to obtain more realistic information about the spatial variability of the process-relevant geosphere (soil cover) and its physical properties, aerogeophysical measurements (electromagnetic, radiometric), carried out by helicopter, from different regions of Austria were interpreted. Previous studies show that, especially with radiometric measurements, the two-dimensional spatial variability of the nature of the process-relevant soil, close to the surface can be determined. In addition, the electromagnetic measurements are more important to obtain three-dimensional information of the deeper geological conditions and to improve the area-specific geological knowledge and understanding. The validation of these measurements is done with terrestrial geoelectrical measurements. So both aspects, radiometric and electromagnetic measurements, are important and subsequently, interpretation of the geophysical results can be used as the parameter maps in the modeling of more realistic susceptibility maps with respect to various processes. Within this presentation, results of geophysical measurements, the outcome and the derived parameter maps, as well as first process-oriented susceptibility maps in terms of gravitational soil mass movements will be presented. As an example results which were obtained with a heuristic method in an area in Vorarlberg (Western Austria) will be shown. References: Schwarz, L. & Tilch, N. (2011): Why are good process data so important for the modelling of landslide susceptibility maps?- EGU-Postersession "Landslide hazard and risk assessment, and landslide management" (NH 3.6), Vienna. [http://www.geologie.ac.at/fileadmin/user_upload/dokumente/pdf/poster/poster_2011_egu_schwarz_tilch_1.pdf] Tilch, N. & Schwarz, L. (2011): Spatial and scale-dependent variability in data quality and their influence on susceptibility maps for gravitational mass movements in soil, modelled by heuristic method.- EGU-Postersession "Landslide hazard and risk assessment, and landslide management" (NH 3.6); Vienna. [http://www.geologie.ac.at/fileadmin/user_upload/dokumente/pdf/poster/poster_2011_egu_tilch_schwarz.pdf

Comparison of the landslide susceptibility models in Taipei Water Source Domain, Taiwan

NASA Astrophysics Data System (ADS)

WU, C. Y.; Yeh, Y. C.; Chou, T. H.

2017-12-01

Taipei Water Source Domain, locating at the southeast of Taipei Metropolis, is the main source of water resource in this region. Recently, the downstream turbidity often soared significantly during the typhoon period because of the upstream landslides. The landslide susceptibilities should be analysed to assess the influence zones caused by different rainfall events, and to ensure the abilities of this domain to serve enough and quality water resource. Generally, the landslide susceptibility models can be established based on either a long-term landslide inventory or a specified landslide event. Sometimes, there is no long-term landslide inventory in some areas. Thus, the event-based landslide susceptibility models are established widely. However, the inventory-based and event-based landslide susceptibility models may result in dissimilar susceptibility maps in the same area. So the purposes of this study were to compare the landslide susceptibility maps derived from the inventory-based and event-based models, and to interpret how to select a representative event to be included in the susceptibility model. The landslide inventory from Typhoon Tim in July, 1994 and Typhoon Soudelor in August, 2015 was collected, and used to establish the inventory-based landslide susceptibility model. The landslides caused by Typhoon Nari and rainfall data were used to establish the event-based model. The results indicated the high susceptibility slope-units were located at middle upstream Nan-Shih Stream basin.

Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models

NASA Astrophysics Data System (ADS)

Hong, Haoyuan; Pourghasemi, Hamid Reza; Pourtaghi, Zohre Sadat

2016-04-01

Landslides are an important natural hazard that causes a great amount of damage around the world every year, especially during the rainy season. The Lianhua area is located in the middle of China's southern mountainous area, west of Jiangxi Province, and is known to be an area prone to landslides. The aim of this study was to evaluate and compare landslide susceptibility maps produced using the random forest (RF) data mining technique with those produced by bivariate (evidential belief function and frequency ratio) and multivariate (logistic regression) statistical models for Lianhua County, China. First, a landslide inventory map was prepared using aerial photograph interpretation, satellite images, and extensive field surveys. In total, 163 landslide events were recognized in the study area, with 114 landslides (70%) used for training and 49 landslides (30%) used for validation. Next, the landslide conditioning factors-including the slope angle, altitude, slope aspect, topographic wetness index (TWI), slope-length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, annual precipitation, land use, normalized difference vegetation index (NDVI), and lithology-were derived from the spatial database. Finally, the landslide susceptibility maps of Lianhua County were generated in ArcGIS 10.1 based on the random forest (RF), evidential belief function (EBF), frequency ratio (FR), and logistic regression (LR) approaches and were validated using a receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that for landslide susceptibility maps produced using the EBF, FR, LR, and RF models, the area under the curve (AUC) values were 0.8122, 0.8134, 0.7751, and 0.7172, respectively. Therefore, we can conclude that all four models have an AUC of more than 0.70 and can be used in landslide susceptibility mapping in the study area; meanwhile, the EBF and FR models had the best performance for Lianhua County, China. Thus, the resultant susceptibility maps will be useful for land use planning and hazard mitigation aims.

Fast Quantitative Susceptibility Mapping with L1-Regularization and Automatic Parameter Selection

PubMed Central

Bilgic, Berkin; Fan, Audrey P.; Polimeni, Jonathan R.; Cauley, Stephen F.; Bianciardi, Marta; Adalsteinsson, Elfar; Wald, Lawrence L.; Setsompop, Kawin

2014-01-01

Purpose To enable fast reconstruction of quantitative susceptibility maps with Total Variation penalty and automatic regularization parameter selection. Methods ℓ1-regularized susceptibility mapping is accelerated by variable-splitting, which allows closed-form evaluation of each iteration of the algorithm by soft thresholding and FFTs. This fast algorithm also renders automatic regularization parameter estimation practical. A weighting mask derived from the magnitude signal can be incorporated to allow edge-aware regularization. Results Compared to the nonlinear Conjugate Gradient (CG) solver, the proposed method offers 20× speed-up in reconstruction time. A complete pipeline including Laplacian phase unwrapping, background phase removal with SHARP filtering and ℓ1-regularized dipole inversion at 0.6 mm isotropic resolution is completed in 1.2 minutes using Matlab on a standard workstation compared to 22 minutes using the Conjugate Gradient solver. This fast reconstruction allows estimation of regularization parameters with the L-curve method in 13 minutes, which would have taken 4 hours with the CG algorithm. Proposed method also permits magnitude-weighted regularization, which prevents smoothing across edges identified on the magnitude signal. This more complicated optimization problem is solved 5× faster than the nonlinear CG approach. Utility of the proposed method is also demonstrated in functional BOLD susceptibility mapping, where processing of the massive time-series dataset would otherwise be prohibitive with the CG solver. Conclusion Online reconstruction of regularized susceptibility maps may become feasible with the proposed dipole inversion. PMID:24259479

Background field removal technique based on non-regularized variable kernels sophisticated harmonic artifact reduction for phase data for quantitative susceptibility mapping.

PubMed

Kan, Hirohito; Arai, Nobuyuki; Takizawa, Masahiro; Omori, Kazuyoshi; Kasai, Harumasa; Kunitomo, Hiroshi; Hirose, Yasujiro; Shibamoto, Yuta

2018-06-11

We developed a non-regularized, variable kernel, sophisticated harmonic artifact reduction for phase data (NR-VSHARP) method to accurately estimate local tissue fields without regularization for quantitative susceptibility mapping (QSM). We then used a digital brain phantom to evaluate the accuracy of the NR-VSHARP method, and compared it with the VSHARP and iterative spherical mean value (iSMV) methods through in vivo human brain experiments. Our proposed NR-VSHARP method, which uses variable spherical mean value (SMV) kernels, minimizes L2 norms only within the volume of interest to reduce phase errors and save cortical information without regularization. In a numerical phantom study, relative local field and susceptibility map errors were determined using NR-VSHARP, VSHARP, and iSMV. Additionally, various background field elimination methods were used to image the human brain. In a numerical phantom study, the use of NR-VSHARP considerably reduced the relative local field and susceptibility map errors throughout a digital whole brain phantom, compared with VSHARP and iSMV. In the in vivo experiment, the NR-VSHARP-estimated local field could sufficiently achieve minimal boundary losses and phase error suppression throughout the brain. Moreover, the susceptibility map generated using NR-VSHARP minimized the occurrence of streaking artifacts caused by insufficient background field removal. Our proposed NR-VSHARP method yields minimal boundary losses and highly precise phase data. Our results suggest that this technique may facilitate high-quality QSM. Copyright © 2017. Published by Elsevier Inc.

Use of Satellite Remote Sensing Data in the Mapping of Global Landslide Susceptibility

NASA Technical Reports Server (NTRS)

Hong, Yang; Adler, Robert F.; Huffman, George J.

2007-01-01

Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIs-based weighted linear combination method. First , six relevant landslide-controlling factors are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor's relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally, the continuous index map is further classified into six susceptibility categories. Results show the hot spots of landslide-prone regions include the Pacific Rim, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, Alps, and parts of the Middle East and Africa. India, China, Nepal, Japan, the USA, and Peru are shown to have landslide-prone areas. This first-cut global landslide susceptibility map forms a starting point to provide a global view of landslide risks and may be used in conjunction with satellite-based precipitation information to potentially detect areas with significant landslide potential due to heavy rainfall. 1

Application of Physically based landslide susceptibility models in Brazil

NASA Astrophysics Data System (ADS)

Carvalho Vieira, Bianca; Martins, Tiago D.

2017-04-01

Shallow landslides and floods are the processes responsible for most material and environmental damages in Brazil. In the last decades, some landslides events induce a high number of deaths (e.g. Over 1000 deaths in one event) and incalculable social and economic losses. Therefore, the prediction of those processes is considered an important tool for land use planning tools. Among different methods the physically based landslide susceptibility models having been widely used in many countries, but in Brazil it is still incipient when compared to other ones, like statistical tools and frequency analyses. Thus, the main objective of this research was to assess the application of some Physically based landslide susceptibility models in Brazil, identifying their main results, the efficiency of susceptibility mapping, parameters used and limitations of the tropical humid environment. In order to achieve that, it was evaluated SHALSTAB, SINMAP and TRIGRS models in some studies in Brazil along with the Geotechnical values, scales, DEM grid resolution and the results based on the analysis of the agreement between predicted susceptibility and the landslide scar's map. Most of the studies in Brazil applied SHALSTAB, SINMAP and to a lesser extent the TRIGRS model. The majority researches are concentrated in the Serra do Mar mountain range, that is a system of escarpments and rugged mountains that extends more than 1,500 km along the southern and southeastern Brazilian coast, and regularly affected by heavy rainfall that generates widespread mass movements. Most part of these studies used conventional topographic maps with scales ranging from 1:2000 to 1:50000 and DEM-grid resolution between 2 and 20m. Regarding the Geotechnical and hydrological values, a few studies use field collected data which could produce more efficient results, as indicated by international literature. Therefore, even though they have enormous potential in the susceptibility mapping, even for comparison purposes between different areas, the studies in Brazil require more detailed consideration on the input of topographic and Geotechnical parameters.

GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China

NASA Astrophysics Data System (ADS)

Xu, Chong; Dai, Fuchu; Xu, Xiwei; Lee, Yuan Hsi

2012-04-01

Support vector machine (SVM) modeling is based on statistical learning theory. It involves a training phase with associated input and target output values. In recent years, the method has become increasingly popular. The main purpose of this study is to evaluate the mapping power of SVM modeling in earthquake triggered landslide-susceptibility mapping for a section of the Jianjiang River watershed using a Geographic Information System (GIS) software. The river was affected by the Wenchuan earthquake of May 12, 2008. Visual interpretation of colored aerial photographs of 1-m resolution and extensive field surveys provided a detailed landslide inventory map containing 3147 landslides related to the 2008 Wenchuan earthquake. Elevation, slope angle, slope aspect, distance from seismogenic faults, distance from drainages, and lithology were used as the controlling parameters. For modeling, three groups of positive and negative training samples were used in concert with four different kernel functions. Positive training samples include the centroids of 500 large landslides, those of all 3147 landslides, and 5000 randomly selected points in landslide polygons. Negative training samples include 500, 3147, and 5000 randomly selected points on slopes that remained stable during the Wenchuan earthquake. The four kernel functions are linear, polynomial, radial basis, and sigmoid. In total, 12 cases of landslide susceptibility were mapped. Comparative analyses of landslide-susceptibility probability and area relation curves show that both the polynomial and radial basis functions suitably classified the input data as either landslide positive or negative though the radial basis function was more successful. The 12 generated landslide-susceptibility maps were compared with known landslide centroid locations and landslide polygons to verify the success rate and predictive accuracy of each model. The 12 results were further validated using area-under-curve analysis. Group 3 with 5000 randomly selected points on the landslide polygons, and 5000 randomly selected points along stable slopes gave the best results with a success rate of 79.20% and predictive accuracy of 79.13% under the radial basis function. Of all the results, the sigmoid kernel function was the least skillful when used in concert with the centroid data of all 3147 landslides as positive training samples, and the negative training samples of 3147 randomly selected points in regions of stable slope (success rate = 54.95%; predictive accuracy = 61.85%). This paper also provides suggestions and reference data for selecting appropriate training samples and kernel function types for earthquake triggered landslide-susceptibility mapping using SVM modeling. Predictive landslide-susceptibility maps could be useful in hazard mitigation by helping planners understand the probability of landslides in different regions.

Methodology for Elaborating Regional Susceptibility Maps of Slope Instability: the State of Guerrero (mexico) Case Study

NASA Astrophysics Data System (ADS)

González Huesca, A. E.; Ferrés, D.; Domínguez-M, L.

2013-05-01

Numerous cases of different types of slope instability occur every year in the mountain areas of México. Sometimes these instabilities severely affect the exposed communities, roads and infrastructure, causing deaths and serious material damage, mainly in the states of Puebla, Veracruz, Oaxaca, Guerrero and Chiapas, at the central and south sectors of the country. The occurrence of the slope instability is the result of the combination of climatic, geologic, hydrologic, geomorphologic and anthropogenic factors. The National Center for Disaster Prevention (CENAPRED) is developing several projects in order to offer civil protection authorities of the Mexican states some methodologies to address the hazard assessment for different natural phenomena in a regional level. In this framework, during the past two years, a methodology was prepared to construct susceptibility maps for slope instability at regional (≤ 1:100 000) and national (≤ 1:1 000 000) levels. This research was addressed in accordance to the criteria established by the International Association of Engineering Geology, which is the highest international authority in this topic. The state of Guerrero has been taken as a pilot scheme to elaborate the susceptibility map for slope instability at a regional level. The major constraints considered in the methodology to calculate susceptibility are: a) the slope of the surface, b) the geology and c) the land use, which were integrated using a Geographic Information System (GIS). The arithmetic sum and weighting factors to obtain the final susceptibility map were based on the average values calculated in the individual study of several cases of slope instability occurred in the state in the past decade. For each case, the evaluation format proposed by CENAPRED in 2006 in the "Guía Básica para la elaboración de Atlas Estatales y Municipales de Peligros y Riesgos" to evaluate instabilities in a local level, was applied. The resulting susceptibility map shows that the central and east-central sectors of the state of Guerrero are those with higher values of susceptibility to slope instability. Future work will elaborate the hazard maps of slope instability for the state of Guerrero using and combining the information of susceptibility obtained with the data of the trigger factors, such as precipitation and seismicity, for different periods of recurrence. The final goal is that this methodology can be applied to other states of the country, in order to nourish and enhance their Atlas of hazards and risk.

Simultaneous Quantitative MRI Mapping of T1, T2* and Magnetic Susceptibility with Multi-Echo MP2RAGE

PubMed Central

Kober, Tobias; Möller, Harald E.; Schäfer, Andreas

2017-01-01

The knowledge of relaxation times is essential for understanding the biophysical mechanisms underlying contrast in magnetic resonance imaging. Quantitative experiments, while offering major advantages in terms of reproducibility, may benefit from simultaneous acquisitions. In this work, we demonstrate the possibility of simultaneously recording relaxation-time and susceptibility maps with a prototype Multi-Echo (ME) Magnetization-Prepared 2 RApid Gradient Echoes (MP2RAGE) sequence. T1 maps can be obtained using the MP2RAGE sequence, which is relatively insensitive to inhomogeneities of the radio-frequency transmit field, B1+. As an extension, multiple gradient echoes can be acquired in each of the MP2RAGE readout blocks, which permits the calculation of T2* and susceptibility maps. We used computer simulations to explore the effects of the parameters on the precision and accuracy of the mapping. In vivo parameter maps up to 0.6 mm nominal resolution were acquired at 7 T in 19 healthy volunteers. Voxel-by-voxel correlations and the test-retest reproducibility were used to assess the reliability of the results. When using optimized paramenters, T1 maps obtained with ME-MP2RAGE and standard MP2RAGE showed excellent agreement for the whole range of values found in brain tissues. Simultaneously obtained T2* and susceptibility maps were of comparable quality as Fast Low-Angle SHot (FLASH) results. The acquisition times were more favorable for the ME-MP2RAGE (≈ 19 min) sequence as opposed to the sum of MP2RAGE (≈ 12 min) and FLASH (≈ 10 min) acquisitions. Without relevant sacrifice in accuracy, precision or flexibility, the multi-echo version may yield advantages in terms of reduced acquisition time and intrinsic co-registration, provided that an appropriate optimization of the acquisition parameters is performed. PMID:28081157

GhMAP3K65, a Cotton Raf-Like MAP3K Gene, Enhances Susceptibility to Pathogen Infection and Heat Stress by Negatively Modulating Growth and Development in Transgenic Nicotiana benthamiana.

PubMed

Zhai, Na; Jia, Haihong; Liu, Dongdong; Liu, Shuchang; Ma, Manli; Guo, Xingqi; Li, Han

2017-11-21

Mitogen-activated protein kinase kinase kinases (MAP3Ks), the top components of MAPK cascades, modulate many biological processes, such as growth, development and various environmental stresses. Nevertheless, the roles of MAP3Ks remain poorly understood in cotton. In this study, GhMAP3K65 was identified in cotton, and its transcription was inducible by pathogen infection, heat stress, and multiple signalling molecules. Silencing of GhMAP3K65 enhanced resistance to pathogen infection and heat stress in cotton. In contrast, overexpression of GhMAP3K65 enhanced susceptibility to pathogen infection and heat stress in transgenic Nicotiana benthamiana . The expression of defence-associated genes was activated in transgenic N. benthamiana plants after pathogen infection and heat stress, indicating that GhMAP3K65 positively regulates plant defence responses. Nevertheless, transgenic N. benthamiana plants impaired lignin biosynthesis and stomatal immunity in their leaves and repressed vitality of their root systems. In addition, the expression of lignin biosynthesis genes and lignin content were inhibited after pathogen infection and heat stress. Collectively, these results demonstrate that GhMAP3K65 enhances susceptibility to pathogen infection and heat stress by negatively modulating growth and development in transgenic N. benthamiana plants.

Land subsidence susceptibility and hazard mapping: the case of Amyntaio Basin, Greece

NASA Astrophysics Data System (ADS)

Tzampoglou, P.; Loupasakis, C.

2017-09-01

Landslide susceptibility and hazard mapping has been applying for more than 20 years succeeding the assessment of the landslide risk and the mitigation the phenomena. On the contrary, equivalent maps aiming to study and mitigate land subsidence phenomena caused by the overexploitation of the aquifers are absent from the international literature. The current study focuses at the Amyntaio basin, located in West Macedonia at Florina prefecture. As proved by numerous studies the wider area has been severely affected by the overexploitation of the aquifers, caused by the mining and the agricultural activities. The intensive ground water level drop has triggered extensive land subsidence phenomena, especially at the perimeter of the open pit coal mine operating at the site, causing damages to settlements and infrastructure. The land subsidence susceptibility and risk maps were produced by applying the semi-quantitative WLC (Weighted Linear Combination) method, especially calibrated for this particular catastrophic event. The results were evaluated by using detailed field mapping data referring to the spatial distribution of the surface ruptures caused by the subsidence. The high correlation between the produced maps and the field mapping data, have proved the great value of the maps and of the applied technique on the management and the mitigation of the phenomena. Obviously, these maps can be safely used by decision-making authorities for the future urban safety development.

A Tool for Modelling the Probability of Landslides Impacting Road Networks

NASA Astrophysics Data System (ADS)

Taylor, Faith E.; Santangelo, Michele; Marchesini, Ivan; Malamud, Bruce D.; Guzzetti, Fausto

2014-05-01

Triggers such as earthquakes or heavy rainfall can result in hundreds to thousands of landslides occurring across a region within a short space of time. These landslides can in turn result in blockages across the road network, impacting how people move about a region. Here, we show the development and application of a semi-stochastic model to simulate how landslides intersect with road networks during a triggered landslide event. This was performed by creating 'synthetic' triggered landslide inventory maps and overlaying these with a road network map to identify where road blockages occur. Our landslide-road model has been applied to two regions: (i) the Collazzone basin (79 km2) in Central Italy where 422 landslides were triggered by rapid snowmelt in January 1997, (ii) the Oat Mountain quadrangle (155 km2) in California, USA, where 1,350 landslides were triggered by the Northridge Earthquake (M = 6.7) in January 1994. For both regions, detailed landslide inventory maps for the triggered events were available, in addition to maps of landslide susceptibility and road networks of primary, secondary and tertiary roads. To create 'synthetic' landslide inventory maps, landslide areas (AL) were randomly selected from a three-parameter inverse gamma probability density function, consisting of a power law decay of about -2.4 for medium and large values of AL and an exponential rollover for small values of AL. The number of landslide areas selected was based on the observed density of landslides (number of landslides km-2) in the triggered event inventories. Landslide shapes were approximated as ellipses, where the ratio of the major and minor axes varies with AL. Landslides were then dropped over the region semi-stochastically, conditioned by a landslide susceptibility map, resulting in a synthetic landslide inventory map. The originally available landslide susceptibility maps did not take into account susceptibility changes in the immediate vicinity of roads, therefore our landslide susceptibility map was adjusted to further reduce the susceptibility near each road based on the road level (primary, secondary, tertiary). For each model run, we superimposed the spatial location of landslide drops with the road network, and recorded the number, size and location of road blockages recorded, along with landslides within 50 and 100 m of the different road levels. Network analysis tools available in GRASS GIS were also applied to measure the impact upon the road network in terms of connectivity. The model was performed 100 times in a Monte-Carlo simulation for each region. Initial results show reasonable agreement between model output and the observed landslide inventories in terms of the number of road blockages. In Collazzone (length of road network = 153 km, landslide density = 5.2 landslides km-2), the median number of modelled road blockages over 100 model runs was 5 (±2.5 standard deviation) compared to the mapped inventory observed number of 5 road blockages. In Northridge (length of road network = 780 km, landslide density = 8.7 landslides km-2), the median number of modelled road blockages over 100 model runs was 108 (±17.2 standard deviation) compared to the mapped inventory observed number of 48 road blockages. As we progress with model development, we believe this semi-stochastic modelling approach will potentially aid civil protection agencies to explore different scenarios of road network potential damage as the result of different magnitude landslide triggering event scenarios.

The magnetic susceptibility of soils in Krakow, southern Poland

NASA Astrophysics Data System (ADS)

Wojas, Anna

2017-06-01

Studies into the magnetic susceptibility have been used to assess the soils contamination in the Krakow area. The results of topsoil (over a 2 × 2 km grid), subsoil (37 shallow holes) and soil samples (112) measurements were presented as maps of soil magnetic susceptibility (both volume and mass) illustrating the distribution of parameters in topsoil horizon (0-10 cm) and differential magnetic susceptibility maps between topsoil horizon and subsoil (40-60 cm). All evidence leads to the finding that the highest values of magnetic susceptibility of soil are found exclusively in industrial areas. Taking into consideration the type of land use, the high median value (89.8 × 10-8 m3kg-1) was obtained for samples of cultivated soils and is likely to be connected with occurrence of fertile soil (chernozem). Moreover, enrichment of soils with Pb and Zn accompanies magnetic susceptibility anomalies in the vicinity of the high roads and in the steelworks area, respectively.

Longitudinal atlas for normative human brain development and aging over the lifespan using quantitative susceptibility mapping.

PubMed

Zhang, Yuyao; Wei, Hongjiang; Cronin, Matthew J; He, Naying; Yan, Fuhua; Liu, Chunlei

2018-05-01

Longitudinal brain atlases play an important role in the study of human brain development and cognition. Existing atlases are mainly based on anatomical features derived from T1-and T2-weighted MRI. A 4D developmental quantitative susceptibility mapping (QSM) atlas may facilitate the estimation of age-related iron changes in deep gray matter nuclei and myelin changes in white matter. To this end, group-wise co-registered QSM templates were generated over various age intervals from age 1-83 years old. Registration was achieved by combining both T1-weighted and QSM images. Based on the proposed template, we created an accurate deep gray matter nuclei parcellation map (DGM map). Notably, we segmented thalamus into 5 sub-regions, i.e. the anterior nuclei, the median nuclei, the lateral nuclei, the pulvinar and the internal medullary lamina. Furthermore, we built a "whole brain QSM parcellation map" by combining existing cortical parcellation and white-matter atlases with the proposed DGM map. Based on the proposed QSM atlas, the segmentation accuracy of iron-rich nuclei using QSM is significantly improved, especially for children and adolescent subjects. The age-related progression of magnetic susceptibility in each of the deep gray matter nuclei, the hippocampus, and the amygdala was estimated. Our automated atlas-based analysis provided a systematic confirmation of previous findings on susceptibility progression with age resulting from manual ROI drawings in deep gray matter nuclei. The susceptibility development in the hippocampus and the amygdala follow an iron accumulation model; while in the thalamus sub-regions, the susceptibility development exhibits a variety of trends. It is envisioned that the newly developed 4D QSM atlas will serve as a template for studying brain iron deposition and myelination/demyelination in both normal aging and various brain diseases. Copyright © 2018 Elsevier Inc. All rights reserved.

Preoperative Cerebral Oxygen Extraction Fraction Imaging Generated from 7T MR Quantitative Susceptibility Mapping Predicts Development of Cerebral Hyperperfusion following Carotid Endarterectomy.

PubMed

Nomura, J-I; Uwano, I; Sasaki, M; Kudo, K; Yamashita, F; Ito, K; Fujiwara, S; Kobayashi, M; Ogasawara, K

2017-12-01

Preoperative hemodynamic impairment in the affected cerebral hemisphere is associated with the development of cerebral hyperperfusion following carotid endarterectomy. Cerebral oxygen extraction fraction images generated from 7T MR quantitative susceptibility mapping correlate with oxygen extraction fraction images on positron-emission tomography. The present study aimed to determine whether preoperative oxygen extraction fraction imaging generated from 7T MR quantitative susceptibility mapping could identify patients at risk for cerebral hyperperfusion following carotid endarterectomy. Seventy-seven patients with unilateral internal carotid artery stenosis (≥70%) underwent preoperative 3D T2*-weighted imaging using a multiple dipole-inversion algorithm with a 7T MR imager. Quantitative susceptibility mapping images were then obtained, and oxygen extraction fraction maps were generated. Quantitative brain perfusion single-photon emission CT was also performed before and immediately after carotid endarterectomy. ROIs were automatically placed in the bilateral middle cerebral artery territories in all images using a 3D stereotactic ROI template, and affected-to-contralateral ratios in the ROIs were calculated on quantitative susceptibility mapping-oxygen extraction fraction images. Ten patients (13%) showed post-carotid endarterectomy hyperperfusion (cerebral blood flow increases of ≥100% compared with preoperative values in the ROIs on brain perfusion SPECT). Multivariate analysis showed that a high quantitative susceptibility mapping-oxygen extraction fraction ratio was significantly associated with the development of post-carotid endarterectomy hyperperfusion (95% confidence interval, 33.5-249.7; P = .002). Sensitivity, specificity, and positive- and negative-predictive values of the quantitative susceptibility mapping-oxygen extraction fraction ratio for the prediction of the development of post-carotid endarterectomy hyperperfusion were 90%, 84%, 45%, and 98%, respectively. Preoperative oxygen extraction fraction imaging generated from 7T MR quantitative susceptibility mapping identifies patients at risk for cerebral hyperperfusion following carotid endarterectomy. © 2017 by American Journal of Neuroradiology.

Progress in landslide susceptibility mapping over Europe using Tier-based approaches

NASA Astrophysics Data System (ADS)

Günther, Andreas; Hervás, Javier; Reichenbach, Paola; Malet, Jean-Philippe

2010-05-01

The European Thematic Strategy for Soil Protection aims, among other objectives, to ensure a sustainable use of soil. The legal instrument of the strategy, the proposed Framework Directive, suggests identifying priority areas of several soil threats including landslides using a coherent and compatible approach based on the use of common thematic data. In a first stage, this can be achieved through landslide susceptibility mapping using geographically nested, multi-step tiered approaches, where areas identified as of high susceptibility by a first, synoptic-scale Tier ("Tier 1") can then be further assessed and mapped at larger scale by successive Tiers. In order to identify areas prone to landslides at European scale ("Tier 1"), a number of thematic terrain and environmental data sets already available for the whole of Europe can be used as input for a continental scale susceptibility model. However, since no coherent landslide inventory data is available at the moment over the whole continent, qualitative heuristic zonation approaches are proposed. For "Tier 1" a preliminary, simplified model has been developed. It consists of an equally weighting combination of a reduced, continent-wide common dataset of landslide conditioning factors including soil parent material, slope angle and land cover, to derive a landslide susceptibility index using raster mapping units consisting of 1 x 1 km pixels. A preliminary European-wide susceptibility map has thus been produced at 1:1 Million scale, since this is compatible with that of the datasets used. The map has been validated by means of a ratio of effectiveness using samples from landslide inventories in Italy, Austria, Hungary and United Kingdom. Although not differentiated for specific geomorphological environments or specific landslide types, the experimental model reveals a relatively good performance in many European regions at a 1:1 Million scale. An additional "Tier 1" susceptibility map at the same scale and using the same or equivalent thematic data as for the one above has been generated for six French departments using a heuristic, weighting-based multi-criteria evaluation model applied also to raster-cell mapping units. In this experiment, thematic data class weights have been differentiated for two stratification areas, namely mountains and plains, and four main landslide types. Separate susceptibility maps for each landslide type and a combined map for all types have been produced. Results have been validated using BRGM's BDMvT landslide inventory. Unlike "Tier 1", "Tier 2" assessment requires landslide inventory data and additional thematic data on conditioning factors which may not be available for all European countries. For the "Tier 2", a nation-wide quantitative landslide susceptibility assessment has been performed for Italy by applying a statistical model. In this assessment, multivariate analysis was applied using bedrock, soil and climate data together with a number of derivatives from SRTM90 DEM. In addition, separate datasets from a historical landslide inventory were used for model training and validation respectively. The mapping units selected were based on administrative boundaries (municipalities). The performance of this nation-wide, quantitative susceptibility assessment has been evaluated using multi-temporal landslide inventory data. Finally, model limitations for "Tier 1" are discussed, and recommendations for enhanced Tier 1 and Tier 2 models including additional thematic data for conditioning factors are drawn. This project is part of the collaborative research carried out within the European Landslide Expert Group coordinated by JRC in support to the EU Soil Thematic Strategy. It is also supported by the International Programme on Landslides of the International Consortium on Landslides.

A Heuristic Approach to Global Landslide Susceptibility Mapping

NASA Technical Reports Server (NTRS)

Stanley, Thomas; Kirschbaum, Dalia B.

2017-01-01

Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.

Shallow landslide prediction and analysis with risk assessment using a spatial model in the coastal region in the state of São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Camarinha, P. I. M.; Canavesi, V.; Alvalá, R. C. S.

2013-10-01

In Brazil, most of the disasters involving landslide occur in coastal regions, with population density concentrated on steep slopes. Thus, different approaches have been used to evaluate the landslide risk, although the greatest difficulty is related to the scarcity of spatial data with good quality. In this context, four cities located on the southeast coast of Brazil - Santos, Cubatão, Caraguatatuba and Ubatuba - in a region with the rough reliefs of the Serra do Mar and with a history of natural disasters were evaluated. Spatial prediction by fuzzy gamma technique was used for the landslide susceptibility mapping, considering environmental variables from data and software in the public domain. To validate the susceptibility mapping results, it was overlapped with risk sectors provided by the Geological Survey of Brazil (CPRM). A positive correlation was observed between the classes most susceptible and the location of these sectors. The results were also analyzed from the categorization of risk levels provided by CPRM. To compare the approach with other studies using landslide-scar maps, correlated indexes were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas and can be replicated to municipalities that do not have risk areas mapped.

Quantitative Susceptibility Mapping in Parkinson's Disease.

PubMed

Langkammer, Christian; Pirpamer, Lukas; Seiler, Stephan; Deistung, Andreas; Schweser, Ferdinand; Franthal, Sebastian; Homayoon, Nina; Katschnig-Winter, Petra; Koegl-Wallner, Mariella; Pendl, Tamara; Stoegerer, Eva Maria; Wenzel, Karoline; Fazekas, Franz; Ropele, Stefan; Reichenbach, Jürgen Rainer; Schmidt, Reinhold; Schwingenschuh, Petra

2016-01-01

Quantitative susceptibility mapping (QSM) and R2* relaxation rate mapping have demonstrated increased iron deposition in the substantia nigra of patients with idiopathic Parkinson's disease (PD). However, the findings in other subcortical deep gray matter nuclei are converse and the sensitivity of QSM and R2* for morphological changes and their relation to clinical measures of disease severity has so far been investigated only sparsely. The local ethics committee approved this study and all subjects gave written informed consent. 66 patients with idiopathic Parkinson's disease and 58 control subjects underwent quantitative MRI at 3T. Susceptibility and R2* maps were reconstructed from a spoiled multi-echo 3D gradient echo sequence. Mean susceptibilities and R2* rates were measured in subcortical deep gray matter nuclei and compared between patients with PD and controls as well as related to clinical variables. Compared to control subjects, patients with PD had increased R2* values in the substantia nigra. QSM also showed higher susceptibilities in patients with PD in substantia nigra, in the nucleus ruber, thalamus, and globus pallidus. Magnetic susceptibility of several of these structures was correlated with the levodopa-equivalent daily dose (LEDD) and clinical markers of motor and non-motor disease severity (total MDS-UPDRS, MDS-UPDRS-I and II). Disease severity as assessed by the Hoehn & Yahr scale was correlated with magnetic susceptibility in the substantia nigra. The established finding of higher R2* rates in the substantia nigra was extended by QSM showing superior sensitivity for PD-related tissue changes in nigrostriatal dopaminergic pathways. QSM additionally reflected the levodopa-dosage and disease severity. These results suggest a more widespread pathologic involvement and QSM as a novel means for its investigation, more sensitive than current MRI techniques.

Environmental geology, Allegheny County and vicinity, Pennsylvania; description of a program and its results

USGS Publications Warehouse

Briggs, Reginald Peter

1977-01-01

Past land-use practices, including mining, in Allegheny County, Pa., have resulted in three principal environmental problems, exclusive of air and water contamination. They are flooding, landsliding, and subsidence over underground mines. In 1973, information was most complete relative to flooding and least complete relative to landsliding. Accordingly, in July 1973, the U.S. Geological Survey (USGS) and The Appalachian Regional Commission (ARC) entered into an agreement by which the USGS undertook studies chiefly aimed at increasing knowledge of landsliding and mine subsidence relative to land use, but having other ramifications as well, as part of a larger ARC 'Land-use and physical-resource analysis' (LUPRA) program. The chief geographic focus was Allegheny County, but adjacent areas were included in some investigations. Resulting products, exclusive of this report, are: 1. Forty-three provisional maps of landslide, distribution and susceptibility and of land modified by man in Allegheny County, 1:24,000 scale, 7? -minute quadrangle format, released to open files. 2. Four USGS Miscellaneous Field Studies (MF) maps of Allegheny County showing (a) bedrock, MF685A; (b) susceptibility to landsliding, MF-685B ; (c) coal-mining features, MF-685C; and (d) zones that can be affected by flooding, landsliding and undermining, MF-685D; all at the scale of 1:50,000. 3. Two MF maps showing coal-mining activity and related information and sites of recorded mine-subsidence events, and one MF map classifying land surface by relative potentiality of mine subsidence, in Allegheny, Washington, and Westmoreland Counties, Pa., at a scale of 1:125,000--MF-693A through MF-693C. 4. A companion report to the Allegheny County map of susceptibility to landsliding--USGS Circular 728. 5. Five MF maps, largely in chart form, describing interaction of the shallow ground-water regime with mining-related problems, landsliding, heavy storm precipitation, and other features and processes, largely in Allegheny County--MF-641A through MF-641E. Map products are directly applicable to general classification of land for susceptibility to landsliding and mine subsidence and, to a lesser extent, flooding and engineering characteristics. The hydrogeologic charts enable greater understanding of environmental effects of ground water. All products are guides to expected conditions, but none are substitutes for detailed investigations of specific sites by competent technical personnel on the ground. Specific results and findings are: 1. Knowledge of .susceptibility to landsliding in Allegheny County now is adequate for application to countywide land-use planning. 2. About 110 mi2 (285. km2), or 15 percent, of the county has some significant degree of susceptibility to landsliding. 3. Although a general classification of land in Allegheny, Washington, and Westmoreland Counties relative to mine-subsidence incidents was prepared, data are wholly inadequate for even moderately precise prediction of subsidence events over previously mined-out areas; the accumulation of adequate data might not repay the effort in terms of damage prevention. 4. Commonwealth-of-Pennsylvania regulations, have been very successful in limiting mine-subsidence damage over areas mined after 1966. 5. Undermining and consequent subsidence may have affected the ground-water regime more widely than heretofore believed. Except for the earth-disturbance inventory that resulted in the maps of susceptibility to landsliding and man-modified land, methods used in the studies .largely were conventional. The inventory and ensuing analysis combined aerial photographic interpretation with field work and incorporation of existing data. The. method worked very well for the purposes of defining distribution of landslides and areas having different susceptibilities to landsliding. However, if susceptibility to landsliding alone had been the goal, this could

STrategically Acquired Gradient Echo (STAGE) imaging, part I: Creating enhanced T1 contrast and standardized susceptibility weighted imaging and quantitative susceptibility mapping.

PubMed

Chen, Yongsheng; Liu, Saifeng; Wang, Yu; Kang, Yan; Haacke, E Mark

2018-02-01

To provide whole brain grey matter (GM) to white matter (WM) contrast enhanced T1W (T1WE) images, multi-echo quantitative susceptibility mapping (QSM), proton density (PD) weighted images, T1 maps, PD maps, susceptibility weighted imaging (SWI), and R2* maps with minimal misregistration in scanning times <5min. Strategically acquired gradient echo (STAGE) imaging includes two fully flow compensated double echo gradient echo acquisitions with a resolution of 0.67×1.33×2.0mm 3 acquired in 5min for 64 slices. Ten subjects were recruited and scanned at 3 Tesla. The optimum pair of flip angles (6° and 24° with TR=25ms at 3T) were used for both T1 mapping with radio frequency (RF) transmit field correction and creating enhanced GM/WM contrast (the T1WE). The proposed T1WE image was created from a combination of the proton density weighted (6°, PDW) and T1W (24°) images and corrected for RF transmit field variations. Prior to the QSM calculation, a multi-echo phase unwrapping strategy was implemented using the unwrapped short echo to unwrap the longer echo to speed up computation. R2* maps were used to mask deep grey matter and veins during the iterative QSM calculation. A weighted-average sum of susceptibility maps was generated to increase the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR). The proposed T1WE image has a significantly improved CNR both for WM to deep GM and WM to cortical GM compared to the acquired T1W image (the first echo of 24° scan) and the T1MPRAGE image. The weighted-average susceptibility maps have 80±26%, 55±22%, 108±33% SNR increases across the ten subjects compared to the single echo result of 17.5ms for the putamen, caudate nucleus, and globus pallidus, respectively. STAGE imaging offers the potential to create a standardized brain imaging protocol providing four pieces of quantitative tissue property information and multiple types of qualitative information in just 5min. Published by Elsevier Inc.

Spatially explicit shallow landslide susceptibility mapping over large areas

USGS Publications Warehouse

Bellugi, Dino; Dietrich, William E.; Stock, Jonathan D.; McKean, Jim; Kazian, Brian; Hargrove, Paul

2011-01-01

Recent advances in downscaling climate model precipitation predictions now yield spatially explicit patterns of rainfall that could be used to estimate shallow landslide susceptibility over large areas. In California, the United States Geological Survey is exploring community emergency response to the possible effects of a very large simulated storm event and to do so it has generated downscaled precipitation maps for the storm. To predict the corresponding pattern of shallow landslide susceptibility across the state, we have used the model Shalstab (a coupled steady state runoff and infinite slope stability model) which susceptibility spatially explicit estimates of relative potential instability. Such slope stability models that include the effects of subsurface runoff on potentially destabilizing pore pressure evolution require water routing and hence the definition of upslope drainage area to each potential cell. To calculate drainage area efficiently over a large area we developed a parallel framework to scale-up Shalstab and specifically introduce a new efficient parallel drainage area algorithm which produces seamless results. The single seamless shallow landslide susceptibility map for all of California was accomplished in a short run time, and indicates that much larger areas can be efficiently modelled. As landslide maps generally over predict the extent of instability for any given storm. Local empirical data on the fraction of predicted unstable cells that failed for observed rainfall intensity can be used to specify the likely extent of hazard for a given storm. This suggests that campaigns to collect local precipitation data and detailed shallow landslide location maps after major storms could be used to calibrate models and improve their use in hazard assessment for individual storms.

Assessment of Groundwater Susceptibility to Non-Point Source Contaminants Using Three-Dimensional Transient Indexes.

PubMed

Zhang, Yong; Weissmann, Gary S; Fogg, Graham E; Lu, Bingqing; Sun, HongGuang; Zheng, Chunmiao

2018-06-05

Groundwater susceptibility to non-point source contamination is typically quantified by stable indexes, while groundwater quality evolution (or deterioration globally) can be a long-term process that may last for decades and exhibit strong temporal variations. This study proposes a three-dimensional (3- d ), transient index map built upon physical models to characterize the complete temporal evolution of deep aquifer susceptibility. For illustration purposes, the previous travel time probability density (BTTPD) approach is extended to assess the 3- d deep groundwater susceptibility to non-point source contamination within a sequence stratigraphic framework observed in the Kings River fluvial fan (KRFF) aquifer. The BTTPD, which represents complete age distributions underlying a single groundwater sample in a regional-scale aquifer, is used as a quantitative, transient measure of aquifer susceptibility. The resultant 3- d imaging of susceptibility using the simulated BTTPDs in KRFF reveals the strong influence of regional-scale heterogeneity on susceptibility. The regional-scale incised-valley fill deposits increase the susceptibility of aquifers by enhancing rapid downward solute movement and displaying relatively narrow and young age distributions. In contrast, the regional-scale sequence-boundary paleosols within the open-fan deposits "protect" deep aquifers by slowing downward solute movement and displaying a relatively broad and old age distribution. Further comparison of the simulated susceptibility index maps to known contaminant distributions shows that these maps are generally consistent with the high concentration and quick evolution of 1,2-dibromo-3-chloropropane (DBCP) in groundwater around the incised-valley fill since the 1970s'. This application demonstrates that the BTTPDs can be used as quantitative and transient measures of deep aquifer susceptibility to non-point source contamination.

Phase processing for quantitative susceptibility mapping of regions with large susceptibility and lack of signal.

PubMed

Fortier, Véronique; Levesque, Ives R

2018-06-01

Phase processing impacts the accuracy of quantitative susceptibility mapping (QSM). Techniques for phase unwrapping and background removal have been proposed and demonstrated mostly in brain. In this work, phase processing was evaluated in the context of large susceptibility variations (Δχ) and negligible signal, in particular for susceptibility estimation using the iterative phase replacement (IPR) algorithm. Continuous Laplacian, region-growing, and quality-guided unwrapping were evaluated. For background removal, Laplacian boundary value (LBV), projection onto dipole fields (PDF), sophisticated harmonic artifact reduction for phase data (SHARP), variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP), regularization enabled sophisticated harmonic artifact reduction for phase data (RESHARP), and 3D quadratic polynomial field removal were studied. Each algorithm was quantitatively evaluated in simulation and qualitatively in vivo. Additionally, IPR-QSM maps were produced to evaluate the impact of phase processing on the susceptibility in the context of large Δχ with negligible signal. Quality-guided unwrapping was the most accurate technique, whereas continuous Laplacian performed poorly in this context. All background removal algorithms tested resulted in important phase inaccuracies, suggesting that techniques used for brain do not translate well to situations where large Δχ and no or low signal are expected. LBV produced the smallest errors, followed closely by PDF. Results suggest that quality-guided unwrapping should be preferred, with PDF or LBV for background removal, for QSM in regions with large Δχ and negligible signal. This reduces the susceptibility inaccuracy introduced by phase processing. Accurate background removal remains an open question. Magn Reson Med 79:3103-3113, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda.

PubMed

Nsengiyumva, Jean Baptiste; Luo, Geping; Nahayo, Lamek; Huang, Xiaotao; Cai, Peng

2018-01-31

Landslides susceptibility assessment has to be conducted to identify prone areas and guide risk management. Landslides in Rwanda are very deadly disasters. The current research aimed to conduct landslide susceptibility assessment by applying Spatial Multi-Criteria Evaluation Model with eight layers of causal factors including: slope, distance to roads, lithology, precipitation, soil texture, soil depth, altitude and land cover. In total, 980 past landslide locations were mapped. The relationship between landslide factors and inventory map was calculated using the Spatial Multi-Criteria Evaluation. The results revealed that susceptibility is spatially distributed countrywide with 42.3% of the region classified from moderate to very high susceptibility, and this is inhabited by 49.3% of the total population. In addition, Provinces with high to very high susceptibility are West, North and South (40.4%, 22.8% and 21.5%, respectively). Subsequently, the Eastern Province becomes the peak under low susceptibility category (87.8%) with no very high susceptibility (0%). Based on these findings, the employed model produced accurate and reliable outcome in terms of susceptibility, since 49.5% of past landslides fell within the very high susceptibility category, which confirms the model's performance. The outcomes of this study will be useful for future initiatives related to landslide risk reduction and management.

Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda

PubMed Central

Nsengiyumva, Jean Baptiste; Luo, Geping; Nahayo, Lamek; Huang, Xiaotao; Cai, Peng

2018-01-01

Landslides susceptibility assessment has to be conducted to identify prone areas and guide risk management. Landslides in Rwanda are very deadly disasters. The current research aimed to conduct landslide susceptibility assessment by applying Spatial Multi-Criteria Evaluation Model with eight layers of causal factors including: slope, distance to roads, lithology, precipitation, soil texture, soil depth, altitude and land cover. In total, 980 past landslide locations were mapped. The relationship between landslide factors and inventory map was calculated using the Spatial Multi-Criteria Evaluation. The results revealed that susceptibility is spatially distributed countrywide with 42.3% of the region classified from moderate to very high susceptibility, and this is inhabited by 49.3% of the total population. In addition, Provinces with high to very high susceptibility are West, North and South (40.4%, 22.8% and 21.5%, respectively). Subsequently, the Eastern Province becomes the peak under low susceptibility category (87.8%) with no very high susceptibility (0%). Based on these findings, the employed model produced accurate and reliable outcome in terms of susceptibility, since 49.5% of past landslides fell within the very high susceptibility category, which confirms the model’s performance. The outcomes of this study will be useful for future initiatives related to landslide risk reduction and management. PMID:29385096

Imaging Cerebral Microhemorrhages in Military Service Members with Chronic Traumatic Brain Injury.

PubMed

Liu, Wei; Soderlund, Karl; Senseney, Justin S; Joy, David; Yeh, Ping-Hong; Ollinger, John; Sham, Elyssa B; Liu, Tian; Wang, Yi; Oakes, Terrence R; Riedy, Gerard

2016-02-01

To detect cerebral microhemorrhages in military service members with chronic traumatic brain injury by using susceptibility-weighted magnetic resonance (MR) imaging. The longitudinal evolution of microhemorrhages was monitored in a subset of patients by using quantitative susceptibility mapping. The study was approved by the Walter Reed National Military Medical Center institutional review board and is compliant with HIPAA guidelines. All participants underwent two-dimensional conventional gradient-recalled-echo MR imaging and three-dimensional flow-compensated multiecho gradient-recalled-echo MR imaging (processed to generate susceptibility-weighted images and quantitative susceptibility maps), and a subset of patients underwent follow-up imaging. Microhemorrhages were identified by two radiologists independently. Comparisons of microhemorrhage number, size, and magnetic susceptibility derived from quantitative susceptibility maps between baseline and follow-up imaging examinations were performed by using the paired t test. Among the 603 patients, cerebral microhemorrhages were identified in 43 patients, with six excluded for further analysis owing to artifacts. Seventy-seven percent (451 of 585) of the microhemorrhages on susceptibility-weighted images had a more conspicuous appearance than on gradient-recalled-echo images. Thirteen of the 37 patients underwent follow-up imaging examinations. In these patients, a smaller number of microhemorrhages were identified at follow-up imaging compared with baseline on quantitative susceptibility maps (mean ± standard deviation, 9.8 microhemorrhages ± 12.8 vs 13.7 microhemorrhages ± 16.6; P = .019). Quantitative susceptibility mapping-derived quantitative measures of microhemorrhages also decreased over time: -0.85 mm(3) per day ± 1.59 for total volume (P = .039) and -0.10 parts per billion per day ± 0.14 for mean magnetic susceptibility (P = .016). The number of microhemorrhages and quantitative susceptibility mapping-derived quantitative measures of microhemorrhages all decreased over time, suggesting that hemosiderin products undergo continued, subtle evolution in the chronic stage. © RSNA, 2015.

Policy Implications and Suggestions on Administrative Measures of Urban Flood

NASA Astrophysics Data System (ADS)

Lee, S. V.; Lee, M. J.; Lee, C.; Yoon, J. H.; Chae, S. H.

2017-12-01

The frequency and intensity of floods are increasing worldwide as recent climate change progresses gradually. Flood management should be policy-oriented in urban municipalities due to the characteristics of urban areas with a lot of damage. Therefore, the purpose of this study is to prepare a flood susceptibility map by using data mining model and make a policy suggestion on administrative measures of urban flood. Therefore, we constructed a spatial database by collecting relevant factors including the topography, geology, soil and land use data of the representative city, Seoul, the capital city of Korea. Flood susceptibility map was constructed by applying the data mining models of random forest and boosted tree model to input data and existing flooded area data in 2010. The susceptibility map has been validated using the 2011 flood area data which was not used for training. The predictor importance value of each factor to the results was calculated in this process. The distance from the water, DEM and geology showed a high predictor importance value which means to be a high priority for flood preparation policy. As a result of receiver operating characteristic (ROC), random forest model showed 78.78% and 79.18% accuracy of regression and classification and boosted tree model showed 77.55% and 77.26% accuracy of regression and classification, respectively. The results show that the flood susceptibility maps can be applied to flood prevention and management, and it also can help determine the priority areas for flood mitigation policy by providing useful information to policy makers.

Lateral Asymmetry and Spatial Difference of Iron Deposition in the Substantia Nigra of Patients with Parkinson Disease Measured with Quantitative Susceptibility Mapping.

PubMed

Azuma, M; Hirai, T; Yamada, K; Yamashita, S; Ando, Y; Tateishi, M; Iryo, Y; Yoneda, T; Kitajima, M; Wang, Y; Yamashita, Y

2016-05-01

Quantitative susceptibility mapping is useful for assessing iron deposition in the substantia nigra of patients with Parkinson disease. We aimed to determine whether quantitative susceptibility mapping is useful for assessing the lateral asymmetry and spatial difference in iron deposits in the substantia nigra of patients with Parkinson disease. Our study population comprised 24 patients with Parkinson disease and 24 age- and sex-matched healthy controls. They underwent 3T MR imaging by using a 3D multiecho gradient-echo sequence. On reconstructed quantitative susceptibility mapping, we measured the susceptibility values in the anterior, middle, and posterior parts of the substantia nigra, the whole substantia nigra, and other deep gray matter structures in both hemibrains. To identify the more and less affected hemibrains in patients with Parkinson disease, we assessed the severity of movement symptoms for each hemibrain by using the Unified Parkinson's Disease Rating Scale. In the posterior substantia nigra of patients with Parkinson disease, the mean susceptibility value was significantly higher in the more than the less affected hemibrain substantia nigra (P < .05). This value was significantly higher in both the more and less affected hemibrains of patients with Parkinson disease than in controls (P < .05). Asymmetry of the mean susceptibility values was significantly greater for patients than controls (P < .05). Receiver operating characteristic analysis showed that quantitative susceptibility mapping of the posterior substantia nigra in the more affected hemibrain provided the highest power for discriminating patients with Parkinson disease from the controls. Quantitative susceptibility mapping is useful for assessing the lateral asymmetry and spatial difference of iron deposition in the substantia nigra of patients with Parkinson disease. © 2016 by American Journal of Neuroradiology.

Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: comparison with COSMOS in human brain imaging.

PubMed

Liu, Tian; Liu, Jing; de Rochefort, Ludovic; Spincemaille, Pascal; Khalidov, Ildar; Ledoux, James Robert; Wang, Yi

2011-09-01

Magnetic susceptibility varies among brain structures and provides insights into the chemical and molecular composition of brain tissues. However, the determination of an arbitrary susceptibility distribution from the measured MR signal phase is a challenging, ill-conditioned inverse problem. Although a previous method named calculation of susceptibility through multiple orientation sampling (COSMOS) has solved this inverse problem both theoretically and experimentally using multiple angle acquisitions, it is often impractical to carry out on human subjects. Recently, the feasibility of calculating the brain susceptibility distribution from a single-angle acquisition was demonstrated using morphology enabled dipole inversion (MEDI). In this study, we further improved the original MEDI method by sparsifying the edges in the quantitative susceptibility map that do not have a corresponding edge in the magnitude image. Quantitative susceptibility maps generated by the improved MEDI were compared qualitatively and quantitatively with those generated by calculation of susceptibility through multiple orientation sampling. The results show a high degree of agreement between MEDI and calculation of susceptibility through multiple orientation sampling, and the practicality of MEDI allows many potential clinical applications. Copyright © 2011 Wiley-Liss, Inc.

Accuracy of MRI-based Magnetic Susceptibility Measurements

NASA Astrophysics Data System (ADS)

Russek, Stephen; Erdevig, Hannah; Keenan, Kathryn; Stupic, Karl

Magnetic Resonance Imaging (MRI) is increasingly used to map tissue susceptibility to identify microbleeds associated with brain injury and pathologic iron deposits associated with neurologic diseases such as Parkinson's and Alzheimer's disease. Field distortions with a resolution of a few parts per billion can be measured using MRI phase maps. The field distortion map can be inverted to obtain a quantitative susceptibility map. To determine the accuracy of MRI-based susceptibility measurements, a set of phantoms with paramagnetic salts and nano-iron gels were fabricated. The shapes and orientations of features were varied. Measured susceptibility of 1.0 mM GdCl3 solution in water as a function of temperature agreed well with the theoretical predictions, assuming Gd+3 is spin 7/2. The MRI susceptibility measurements were compared with SQUID magnetometry. The paramagnetic susceptibility sits on top of the much larger diamagnetic susceptibility of water (-9.04 x 10-6), which leads to errors in the SQUID measurements. To extract out the paramagnetic contribution using standard magnetometry, measurements must be made down to low temperature (2K). MRI-based susceptometry is shown to be as or more accurate than standard magnetometry and susceptometry techniques.

Regional liquefaction hazard evaluation following the 2010-2011 Christchurch (New Zealand) earthquake sequence

NASA Astrophysics Data System (ADS)

Begg, John; Brackley, Hannah; Irwin, Marion; Grant, Helen; Berryman, Kelvin; Dellow, Grant; Scott, David; Jones, Katie; Barrell, David; Lee, Julie; Townsend, Dougal; Jacka, Mike; Harwood, Nick; McCahon, Ian; Christensen, Steve

2013-04-01

Following the damaging 4 Sept 2010 Mw7.1 Darfield Earthquake, the 22 Feb 2011 Christchurch Earthquake and subsequent damaging aftershocks, we completed a liquefaction hazard evaluation for c. 2700 km2 of the coastal Canterbury region. Its purpose was to distinguish at a regional scale areas of land that, in the event of strong ground shaking, may be susceptible to damaging liquefaction from areas where damaging liquefaction is unlikely. This information will be used by local government for defining liquefaction-related geotechnical investigation requirements for consent applications. Following a review of historic records of liquefaction and existing liquefaction assessment maps, we undertook comprehensive new work that included: a geologic context from existing geologic maps; geomorphic mapping using LiDAR and integrating existing soil map data; compilation of lithological data for the surficial 10 m from an extensive drillhole database; modelling of depth to unconfined groundwater from existing subsurface and surface water data. Integrating and honouring all these sources of information, we mapped areas underlain by materials susceptible to liquefaction (liquefaction-prone lithologies present, or likely, in the near-surface, with shallow unconfined groundwater) from areas unlikely to suffer widespread liquefaction damage. Comparison of this work with more detailed liquefaction susceptibility assessment based on closely spaced geotechnical probes in Christchurch City provides a level of confidence in these results. We tested our susceptibility map by assigning a matrix of liquefaction susceptibility rankings to lithologies recorded in drillhole logs and local groundwater depths, then applying peak ground accelerations for four earthquake scenarios from the regional probabilistic seismic hazard model (25 year return = 0.13g; 100 year return = 0.22g; 500 year return = 0.38g and 2500 year return = 0.6g). Our mapped boundary between liquefaction-prone areas and areas unlikely to sustain heavy damage proved sound. In addition, we compared mapped liquefaction extents (derived from post-earthquake aerial photographs) from the 4 Sept 2010 Mw7.1 and 22 Feb 2011 Mw6.2 earthquakes with our liquefaction susceptibility map. The overall area of liquefaction for these two earthquakes was similar, and statistics show that for the first (large regional) earthquake, c. 93% of mapped liquefaction fell within the liquefaction-prone area, and for the second (local, high peak ground acceleration) earthquake, almost 99% fell within the liquefaction-prone area. We conclude that basic geological and groundwater data when coupled with LiDAR data can usefully delineate areas susceptible to liquefaction from those unlikely to suffer damaging liquefaction. We believe that these techniques can be used successfully in many other cities around the world.

Comparing two models for post-wildfire debris flow susceptibility mapping

NASA Astrophysics Data System (ADS)

Cramer, J.; Bursik, M. I.; Legorreta Paulin, G.

2017-12-01

Traditionally, probabilistic post-fire debris flow susceptibility mapping has been performed based on the typical method of failure for debris flows/landslides, where slip occurs along a basal shear zone as a result of rainfall infiltration. Recent studies have argued that post-fire debris flows are fundamentally different in their method of initiation, which is not infiltration-driven, but surface runoff-driven. We test these competing models by comparing the accuracy of the susceptibility maps produced by each initiation method. Debris flow susceptibility maps are generated according to each initiation method for a mountainous region of Southern California that recently experienced wildfire and subsequent debris flows. A multiple logistic regression (MLR), which uses the occurrence of past debris flows and the values of environmental parameters, was used to determine the probability of future debris flow occurrence. The independent variables used in the MLR are dependent on the initiation method; for example, depth to slip plane, and shear strength of soil are relevant to the infiltration initiation, but not surface runoff. A post-fire debris flow inventory serves as the standard to compare the two susceptibility maps, and was generated by LiDAR analysis and field based ground-truthing. The amount of overlap between the true locations where debris flow erosion can be documented, and where the MLR predicts high probability of debris flow initiation was statistically quantified. The Figure of Merit in Space (FMS) was used to compare the two models, and the results of the FMS comparison suggest that surface runoff-driven initiation better explains debris flow occurrence. Wildfire can breed conditions that induce debris flows in areas that normally would not be prone to them. Because of this, nearby communities at risk may not be equipped to protect themselves against debris flows. In California, there are just a few months between wildland fire season and the wet season to assess a community's risk and prepare. It is important, therefore, that researchers have a way to quickly and accurately assess the susceptibility for debris flows in recently burned areas.

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Hoang, Nhat-Duc

2017-09-01

In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

Comprehensive Clinical Phenotyping and Genetic Mapping for the Discovery of Autism Susceptibility Genes

DTIC Science & Technology

2013-03-14

SUPPLEMENTARY NOTES 14. ABSTRACT Autism is an extremely common and heterogeneous neurodevelopmental disorder. While genetic factors are known to play...AFRL-SA-WP-TR-2013-0013 Comprehensive Clinical Phenotyping and Genetic Mapping for the Discovery of Autism Susceptibility Genes...Genetic Mapping for the Discovery of Autism Susceptibility Genes 5a. CONTRACT NUMBER N/A 5b. GRANT NUMBER N/A 5c. PROGRAM ELEMENT NUMBER N/A 6

Multiscale/multiresolution landslides susceptibility mapping

NASA Astrophysics Data System (ADS)

Grozavu, Adrian; Cătălin Stanga, Iulian; Valeriu Patriche, Cristian; Toader Juravle, Doru

2014-05-01

Within the European strategies, landslides are considered an important threatening that requires detailed studies to identify areas where these processes could occur in the future and to design scientific and technical plans for landslide risk mitigation. In this idea, assessing and mapping the landslide susceptibility is an important preliminary step. Generally, landslide susceptibility at small scale (for large regions) can be assessed through qualitative approach (expert judgements), based on a few variables, while studies at medium and large scale requires quantitative approach (e.g. multivariate statistics), a larger set of variables and, necessarily, the landslide inventory. Obviously, the results vary more or less from a scale to another, depending on the available input data, but also on the applied methodology. Since it is almost impossible to have a complete landslide inventory on large regions (e.g. at continental level), it is very important to verify the compatibility and the validity of results obtained at different scales, identifying the differences and fixing the inherent errors. This paper aims at assessing and mapping the landslide susceptibility at regional level through a multiscale-multiresolution approach from small scale and low resolution to large scale and high resolution of data and results, comparing the compatibility of results. While the first ones could be used for studies at european and national level, the later ones allows results validation, including through fields surveys. The test area, namely the Barlad Plateau (more than 9000 sq.km) is located in Eastern Romania, covering a region where both the natural environment and the human factor create a causal context that favor these processes. The landslide predictors were initially derived from various databases available at pan-european level and progressively completed and/or enhanced together with scale and the resolution: the topography (from SRTM at 90 meters to digital elevation models based on topographical maps, 1:25,000 and 1:5,000), the lithology (from geological maps, 1:200,000), land cover and land use (from CLC 2006 to maps derived from orthorectified aerial images, 0.5 meters resolution), rainfall (from Worldclim, ECAD to our own data), the seismicity (the seismic zonation of Romania) etc. The landslide inventory was created as polygonal data based on aerial images (resolution 0.5 meters), the information being considered at county level (NUTS 3) and, eventually, at communal level (LAU2). The methodological framework is based on the logistic regression as a quantitative method and the analytic hierarchy process as a semi-qualitative methods, both being applied once identically for all scales and once recalibrated for each scale and resolution (from 1:1,000,000 and one km pixel resolution to 1:25,000 and ten meters resolution). The predictive performance of the two models was assessed using the ROC (Receiver Operating Characteristic) curve and the AUC (Area Under Curve) parameter and the results indicate a good correspondence between the susceptibility estimated for the test samples (0.855-0.890) and for the validation samples (0.830-0.865). Finally, the results were compared in pairs in order to fix the errors at small scale and low resolution and to optimize the methodology for landslide susceptibility mapping on large areas.

The landslide susceptibility mapping and assessment with ZY satellite data

NASA Astrophysics Data System (ADS)

Zhang, R.; Zhang, Z.; Zhao, Y.

2012-12-01

Natural hazards can result in enormous property damage and casualties in mountainous regions. In China, the direct loss of hazards is about 400 million yuan in 2011. Especially the landslide, the most common natural hazards, got the wide attention of each country. Landslide susceptibility mapping is of great importance for landslide hazard mitigation efforts throughout the world. In Southwest Hubei, there are much mineral mining activities, which may trigger the landslide. In addition the Three Gorges reservoir is located in this area, and the storage changed the geological and hydrological environment, which may increase the frequency of the ancient landslide reactivation, and the new landslide occurrence. There are more than 200 landslide hazards happened since 2003. So producing a regional-scaled landslide susceptibility map is necessary. For the above purpose, the landslide susceptibility mapping was produced by using the ZY-3 and ZY-1-02C satellite data, the DEMs and the conventional topographic data.(1) The DEM derivatives slope gradient, the slope aspect and the topographic wetness index (TWI) ; (2) in order to acquire the spatially continuous vegetation information, Normalized Difference Vegetation Index (NDVI) was computed using ZY-1-02C and ZY-3; (3) the regional lithologic information (i.e. mineral distribution) and the tectonic information obtained from remote sensing data in combination with regional geological survey; (4) the regional hydrogeological information was produced by using the remote sensing data in combination with the DEMs; (5) the existed landslides information obtained from remote sensing. To model the landslide hazard assessment using variety of statistic methods and evaluation methods, the cross application model yields reasonable results which can be applied for preliminary landslide hazard mapping and the hazard grade division.

Validating national landslide susceptibility and hazard maps for Caribbean island countries: the case of Dominica and tropical storm Erika.

NASA Astrophysics Data System (ADS)

van Westen, Cees; Jetten, Victor; Alkema, Dinand

2016-04-01

The aim of this study was to generate national-scale landslide susceptibility and hazard maps for four Caribbean islands, as part of the World Bank project CHARIM (Caribbean Handbook on Disaster Geoinformation Management, www.charim.net). This paper focuses on the results for the island country of Dominica, located in the Eastern part of the Caribbean, in-between Guadalupe and Martinique. The available data turned out to be insufficient to generate reliable results. We therefore generated a new database of disaster events for Dominica using all available data, making use of many different sources. We compiled landslide inventories for five recent rainfall events from the maintenance records of the Ministry of Public Works, and generated a completely new landslide inventory using multi-temporal visual image interpretation, and generated an extensive landslide database for Dominica. We analyzed the triggering conditions for landslides as far as was possible given the available data, and generated rainfall magnitude-frequency relations. We applied a method for landslide susceptibility assessment which combined bi-variate statistical analysis, that provided indications on the importance of the possible contributing factors, with an expert-based iterative weighing approach using Spatial Multi-Criteria Evaluation. The method is transparent, as the stakeholders (e.g. the engineers and planners from the four countries) and other consultants can consult the criteria trees and evaluate the standardization and weights, and make adjustments. The landslide susceptibility map was converted into a landslide hazard map using landslide density and frequencies for so called major, moderate and minor triggering events. The landslide hazard map was produced in May 2015. A major rainfall event occurred on Dominica following the passage of tropical storm Erika on 26 to 28 August 2015. An event-based landslide inventory for this event was produced by UNOSAT using very high resolution optical images, and an additional field-based inventory was obtained from BRGM. These were used to analyze the predictive capabilities of the national-scale landslide susceptibility and hazard maps. Although the spatial patterns of the landslide susceptibility map was fairly accurate in predicting the locations of the landslides triggered by the recent tropical storm, the landslide densities and related frequencies used for the hazard assessment turned out to deviate considerably taking into account the spatial landslide pattern and estimated frequency of rainfall for tropical storm Erika. This study demonstrates the importance of reconstructing landslide inventories for a variety of triggering events, and the requirement of including landslide inventory data of a major event in the hazard assessment.

A method of mapping sinkhole susceptibility using a geographic information system : a case study for interstates in the karst counties of Virginia.

DOT National Transportation Integrated Search

2015-02-01

This study proposes the use of a geographic information system (GIS) to create a susceptibility map, pinpointing : regions in the karst counties of Virginia, in particular, along interstates, most susceptible to future sinkhole : development, determi...

Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment

PubMed Central

Hashim, Mazlan

2015-01-01

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning. PMID:25898919

Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment.

PubMed

Shahabi, Himan; Hashim, Mazlan

2015-04-22

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning.

Landslides susceptibility change over time according to terrain conditions in a mountain area of the tropic region.

PubMed

Pineda, M C; Viloria, J; Martínez-Casasnovas, J A

2016-04-01

Susceptibility to landslides in mountain areas results from the interaction of various factors related to relief formation and soil development. The assessment of landslide susceptibility has generally taken into account individual events, or it has been aimed at establishing relationships between landslide-inventory maps and maps of environmental factors, without considering that such relationships can change in space and time. In this work, temporal and space changes in landslides were analysed in six different combinations of date and geomorphological conditions, including two different geological units, in a mountainous area in the north-centre of Venezuela, in northern South America. Landslide inventories from different years were compared with a number of environmental factors by means of logistic regression analysis. The resulting equations predicted landslide susceptibility from a range of geomorphometric parameters and a vegetation index, with diverse accuracy, in the study area. The variation of the obtained models and their prediction accuracy between geological units and dates suggests that the complexity of the landslide processes and their explanatory factors changed over space and time in the studied area. This calls into question the use of a single model to evaluate landslide susceptibility over large regions.

Quantitative Susceptibility Mapping Reveals an Association between Brain Iron Load and Depression Severity

PubMed Central

Yao, Shun; Zhong, Yi; Xu, Yuhao; Qin, Jiasheng; Zhang, Ningning; Zhu, Xiaolan; Li, Yuefeng

2017-01-01

Previous studies have detected abnormal serum ferritin levels in patients with depression; however, the results have been inconsistent. This study used quantitative susceptibility mapping (QSM) for the first time to examine brain iron concentration in depressed patients and evaluated whether it is related to severity. We included three groups of age- and gender-matched participants: 30 patients with mild-moderate depression (MD), 14 patients with major depression disorder (MDD) and 20 control subjects. All participants underwent MR scans with a 3D gradient-echo sequence reconstructing for QSM and performed the 17-item Hamilton Depression Rating Scale (HDRS) test. In MDD, the susceptibility value in the bilateral putamen was significantly increased compared with MD or control subjects. In addition, a significant difference was also observed in the left thalamus in MDD patients compared with controls. However, the susceptibility values did not differ between MD patients and controls. The susceptibility values positively correlated with the severity of depression as indicated by the HDRS scores. Our results provide evidence that brain iron deposition may be associated with depression and may even be a biomarker for investigating the pathophysiological mechanism of depression. PMID:28900391

Comparison of two landslide susceptibility assessments in the Champagne-Ardenne region (France)

NASA Astrophysics Data System (ADS)

Den Eeckhaut, M. Van; Marre, A.; Poesen, J.

2010-02-01

The vineyards of the Montagne de Reims are mostly planted on steep south-oriented cuesta fronts receiving a maximum of sun radiation. Due to the location of the vineyards on steep hillslopes, the viticultural activity is threatened by slope failures. This study attempts to better understand the spatial patterns of landslide susceptibility in the Champagne-Ardenne region by comparing a heuristic (qualitative) and a statistical (quantitative) model in a 1120 km² study area. The heuristic landslide susceptibility model was adopted from the Bureau de Recherches Géologiques et Minières, the GEGEAA - Reims University and the Comité Interprofessionnel du Vin de Champagne. In this model, expert knowledge of the region was used to assign weights to all slope classes and lithologies present in the area, but the final susceptibility map was never evaluated with the location of mapped landslides. For the statistical landslide susceptibility assessment, logistic regression was applied to a dataset of 291 'old' (Holocene) landslides. The robustness of the logistic regression model was evaluated and ROC curves were used for model calibration and validation. With regard to the variables assumed to be important environmental factors controlling landslides, the two models are in agreement. They both indicate that present and future landslides are mainly controlled by slope gradient and lithology. However, the comparison of the two landslide susceptibility maps through (1) an evaluation with the location of mapped 'old' landslides and through (2) a temporal validation with spatial data of 'recent' (1960-1999; n = 48) and 'very recent' (2000-2008; n = 46) landslides showed a better prediction capacity for the statistical model produced in this study compared to the heuristic model. In total, the statistically-derived landslide susceptibility map succeeded in correctly classifying 81.0% of the 'old' and 91.6% of the 'recent' and 'very recent' landslides. On the susceptibility map derived from the heuristic model, on the other hand, only 54.6% of the 'old' and 64.0% of the 'recent' and 'very recent' landslides were correctly classified as unstable. Hence, the landslide susceptibility map obtained from logistic regression is a better tool for regional landslide susceptibility analysis in the study area of the Montagne de Reims. The accurate classification of zones with very high and high susceptibility allows delineating zones where viticulturists should be informed and where implementation of precaution measures is needed to secure slope stability.

Hazard-Specific Vulnerability Mapping for Water Security in a Shale Gas Context

NASA Astrophysics Data System (ADS)

Allen, D. M.; Holding, S.; McKoen, Z.

2015-12-01

Northeast British Columbia (NEBC) is estimated to hold large reserves of unconventional natural gas and has experienced rapid growth in shale gas development activities over recent decades. Shale gas development has the potential to impact the quality and quantity of surface and ground water. Robust policies and sound water management are required to protect water security in relation to the water-energy nexus surrounding shale gas development. In this study, hazard-specific vulnerability mapping was conducted across NEBC to identify areas most vulnerable to water quality and quantity deterioration due to shale gas development. Vulnerability represents the combination of a specific hazard threat and the susceptibility of the water system to that threat. Hazard threats (i.e. potential contamination sources and water abstraction) were mapped spatially across the region. The shallow aquifer susceptibility to contamination was characterised using the DRASTIC aquifer vulnerability approach, while the aquifer susceptibility to abstraction was mapped according to aquifer productivity. Surface water susceptibility to contamination was characterised on a watershed basis to describe the propensity for overland flow (i.e. contaminant transport), while watershed discharge estimates were used to assess surface water susceptibility to water abstractions. The spatial distribution of hazard threats and susceptibility were combined to form hazard-specific vulnerability maps for groundwater quality, groundwater quantity, surface water quality and surface water quantity. The vulnerability maps identify priority areas for further research, monitoring and policy development. Priority areas regarding water quality occur where hazard threat (contamination potential) coincide with high aquifer susceptibility or high overland flow potential. Priority areas regarding water quantity occur where demand is estimated to represent a significant proportion of estimated supply. The identification of priority areas allows for characterization of the vulnerability of water security in the region. This vulnerability mapping approach, using the hazard threat and susceptibility indicators, can be applied to other shale gas areas to assess vulnerability to shale gas activities and support water security.

Low signal-to-noise FDEM in-phase data: Practical potential for magnetic susceptibility modelling

NASA Astrophysics Data System (ADS)

Delefortrie, Samuël; Hanssens, Daan; De Smedt, Philippe

2018-05-01

In this paper, we consider the use of land-based frequency-domain electromagnetics (FDEM) for magnetic susceptibility modelling. FDEM data comprises both out-of-phase and in-phase components, which can be related to the electrical conductivity and magnetic susceptibility of the subsurface. Though applying the FDEM method to obtain information on the subsurface conductivity is well established in various domains (e.g. through the low induction number approximation of subsurface apparent conductivity), the potential for susceptibility mapping is often overlooked. Especially given a subsurface with a low magnetite and maghemite content (e.g. most sedimentary environments), it is generally assumed that susceptibility is negligible. Nonetheless, the heterogeneity of the near surface and the impact of anthropogenic disturbances on the soil can cause sufficient variation in susceptibility for it to be detectable in a repeatable way. Unfortunately, it can be challenging to study the potential for susceptibility mapping due to systematic errors, an often poor low signal-to-noise ratio, and the intricacy of correlating in-phase responses with subsurface susceptibility and conductivity. Alongside use of an accurate forward model - accounting for out-of-phase/in-phase coupling - any attempt at relating the in-phase response with subsurface susceptibility requires overcoming instrument-specific limitations that burden the real-world application of FDEM susceptibility mapping. Firstly, the often erratic and drift-sensitive nature of in-phase responses calls for relative data levelling. In addition, a correction for absolute levelling offsets may be equally necessary: ancillary (subsurface) susceptibility data can be used to assess the importance of absolute in-phase calibration though hereby accurate in-situ data is required. To allow assessing the (importance of) in-phase calibration alongside the potential of FDEM data for susceptibility modelling, we consider an experimental test case whereby the in-phase responses of a multi-receiver FDEM instrument are calibrated through downhole susceptibility data. Our results show that, while it is possible to derive approximate susceptibility profiles from FDEM data, robust quantitative analysis hinges on appropriate calibration of the responses.

Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures.

PubMed

Lim, Issel Anne L; Faria, Andreia V; Li, Xu; Hsu, Johnny T C; Airan, Raag D; Mori, Susumu; van Zijl, Peter C M

2013-11-15

The purpose of this paper is to extend the single-subject Eve atlas from Johns Hopkins University, which currently contains diffusion tensor and T1-weighted anatomical maps, by including contrast based on quantitative susceptibility mapping. The new atlas combines a "deep gray matter parcellation map" (DGMPM) derived from a single-subject quantitative susceptibility map with the previously established "white matter parcellation map" (WMPM) from the same subject's T1-weighted and diffusion tensor imaging data into an MNI coordinate map named the "Everything Parcellation Map in Eve Space," also known as the "EvePM." It allows automated segmentation of gray matter and white matter structures. Quantitative susceptibility maps from five healthy male volunteers (30 to 33 years of age) were coregistered to the Eve Atlas with AIR and Large Deformation Diffeomorphic Metric Mapping (LDDMM), and the transformation matrices were applied to the EvePM to produce automated parcellation in subject space. Parcellation accuracy was measured with a kappa analysis for the left and right structures of six deep gray matter regions. For multi-orientation QSM images, the Kappa statistic was 0.85 between automated and manual segmentation, with the inter-rater reproducibility Kappa being 0.89 for the human raters, suggesting "almost perfect" agreement between all segmentation methods. Segmentation seemed slightly more difficult for human raters on single-orientation QSM images, with the Kappa statistic being 0.88 between automated and manual segmentation, and 0.85 and 0.86 between human raters. Overall, this atlas provides a time-efficient tool for automated coregistration and segmentation of quantitative susceptibility data to analyze many regions of interest. These data were used to establish a baseline for normal magnetic susceptibility measurements for over 60 brain structures of 30- to 33-year-old males. Correlating the average susceptibility with age-based iron concentrations in gray matter structures measured by Hallgren and Sourander (1958) allowed interpolation of the average iron concentration of several deep gray matter regions delineated in the EvePM. Copyright © 2013 Elsevier Inc. All rights reserved.

Exposure of young dairy cattle to Mycobacterium avium subsp. paratuberculosis (MAP) through intensive grazing of contaminated pastures in a herd positive for Johne's disease.

PubMed

Fecteau, Marie-Eve; Whitlock, Robert H; Buergelt, Claus D; Sweeney, Raymond W

2010-02-01

This study investigated the susceptibility of 1- to 2-year-old cattle to Mycobacterium avium subsp. paratuberculosis (MAP) on pasture previously grazed by infected cattle. The exposure of yearling cattle to pastures contaminated with MAP resulted in infection with MAP, showing that age resistance to infection can be overcome by pressure of infection.

Integration of data-driven and physically-based methods to assess shallow landslides susceptibility

NASA Astrophysics Data System (ADS)

Lajas, Sara; Oliveira, Sérgio C.; Zêzere, José Luis

2016-04-01

Approaches used to assess shallow landslides susceptibility at the basin scale are conceptually different depending on the use of statistic or deterministic methods. The data-driven methods are sustained in the assumption that the same causes are likely to produce the same effects and for that reason a present/past landslide inventory and a dataset of factors assumed as predisposing factors are crucial for the landslide susceptibility assessment. The physically-based methods are based on a system controlled by physical laws and soil mechanics, where the forces which tend to promote movement are compared with forces that tend to promote resistance to movement. In this case, the evaluation of susceptibility is supported by the calculation of the Factor of safety (FoS), and dependent of the availability of detailed data related with the slope geometry and hydrological and geotechnical properties of the soils and rocks. Within this framework, this work aims to test two hypothesis: (i) although conceptually distinct and based on contrasting procedures, statistic and deterministic methods generate similar shallow landslides susceptibility results regarding the predictive capacity and spatial agreement; and (ii) the integration of the shallow landslides susceptibility maps obtained with data-driven and physically-based methods, for the same study area, generate a more reliable susceptibility model for shallow landslides occurrence. To evaluate these two hypotheses, we select the Information Value data-driven method and the physically-based Infinite Slope model to evaluate shallow landslides in the study area of Monfalim and Louriceira basins (13.9 km2), which is located in the north of Lisbon region (Portugal). The landslide inventory is composed by 111 shallow landslides and was divide in two independent groups based on temporal criteria (age ≤ 1983 and age > 1983): (i) the modelling group (51 cases) was used to define the weights for each predisposing factor (lithology, land use, slope, aspect, curvature, topographic position index and the slope over area ratio) with the Information Value method and was used also to calibrate the strength parameters (cohesion and friction angle) of the different lithological units considered in the Infinity Slope model; and (ii) the validation group (60 cases) was used to independent validate and define the predictive capacity of the shallow landslides susceptibility maps produced with the Information Value method and the Infinite Slope method. The comparison of both landslide susceptibility maps was supported by: (i) the computation of the Receiver Operator Characteristic (ROC) curves; (ii) the calculation of the Area Under the Curve (AUC); and (iii) the evaluation of the spatial agreement between the landslide susceptibility classes. Finally, the susceptibility maps produced with the Information Value and the Infinite Slope methods are integrated into a single landslide susceptibility map based on a set of integration rules define by cross-validation of the susceptibility classes of both maps and analysis of the corresponding contingency table. This work was supported by the FCT - Portuguese Foundation for Science and Technology and is within the framework of the FORLAND Project. Sérgio Oliveira was funded by a postdoctoral grant (SFRH/BPD/85827/2012) from the Portuguese Foundation for Science and Technology (FCT).

Development and testing of method for assessing and mapping agricultural areas susceptible to atrazine leaching in the state of Washington

USGS Publications Warehouse

Voss, Frank D.

2003-01-01

In a joint effort by the Washington State Department of Agriculture, the Washington Department of Ecology, and the U.S. Geological Survey, the Environmental Protection Agency's Pesticide Root Zone Model and a Geographic Information System were used to develop and test a method for screening and mapping the susceptibility of ground water in agricultural areas to pesticide contamination. The objective was to produce a map that would be used by the Washington State Department of Agriculture to allocate resources for monitoring pesticide levels in ground water. The method was tested by producing a map showing susceptibility to leaching of the pesticide atrazine for the Columbia Basin Irrigation Project, which encompasses an area of intensive agriculture in eastern Washington. The reliability of the atrazine map was assessed by using statistical procedures to determine whether the median of the percentage of atrazine simulated to leach below the root zone in wells where atrazine was detected was statistically greater than the median percentage at wells where atrazine was not detected (at or above 0.001 microgram per liter) in 134 wells sampled by the U.S. Geological Survey. A statistical difference in medians was not found when all 134 wells were compared. However, a statistical difference was found in medians for two subsets of the 134 wells that were used in land-use studies (studies examining the quality of ground water beneath specific crops). The statistical results from wells from the land-use studies indicate that the model potentially can be used to map the relative susceptibility of agricultural areas to atrazine leaching. However, the distinction between areas of high and low susceptibility may not yet be sufficient to use the method for allocating resources to monitor water quality. Several options are offered for improving the reliability of future simulations.

Joint eigenvector estimation from mutually anisotropic tensors improves susceptibility tensor imaging of the brain, kidney, and heart.

PubMed

Dibb, Russell; Liu, Chunlei

2017-06-01

To develop a susceptibility-based MRI technique for probing microstructure and fiber architecture of magnetically anisotropic tissues-such as central nervous system white matter, renal tubules, and myocardial fibers-in three dimensions using susceptibility tensor imaging (STI) tools. STI can probe tissue microstructure, but is limited by reconstruction artifacts because of absent phase information outside the tissue and noise. STI accuracy may be improved by estimating a joint eigenvector from mutually anisotropic susceptibility and relaxation tensors. Gradient-recalled echo image data were simulated using a numerical phantom and acquired from the ex vivo mouse brain, kidney, and heart. Susceptibility tensor data were reconstructed using STI, regularized STI, and the proposed algorithm of mutually anisotropic and joint eigenvector STI (MAJESTI). Fiber map and tractography results from each technique were compared with diffusion tensor data. MAJESTI reduced the estimated susceptibility tensor orientation error by 30% in the phantom, 36% in brain white matter, 40% in the inner medulla of the kidney, and 45% in myocardium. This improved the continuity and consistency of susceptibility-based fiber tractography in each tissue. MAJESTI estimation of the susceptibility tensors yields lower orientation errors for susceptibility-based fiber mapping and tractography in the intact brain, kidney, and heart. Magn Reson Med 77:2331-2346, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Rainfall thresholds and susceptibility mapping for shallow landslides and debris flows in Scotland

NASA Astrophysics Data System (ADS)

Postance, Benjamin; Hillier, John; Dijkstra, Tom; Dixon, Neil

2017-04-01

Shallow translational slides and debris flows (hereafter 'landslides') pose a significant threat to life and cause significant annual economic impacts (e.g. by damage and disruption of infrastructure). The focus of this research is on the definition of objective rainfall thresholds using a weather radar system and landslide susceptibility mapping. In the study area Scotland, an inventory of 75 known landslides was used for the period 2003 to 2016. First, the effect of using different rain records (i.e. time series length) on two threshold selection techniques in receiver operating characteristic (ROC) analysis was evaluated. The results show that thresholds selected by 'Threat Score' (minimising false alarms) are sensitive to rain record length and which is not routinely considered, whereas thresholds selected using 'Optimal Point' (minimising failed alarms) are not; therefore these may be suited to establishing lower limit thresholds and be of interest to those developing early warning systems. Robust thresholds are found for combinations of normalised rain duration and accumulation at 1 and 12 day's antecedence respectively; these are normalised using the rainy-day normal and an equivalent measure for rain intensity. This research indicates that, in Scotland, rain accumulation provides a better indicator than rain intensity and that landslides may be generated by threshold conditions lower than previously thought. Second, a landslide susceptibility map is constructed using a cross-validated logistic regression model. A novel element of the approach is that landslide susceptibility is calculated for individual hillslope sections. The developed thresholds and susceptibility map are combined to assess potential hazards and impacts posed to the national highway network in Scotland.

Progress in national-scale landslide susceptibility mapping in Romania using a combined statistical-heuristical approach

NASA Astrophysics Data System (ADS)

Bălteanu, Dan; Micu, Mihai; Malet, Jean-Philippe; Jurchescu, Marta; Sima, Mihaela; Kucsicsa, Gheorghe; Dumitrică, Cristina; Petrea, Dănuţ; Mărgărint, Ciprian; Bilaşco, Ştefan; Văcăreanu, Radu; Georgescu, Sever; Senzaconi, Francisc

2017-04-01

Landslide processes represent a very widespread geohazard in Romania, affecting mainly the hilly and plateau regions as well as the mountain sectors developed on flysch formations. Two main projects provided the framework for improving the existing national landslide susceptibility map (Bălteanu et al. 2010): the ELSUS (Pan-European and nation-wide landslide susceptibility assessment, EC-CERG) and the RO-RISK (Disaster Risk Evaluation at National Level, ESF-POCA) projects. The latter one, a flagship project aiming at strengthening risk prevention and management in Romania, focused on a national-level evaluation of the main risks in the country including landslides. The strategy for modeling landslide susceptibility was designed based on the experience gained from continental and national level assessments conducted in the frame of the International Programme on Landslides (IPL) project IPL-162, the European Landslides Expert Group - JRC and the ELSUS project. The newly proposed landslide susceptibility model used as input a reduced set of landslide conditioning factor maps available at scales of 1:100,000 - 1:200,000 and consisting of lithology, slope angle and land cover. The input data was further differentiated for specific natural environments, defined here as morpho-structural units in order to incorporate differences induced by elevation (vertical climatic zonation), morpho-structure as well as neotectonic features. In order to best discern the specific landslide conditioning elements, the analysis has been carried out for one single process category, namely slides. The existence of a landslide inventory covering the whole country's territory ( 30,000 records, Micu et al. 2014), although affected by incompleteness and lack of homogeneity, allowed for the application of a semi-quantitative, mixed statistical-heuristical approach having the advantage of combining the objectivity of statistics with expert-knowledge in calibrating class and factor weights. The maps obtained for the different units were subjected to evaluation and validation using both expert judgment and two additional landslide inventories with national coverage. Expert evaluations were provided for several parts of the country, where possible also using available regional zonations, and derived knowledge was subsequently used for map improvements. The external landslide datasets allowed for validation of the maps through prediction-rate curves (PRC). An improved national landslide susceptibility map of Romania (100 m resolution) resulted from merging the various unit maps and classifying them according to the PRC-thresholds. The final map reveals good performance for most areas. Finally, improvements compared to the previous version of the national map as well as model limitations and possible enhancement requirements are discussed. This study is part of the RO-RISK project (2016) coordinated by the Romanian General Inspectorate for Emergency Situations (IGSU) and supported by the European Social Fund through the Operational Programme for Administrative Capacity (POCA).

Mapping flood and flooding potential indices: a methodological approach to identifying areas susceptible to flood and flooding risk. Case study: the Prahova catchment (Romania)

NASA Astrophysics Data System (ADS)

Zaharia, Liliana; Costache, Romulus; Prăvălie, Remus; Ioana-Toroimac, Gabriela

2017-04-01

Given that floods continue to cause yearly significant worldwide human and material damages, flood risk mitigation is a key issue and a permanent challenge in developing policies and strategies at various spatial scales. Therefore, a basic phase is elaborating hazard and flood risk maps, documents which are an essential support for flood risk management. The aim of this paper is to develop an approach that allows for the identification of flash-flood and flood-prone susceptible areas based on computing and mapping of two indices: FFPI (Flash-Flood Potential Index) and FPI (Flooding Potential Index). These indices are obtained by integrating in a GIS environment several geographical variables which control runoff (in the case of the FFPI) and favour flooding (in the case of the FPI). The methodology was applied in the upper (mountainous) and middle (hilly) catchment of the Prahova River, a densely populated and socioeconomically well-developed area which has been affected repeatedly by water-related hazards over the past decades. The resulting maps showing the spatialization of the FFPI and FPI allow for the identification of areas with high susceptibility to flashfloods and flooding. This approach can provide useful mapped information, especially for areas (generally large) where there are no flood/hazard risk maps. Moreover, the FFPI and FPI maps can constitute a preliminary step for flood risk and vulnerability assessment.

Spatio Temporal Detection and Virtual Mapping of Landslide Using High-Resolution Airborne Laser Altimetry (lidar) in Densely Vegetated Areas of Tropics

NASA Astrophysics Data System (ADS)

Bibi, T.; Azahari Razak, K.; Rahman, A. Abdul; Latif, A.

2017-10-01

Landslides are an inescapable natural disaster, resulting in massive social, environmental and economic impacts all over the world. The tropical, mountainous landscape in generally all over Malaysia especially in eastern peninsula (Borneo) is highly susceptible to landslides because of heavy rainfall and tectonic disturbances. The purpose of the Landslide hazard mapping is to identify the hazardous regions for the execution of mitigation plans which can reduce the loss of life and property from future landslide incidences. Currently, the Malaysian research bodies e.g. academic institutions and government agencies are trying to develop a landslide hazard and risk database for susceptible areas to backing the prevention, mitigation, and evacuation plan. However, there is a lack of devotion towards landslide inventory mapping as an elementary input of landslide susceptibility, hazard and risk mapping. The developing techniques based on remote sensing technologies (satellite, terrestrial and airborne) are promising techniques to accelerate the production of landslide maps, shrinking the time and resources essential for their compilation and orderly updates. The aim of the study is to provide a better perception regarding the use of virtual mapping of landslides with the help of LiDAR technology. The focus of the study is spatio temporal detection and virtual mapping of landslide inventory via visualization and interpretation of very high-resolution data (VHR) in forested terrain of Mesilau river, Kundasang. However, to cope with the challenges of virtual inventory mapping on in forested terrain high resolution LiDAR derivatives are used. This study specifies that the airborne LiDAR technology can be an effective tool for mapping landslide inventories in a complex climatic and geological conditions, and a quick way of mapping regional hazards in the tropics.

Map showing landslide susceptibility in Prince Georges County, Maryland

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pomeroy, J.S.

1989-01-01

Prince Georges County was identified during a statewide investigation of landslide susceptibility (MF-2048) as the county with the most serious slope-stability problems. This map uses a ranking system ranging from 1 (nil to very low susceptibility) to 4 (moderate to severe susceptibility). Geologic factors and precipitation are major elements in the initiation of landslides in the county. The Potomac Group and the Marlboro Clay are the most slideprone units. This map should enable users to make a rapid, generalized evaluation of the potential for mass movement. Planners, engineers, soil scientists, geologist, university faculty, and elected officials should find it usefulmore » in the assessment of slope hazards for county-wide analyses.« less

Evaluating the Variations in the Flood Susceptibility Maps Accuracies due to the Alterations in the Type and Extent of the Flood Inventory

NASA Astrophysics Data System (ADS)

Tehrany, M. Sh.; Jones, S.

2017-10-01

This paper explores the influence of the extent and density of the inventory data on the final outcomes. This study aimed to examine the impact of different formats and extents of the flood inventory data on the final susceptibility map. An extreme 2011 Brisbane flood event was used as the case study. LR model was applied using polygon and point formats of the inventory data. Random points of 1000, 700, 500, 300, 100 and 50 were selected and susceptibility mapping was undertaken using each group of random points. To perform the modelling Logistic Regression (LR) method was selected as it is a very well-known algorithm in natural hazard modelling due to its easily understandable, rapid processing time and accurate measurement approach. The resultant maps were assessed visually and statistically using Area under Curve (AUC) method. The prediction rates measured for susceptibility maps produced by polygon, 1000, 700, 500, 300, 100 and 50 random points were 63 %, 76 %, 88 %, 80 %, 74 %, 71 % and 65 % respectively. Evidently, using the polygon format of the inventory data didn't lead to the reasonable outcomes. In the case of random points, raising the number of points consequently increased the prediction rates, except for 1000 points. Hence, the minimum and maximum thresholds for the extent of the inventory must be set prior to the analysis. It is concluded that the extent and format of the inventory data are also two of the influential components in the precision of the modelling.

Environmental magnetic methods for detecting and mapping contaminated sediments in lakes

NASA Astrophysics Data System (ADS)

Boyce, J. I.

2009-05-01

The remediation of contaminated sediments is an urgent environmental priority in the Great Lakes and requires detailed mapping of impacted sediment layer thickness, areal distribution and pollutant levels. Magnetic property measurements of sediment cores from two heavily polluted basins in Lake Ontario (Hamilton Harbour, Frenchman's Bay) show that concentrations of hydrocarbons (PAH) and a number of heavy metals (Pb, As, Ni, Cu, Cr, Zn, Cd, Fe) are strongly correlated with magnetic susceptibility. The magnetic susceptibility contrast between the contaminated sediment and underlying 'pre-colonial' sediments is sufficient to generate a total field anomaly (ca. 2-20 nT) that can be measured with a magnetometer towed above the lake bed. Systematic magnetic surveying (550 line km) of Hamilton Harbour using a towed marine magnetometer clearly identifies a number of well-defined magnetic anomalies that coincide with known accumulations of contaminated lake sediment. When calibrated against in-situ magnetic property measurements, the modeled apparent susceptibility from magnetic survey results can be used to classify the relative contaminant impact levels. The results demonstrate the potential of magnetic property measurements for rapid reconnaissance mapping of large areas of bottom contamination prior to detailed coring and sediment remediation.

Probabilistic landslide susceptibility mapping in the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides

NASA Astrophysics Data System (ADS)

Lee, Saro; Dan, Nguyen Tu

2005-09-01

This study evaluates the susceptibility of landslides in the Lai Chau province of Vietnam using Geographic Information System (GIS) and remote sensing data to focus on the relationship between tectonic fractures and landslides. Landslide locations were identified from aerial photographs and field surveys. Topographic, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS data and image-processing techniques. A scheme of the tectonic fracturing of crust in the Lai Chau region was established. Lai Chau was identified as a region with many crustal fractures, where the grade of tectonic fracture is closely related to landslide occurrence. The influencing factors of landslide occurrence were: distance from a tectonic fracture, slope, aspect, curvature, soil, and vegetative land cover. Landslide prone areas were analyzed and mapped using the landslide occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using landslide location data and showed 83.47% prediction accuracy. That emphasized a strong relationship between the susceptibility map and the existing landslide location data. The results of this study can form a basis stable development and land use planning for the region.

Multi-Collinearity Based Model Selection for Landslide Susceptibility Mapping: A Case Study from Ulus District of Karabuk, Turkey

NASA Astrophysics Data System (ADS)

Sahin, E. K.; Colkesen, I., , Dr; Kavzoglu, T.

2017-12-01

Identification of localities prone to landslide areas plays an important role for emergency planning, disaster management and recovery planning. Due to its great importance for disaster management, producing accurate and up-to-date landslide susceptibility maps is essential for hazard mitigation purpose and regional planning. The main objective of the present study was to apply multi-collinearity based model selection approach for the production of a landslide susceptibility map of Ulus district of Karabuk, Turkey. It is a fact that data do not contain enough information to describe the problem under consideration when the factors are highly correlated with each other. In such cases, choosing a subset of the original features will often lead to better performance. This paper presents multi-collinearity based model selection approach to deal with the high correlation within the dataset. Two collinearity diagnostic factors (Tolerance (TOL) and the Variance Inflation Factor (VIF)) are commonly used to identify multi-collinearity. Values of VIF that exceed 10.0 and TOL values less than 1.0 are often regarded as indicating multi-collinearity. Five causative factors (slope length, curvature, plan curvature, profile curvature and topographical roughness index) were found highly correlated with each other among 15 factors available for the study area. As a result, the five correlated factors were removed from the model estimation, and performances of the models including the remaining 10 factors (aspect, drainage density, elevation, lithology, land use/land cover, NDVI, slope, sediment transport index, topographical position index and topographical wetness index) were evaluated using logistic regression. The performance of prediction model constructed with 10 factors was compared to that of 15-factor model. The prediction performance of two susceptibility maps was evaluated by overall accuracy and the area under the ROC curve (AUC) values. Results showed that overall accuracy and AUC was calculated as 77.15% and 96.62% respectively for the model with 10 selected factors whilst they were estimated as 73.45% and 89.45% respectively for the model with all factors. It is clear that the multi-collinearity based model outperformed the conventional model in the mapping of landslide susceptibility.

New techniques on oil spill modelling applied in the Eastern Mediterranean sea

NASA Astrophysics Data System (ADS)

Zodiatis, George; Kokinou, Eleni; Alves, Tiago; Lardner, Robin

2016-04-01

Small or large oil spills resulting from accidents on oil and gas platforms or due to the maritime traffic comprise a major environmental threat for all marine and coastal systems, and they are responsible for huge economic losses concerning the human infrastructures and the tourism. This work aims at presenting the integration of oil-spill model, bathymetric, meteorological, oceanographic, geomorphological and geological data to assess the impact of oil spills in maritime regions such as bays, as well as in the open sea, carried out in the Eastern Mediterranean Sea within the frame of NEREIDs, MEDESS-4MS and RAOP-Med EU projects. The MEDSLIK oil spill predictions are successfully combined with bathymetric analyses, the shoreline susceptibility and hazard mapping to predict the oil slick trajectories and the extend of the coastal areas affected. Based on MEDSLIK results, oil spill spreading and dispersion scenarios are produced both for non-mitigated and mitigated oil spills. MEDSLIK model considers three response combating methods of floating oil spills: a) mechanical recovery using skimmers or similar mechanisms; b) destruction by fire, c) use of dispersants or other bio-chemical means and deployment of booms. Shoreline susceptibility map can be compiled for the study areas based on the Environmental Susceptibility Index. The ESI classification considers a range of values between 1 and 9, with level 1 (ESI 1) representing areas of low susceptibility, impermeable to oil spilt during accidents, such as linear shorelines with rocky cliffs. In contrast, ESI 9 shores are highly vulnerable, and often coincide with natural reserves and special protected areas. Additionally, hazard maps of the maritime and coastal areas, possibly exposed to the danger on an oil spill, evaluate and categorize the hazard in levels from low to very high. This is important because a) Prior to an oil spill accident, hazard and shoreline susceptibility maps are made available to design preparedness and prevention plans in an effective way, b) After an oil spill accident, oil spill predictions can be combined with hazard maps to provide information on the oil spill dispersion and their impacts. This way, prevention plans can be directly modified at any time after the accident.

Landslide susceptibility mapping in the coastal region in the State of São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Alvala, R. C.; Camarinha, P. I.; Canavesi, V.

2013-05-01

The exposure of populations in risk areas is a matter of global concern, because it is a determining factor for the natural disasters occurrences. Furthermore, it has also been observed an intensification of extreme hydrometeorological events that has triggered disasters in various parts of the globe, further increasing the need for monitoring and alerting for natural disasters, aiming the safeguarding of life and minimize economic losses. Accordingly, different methodologies for risk assessment have been proposed, focusing on the specific natural hazards. Particularly for Brazil, which has economic axis of development in the regions near the coast, it is common to observe the process of urbanization advancing on steep slopes of the mountain regions. This characteristic causes the population exposure to the natural hazards related to the mass movements, which the landslides stood out as the cause of many deaths and economic losses every year. Thus, prior to risk analysis (when human occupation intersect with natural hazard), it is essential to analyze the susceptibility, which reflects the physical and environmental conditions that trigger for such phenomena. However, this task becomes a major challenge due to the difficulty of finding databases with good quality. In this context, this paper presents a methodology based only on spatial information in the public domain, integrated into a Geographic Information System free, in order to analyze the landslides susceptibility. In a first effort, we evaluated four counties of Southeastern Brazil - Santos, Cubatão, Caraguatatuba and Ubatuba - located in a region that includes the rugged reliefs of Serra do Mar and the transition to the coastal region, that have historic of disasters related. It is noteworthy that the methodology takes into account many variables that was weighted and crossed by Fuzzy Gamma technique, such as: topography (horizontal and vertical curvature of the slopes), geology, geomorphology, slope, land use and pedology. As a result, we obtain 5 susceptibility classes: very low, low, medium, high and very high. To validate the methodology, there was overlapped the Landslides Susceptibility Map with real risk areas previously mapped, provided by the National Centre for Monitoring and Alert of Natural Disasters. This step is important especially to assess the methodology adherence to evaluate the classes that was mapped with high and very high susceptibility. The preliminary results indicate that over 70% of the the mapped risks areas are located into the classes more susceptible. We observed small inconsistencies that are related with spatial displacement of the various databases considered, which has different resolutions and scales. Therefore, the results indicated that the methodology is robust and showed the high vulnerability of the counties analyzed, which further highlights that the landslides susceptibility should be monitored carefully by the decision makers in order to prevent and minimize the natural disasters impact, so that provide better territorial planning.

Positive Contrast Visualization of Nitinol Devices using Susceptibility Gradient Mapping

PubMed Central

Vonken, Evert-jan P.A.; Schär, Michael; Stuber, Matthias

2008-01-01

MRI visualization of devices is traditionally based on the signal loss due to T2* effects originating from the local susceptibility differences. To visualize nitinol devices with positive contrast a recently introduced post processing method is adapted to map the induced susceptibility gradients. This method operates on regular gradient echo MR images and maps the shift in k-space in a (small) neighborhood of every voxel by Fourier analysis followed by a center of mass calculation. The quantitative map of the local shifts generates the positive contrast image of the devices, while areas without susceptibility gradients render a background with noise only. The positive signal response of this method depends only on the choice of the voxel neighborhood size. The properties of the method are explained and the visualization of a nitinol wire and two stents are shown for illustration. PMID:18727096

Cerebral Microbleeds: Burden Assessment by Using Quantitative Susceptibility Mapping

PubMed Central

Liu, Tian; Surapaneni, Krishna; Lou, Min; Cheng, Liuquan; Spincemaille, Pascal

2012-01-01

Purpose: To assess quantitative susceptibility mapping (QSM) for reducing the inconsistency of standard magnetic resonance (MR) imaging sequences in measurements of cerebral microbleed burden. Materials and Methods: This retrospective study was HIPAA compliant and institutional review board approved. Ten patients (5.6%) were selected from among 178 consecutive patients suspected of having experienced a stroke who were imaged with a multiecho gradient-echo sequence at 3.0 T and who had cerebral microbleeds on T2*-weighted images. QSM was performed for various ranges of echo time by using both the magnitude and phase components in the morphology-enabled dipole inversion method. Cerebral microbleed size was measured by two neuroradiologists on QSM images, T2*-weighted images, susceptibility-weighted (SW) images, and R2* maps calculated by using different echo times. The sum of susceptibility over a region containing a cerebral microbleed was also estimated on QSM images as its total susceptibility. Measurement differences were assessed by using the Student t test and the F test; P < .05 was considered to indicate a statistically significant difference. Results: When echo time was increased from approximately 20 to 40 msec, the measured cerebral microbleed volume increased by mean factors of 1.49 ± 0.86 (standard deviation), 1.64 ± 0.84, 2.30 ± 1.20, and 2.30 ± 1.19 for QSM, R2*, T2*-weighted, and SW images, respectively (P < .01). However, the measured total susceptibility with QSM did not show significant change over echo time (P = .31), and the variation was significantly smaller than any of the volume increases (P < .01 for each). Conclusion: The total susceptibility of a cerebral microbleed measured by using QSM is a physical property that is independent of echo time. © RSNA, 2011 PMID:22056688

Climate change induced lanslide hazard mapping over Greece- A case study in Pelion Mountain (SE Thessaly, Central Greece)

NASA Astrophysics Data System (ADS)

Angelitsa, Varvara; Loupasakis, Constantinos; Anagnwstopoulou, Christina

2015-04-01

Landslides, as a major type of geological hazard, represent one of the natural events that occur most frequently worldwide after hydro-meteorological events. Landslides occur when the stability of a slope changes due to a number of factors, such as the steep terrain and prolonged precipitation. Identification of landslides and compilation of landslide susceptibility, hazard and risk maps are very important issues for the public authorities providing substantial information regarding, the strategic planning and management of the land-use. Although landslides cannot be predicted accurately, many attempts have been made to compile these maps. Important factors for the the compilation of reliable maps are the quality and the amount of available data and the selection of the best method for the analysis. Numerous studies and publications providing landslide susceptibility,hazard and risk maps, for different regions of Greece, have completed up to now. Their common characteristic is that they are static, taking into account parameters like geology, mean annual precipitaion, slope, aspect, distance from roads, faults and drainage network, soil capability, land use etc., without introducing the dimension of time. The current study focuses on the Pelion Mountain, which is located at the southeastern part of Thessaly in Central Greece; aiming to compile "dynamic" susceptibility and hazard maps depending on climate changes. For this purpose, past and future precipipation data from regional climate models (RCMs) datasets are introduced as input parameters for the compilation of "dynamic" landslide hazard maps. Moreover, land motion mapping data produced by Persistent Scatterer Interferometry (PSI) are used for the validation of the landslide occurrence during the period from June 1992 to December 2003 and as a result for the calibration of the mapping procedure. The PSI data can be applied at a regional scale as support for land motion mapping and at local scale for the monitoring of single well-known ground motion event. The PSI data were produced within the framework of the Terrafirma project. Terrafirma is a pan- European ground motion information service focused on seismic risk, flood defense and costal lowland subsidence,inactive mines and hydrogeological risks. The produced maps provided substantial information for the land use planning and the civil protection of an area presenting excelent natural beauty and numerous preservable trtaditional villages. Keywords: landslide, psi technique, regional climate models, lanslide susceptibility maps, Greece

Modeling of natural risks in GIS, decision support in the Civil Protection and Emergency Planning

NASA Astrophysics Data System (ADS)

Santos, M.; Martins, L.; Moreira, S.; Costa, A.; Matos, F.; Teixeira, M.; Bateira, C.

2012-04-01

The assessment of natural hazards in Civil Protection is essential in the prevention and mitigation of emergency situations. This paper presents the results of the development of mapping susceptibility to landslides, floods, forest fires and soil erosion, using GIS (Geographic Information System) tools in two municipalities - Santo Tirso and Trofa - in the district of Oporto, in the northwest of Portugal. The mapping of natural hazards fits in the legislative plan of the Municipal Civil Protection (Law No. 65/2007 of 12 November) and it provides the key elements to planning and preparing an appropriate response in case some of the processes / phenomena occur, thus optimizing the procedures for protection and relief provided by the Municipal Civil Protection Service. Susceptibility mapping to landslides, floods, forest fires and soil erosion was performed with GIS tools resources. The methodology used to compile the mapping of landslides, forest fires and soil erosion was based on the modeling of different conditioning factors and validated with field work and event log. The mapping of susceptibility to floods and flooding was developed through mathematical parameters (statistical, hydrologic and hydraulic), supported by field work and the recognition of individual characteristics of each sector analysis and subsequently analyzed in a GIS environment The mapping proposal was made in 1:5000 scale which allows not only the identification of large sets affected by the spatial dynamics of the processes / phenomena, but also a more detailed analysis, especially when combined with geographic information systems (GIS) thus allowing to study more specific situations that require a quick response. The maps developed in this study are fundamental to the understanding, prediction and prevention of susceptibility and risks present in the municipalities, being a valuable tool in the process of Emergency Planning, since it identifies priority areas of intervention for farther detail analysis, promote and safeguard mechanisms to prevent injury and it anticipates the possibility of potential interventions that can minimize the risk.

Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures

PubMed Central

Lim, Issel Anne L.; Faria, Andreia V.; Li, Xu; Hsu, Johnny T.C.; Airan, Raag D.; Mori, Susumu; van Zijl, Peter C. M.

2013-01-01

The purpose of this paper is to extend the single-subject Eve atlas from Johns Hopkins University, which currently contains diffusion tensor and T1-weighted anatomical maps, by including contrast based on quantitative susceptibility mapping. The new atlas combines a “deep gray matter parcellation map” (DGMPM) derived from a single-subject quantitative susceptibility map with the previously established “white matter parcellation map” (WMPM) from the same subject’s T1-weighted and diffusion tensor imaging data into an MNI coordinate map named the “Everything Parcellation Map in Eve Space,” also known as the “EvePM.” It allows automated segmentation of gray matter and white matter structures. Quantitative susceptibility maps from five healthy male volunteers (30 to 33 years of age) were coregistered to the Eve Atlas with AIR and Large Deformation Diffeomorphic Metric Mapping (LDDMM), and the transformation matrices were applied to the EvePM to produce automated parcellation in subject space. Parcellation accuracy was measured with a kappa analysis for the left and right structures of six deep gray matter regions. For multi-orientation QSM images, the Kappa statistic was 0.85 between automated and manual segmentation, with the inter-rater reproducibility Kappa being 0.89 for the human raters, suggesting “almost perfect” agreement between all segmentation methods. Segmentation seemed slightly more difficult for human raters on single-orientation QSM images, with the Kappa statistic being 0.88 between automated and manual segmentation, and 0.85 and 0.86 between human raters. Overall, this atlas provides a time-efficient tool for automated coregistration and segmentation of quantitative susceptibility data to analyze many regions of interest. These data were used to establish a baseline for normal magnetic susceptibility measurements for over 60 brain structures of 30- to 33-year-old males. Correlating the average susceptibility with age-based iron concentrations in gray matter structures measured by Hallgren and Sourander (1958) allowed interpolation of the average iron concentration of several deep gray matter regions delineated in the EvePM. PMID:23769915

Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy)

NASA Astrophysics Data System (ADS)

Angileri, Silvia Eleonora; Conoscenti, Christian; Hochschild, Volker; Märker, Michael; Rotigliano, Edoardo; Agnesi, Valerio

2016-06-01

Soil erosion by water constitutes a serious problem affecting various countries. In the last few years, a number of studies have adopted statistical approaches for erosion susceptibility zonation. In this study, the Stochastic Gradient Treeboost (SGT) was tested as a multivariate statistical tool for exploring, analyzing and predicting the spatial occurrence of rill-interrill erosion and gully erosion. This technique implements the stochastic gradient boosting algorithm with a tree-based method. The study area is a 9.5 km2 river catchment located in central-northern Sicily (Italy), where water erosion processes are prevalent, and affect the agricultural productivity of local communities. In order to model soil erosion by water, the spatial distribution of landforms due to rill-interrill and gully erosion was mapped and 12 environmental variables were selected as predictors. Four calibration and four validation subsets were obtained by randomly extracting sets of negative cases, both for rill-interrill erosion and gully erosion models. The results of validation, based on receiving operating characteristic (ROC) curves, showed excellent to outstanding accuracies of the models, and thus a high prediction skill. Moreover, SGT allowed us to explore the relationships between erosion landforms and predictors. A different suite of predictor variables was found to be important for the two models. Elevation, aspect, landform classification and land-use are the main controlling factors for rill-interrill erosion, whilst the stream power index, plan curvature and the topographic wetness index were the most important independent variables for gullies. Finally, an ROC plot analysis made it possible to define a threshold value to classify cells according to the presence/absence of the two erosion processes. Hence, by heuristically combining the resulting rill-interrill erosion and gully erosion susceptibility maps, an integrated water erosion susceptibility map was created. The adopted method offers the advantages of an objective and repeatable procedure, whose result is useful for local administrators to identify the areas that are most susceptible to water erosion and best allocate resources for soil conservation strategies.

Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI.

PubMed

Liu, Tian; Spincemaille, Pascal; de Rochefort, Ludovic; Kressler, Bryan; Wang, Yi

2009-01-01

Magnetic susceptibility differs among tissues based on their contents of iron, calcium, contrast agent, and other molecular compositions. Susceptibility modifies the magnetic field detected in the MR signal phase. The determination of an arbitrary susceptibility distribution from the induced field shifts is a challenging, ill-posed inverse problem. A method called "calculation of susceptibility through multiple orientation sampling" (COSMOS) is proposed to stabilize this inverse problem. The field created by the susceptibility distribution is sampled at multiple orientations with respect to the polarization field, B(0), and the susceptibility map is reconstructed by weighted linear least squares to account for field noise and the signal void region. Numerical simulations and phantom and in vitro imaging validations demonstrated that COSMOS is a stable and precise approach to quantify a susceptibility distribution using MRI.

Developing a scientific procedure for community based hazard mapping and risk mitigation

NASA Astrophysics Data System (ADS)

Verrier, M.

2011-12-01

As an international exchange student from the Geological Sciences Department at San Diego State University (SDSU), I joined the KKN-PPM program at Universitas Gadjah Mada (UGM), Yogyakarta, Indonesia, in July 2011 for 12 days (July 4th to July 16th) of its two month duration (July 4th to August 25th). The KKN-PPM group I was attached was designated 154 and was focused in Plosorejo Village, Karanganyar, Kerjo, Central Java, Indonesia. The mission of KKN-PPM 154 was to survey Plosorejo village for existing landslides, to generate a simple hazard susceptibility map that can be understood by local villagers, and then to begin dissemination of that map into the community. To generate our susceptibility map we first conducted a geological survey of the existing landslides in the field study area, with a focus on determining landslide triggers and gauging areas for susceptibility for future landslides. The methods for gauging susceptibility included lithological observation, the presence of linear cracking, visible loss of structural integrity in structures such as villager homes, as well as collaboration with local residents and with the local rescue and response team. There were three color distinctions used in representing susceptibility which were green, where there is no immediate danger of landslide damage; orange, where transportation routes are at risk of being disrupted by landslides; and red, where imminent landslide potential puts a home in direct danger. The landslide inventory and susceptibility data was compiled into digital mediums such as CorelDraw, ArcGIS and Google Earth. Once a technical map was generated, we presented it to the village leadership for confirmation and modification based on their experience. Finally, we began to use the technical susceptibility map to draft evacuation routes and meeting points in the event of landslides, as well as simple susceptibility maps that can be understood and utilized by local villagers. Landslide mitigation projects that are being conducted alongside the community hazard map include marking evacuation routes with painted bamboo signs, creating a meaningful landslide awareness mural, and installing simple early warning systems that detect land movement and alert residents that evacuation routes should be used. KKN-PPM is scheduled to continue until August 25th, 2011. In the future, research will be done into using the model for community based hazard mapping outlined here in the Geological Sciences Department at SDSU to increase georisk awareness and improve mitigation of landslides in local areas of need such as Tijuana, Mexico.

Fine mapping implicates two immunity genes in larval resistance to the honey bee brood fungal disease, Chalkbrood

USDA-ARS?s Scientific Manuscript database

Chalkbrood infection of honey bee (Apis mellifera) brood by the fungus Ascosphaera apis results in fatal encapsulation of susceptible larvae with a mycelial coat. Recent QTL analysis indicates that some level of physiological resistance exists in individual larvae. We performed a fine mapping anal...

Joint multi-population analysis for genetic linkage of bipolar disorder or "wellness" to chromosome 4p.

PubMed

Visscher, P M; Haley, C S; Ewald, H; Mors, O; Egeland, J; Thiel, B; Ginns, E; Muir, W; Blackwood, D H

2005-02-05

To test the hypothesis that the same genetic loci confer susceptibility to, or protection from, disease in different populations, and that a combined analysis would improve the map resolution of a common susceptibility locus, we analyzed data from three studies that had reported linkage to bipolar disorder in a small region on chromosome 4p. Data sets comprised phenotypic information and genetic marker data on Scottish, Danish, and USA extended pedigrees. Across the three data sets, 913 individuals appeared in the pedigrees, 462 were classified, either as unaffected (323) or affected (139) with unipolar or bipolar disorder. A consensus linkage map was created from 14 microsatellite markers in a 33 cM region. Phenotypic and genetic data were analyzed using a variance component (VC) and allele sharing method. All previously reported elevated test statistics in the region were confirmed with one or both analysis methods, indicating the presence of one or more susceptibility genes to bipolar disorder in the three populations in the studied chromosome segment. When the results from both the VC and allele sharing method were considered, there was strong evidence for a susceptibility locus in the data from Scotland, some evidence in the data from Denmark and relatively less evidence in the data from the USA. The test statistics from the Scottish data set dominated the test statistics from the other studies, and no improved map resolution for a putative genetic locus underlying susceptibility in all three studies was obtained. Studies reporting linkage to the same region require careful scrutiny and preferably joint or meta analysis on the same basis in order to ensure that the results are truly comparable. (c) 2004 Wiley-Liss, Inc.

A new concept in seismic landslide hazard analysis for practical application

NASA Astrophysics Data System (ADS)

Lee, Chyi-Tyi

2017-04-01

A seismic landslide hazard model could be constructed using deterministic approach (Jibson et al., 2000) or statistical approach (Lee, 2014). Both approaches got landslide spatial probability under a certain return-period earthquake. In the statistical approach, our recent study found that there are common patterns among different landslide susceptibility models of the same region. The common susceptibility could reflect relative stability of slopes at a region; higher susceptibility indicates lower stability. Using the common susceptibility together with an earthquake event landslide inventory and a map of topographically corrected Arias intensity, we can build the relationship among probability of failure, Arias intensity and the susceptibility. This relationship can immediately be used to construct a seismic landslide hazard map for the region that the empirical relationship built. If the common susceptibility model is further normalized and the empirical relationship built with normalized susceptibility, then the empirical relationship may be practically applied to different region with similar tectonic environments and climate conditions. This could be feasible, when a region has no existing earthquake-induce landslide data to train the susceptibility model and to build the relationship. It is worth mentioning that a rain-induced landslide susceptibility model has common pattern similar to earthquake-induced landslide susceptibility in the same region, and is usable to build the relationship with an earthquake event landslide inventory and a map of Arias intensity. These will be introduced with examples in the meeting.

Effects of tissue susceptibility on brain temperature mapping.

PubMed

Maudsley, Andrew A; Goryawala, Mohammed Z; Sheriff, Sulaiman

2017-02-01

A method for mapping of temperature over a large volume of the brain using volumetric proton MR spectroscopic imaging has been implemented and applied to 150 normal subjects. Magnetic susceptibility-induced frequency shifts in gray- and white-matter regions were measured and included as a correction in the temperature mapping calculation. Additional sources of magnetic susceptibility variations of the individual metabolite resonance frequencies were also observed that reflect the cellular-level organization of the brain metabolites, with the most notable differences being attributed to changes of the N-Acetylaspartate resonance frequency that reflect the intra-axonal distribution and orientation of the white-matter tracts with respect to the applied magnetic field. These metabolite-specific susceptibility effects are also shown to change with age. Results indicate no change of apparent brain temperature with age from 18 to 84 years old, with a trend for increased brain temperature throughout the cerebrum in females relative for males on the order of 0.1°C; slightly increased temperatures in the left hemisphere relative to the right; and a lower temperature of 0.3°C in the cerebellum relative to that of cerebral white-matter. This study presents a novel acquisition method for noninvasive measurement of brain temperature that is of potential value for diagnostic purposes and treatment monitoring, while also demonstrating limitations of the measurement due to the confounding effects of tissue susceptibility variations. Copyright © 2016 Elsevier Inc. All rights reserved.

Effects of Inventory Bias on Landslide Susceptibility Calculations

NASA Technical Reports Server (NTRS)

Stanley, T. A.; Kirschbaum, D. B.

2017-01-01

Many landslide inventories are known to be biased, especially inventories for large regions such as Oregon's SLIDO or NASA's Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modeling landslide susceptibility with heavily biased inventories.

Effects of Inventory Bias on Landslide Susceptibility Calculations

NASA Technical Reports Server (NTRS)

Stanley, Thomas; Kirschbaum, Dalia B.

2017-01-01

Many landslide inventories are known to be biased, especially inventories for large regions such as Oregons SLIDO or NASAs Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modelling landslide susceptibility with heavily biased inventories.

Spatial analysis for susceptibility of second-time karst sinkholes: A case study of Jili Village in Guangxi, China

NASA Astrophysics Data System (ADS)

Zhou, Guoqing; Yan, Hongbo; Chen, Kunhua; Zhang, Rongting

2016-04-01

After a big karst sinkhole happened in Jili Village of Guangxi, China, the local government was eager to quantitatively analyze and map susceptible areas of the potential second-time karst sinkholes in order to make timely decisions whether the residents living in the first-time sinkhole areas should move. For this reason, karst sinkholes susceptibility geospatial analysis is investigated using multivariate spatial data, logistic regression model (LRM) and Geographical Information System (GIS). Ten major karst sinkholes related factors, including (1) formation lithology, (2) soil structure, (3) profile curvature, (4) groundwater depth, (5) fluctuation of groundwater level, (6) percolation rate of soil, (7) degree of karst development, (8) distance from fault, (9) distance from the traffic route, and (10) overburden thickness were selected, and then each of factors was classified and quantitated with the three or four levels. The LRM was applied to evaluate which factor makes significant contributions to sinkhole. The results demonstrated that formation lithology, soil structure, profile curvature, groundwater depth, ground water level, percolation rate of soil, and degree of karst development, the distance from fault, and overburden thickness are positive, while one factor, the distance from traffic routes is negative, which is deleted from LRM model. The susceptibility of the potential sinkholes in the study area is estimated and mapped using the solved impact factors. The susceptible degrees of the study area are classified into five levels, very high, high, moderate, low, and ignore susceptibility. It has been found that that both very high and high susceptibility areas are along Datou Hill and the foothills of the study area. This finding is verified by field observations. With the investigations conducted in this paper, it can be concluded that the susceptibility maps produced in this paper are reliable and accurate, and useful as a reference for local governments to make decisions regarding whether or not residents living within sinkhole areas should move.

Dual-pathway multi-echo sequence for simultaneous frequency and T2 mapping

NASA Astrophysics Data System (ADS)

Cheng, Cheng-Chieh; Mei, Chang-Sheng; Duryea, Jeffrey; Chung, Hsiao-Wen; Chao, Tzu-Cheng; Panych, Lawrence P.; Madore, Bruno

2016-04-01

Purpose: To present a dual-pathway multi-echo steady state sequence and reconstruction algorithm to capture T2, T2∗ and field map information. Methods: Typically, pulse sequences based on spin echoes are needed for T2 mapping while gradient echoes are needed for field mapping, making it difficult to jointly acquire both types of information. A dual-pathway multi-echo pulse sequence is employed here to generate T2 and field maps from the same acquired data. The approach might be used, for example, to obtain both thermometry and tissue damage information during thermal therapies, or susceptibility and T2 information from a same head scan, or to generate bonus T2 maps during a knee scan. Results: Quantitative T2, T2∗ and field maps were generated in gel phantoms, ex vivo bovine muscle, and twelve volunteers. T2 results were validated against a spin-echo reference standard: A linear regression based on ROI analysis in phantoms provided close agreement (slope/R2 = 0.99/0.998). A pixel-wise in vivo Bland-Altman analysis of R2 = 1/T2 showed a bias of 0.034 Hz (about 0.3%), as averaged over four volunteers. Ex vivo results, with and without motion, suggested that tissue damage detection based on T2 rather than temperature-dose measurements might prove more robust to motion. Conclusion: T2, T2∗ and field maps were obtained simultaneously, from the same datasets, in thermometry, susceptibility-weighted imaging and knee-imaging contexts.

[Fine mapping of complex disease susceptibility loci].

PubMed

Song, Qingfeng; Zhang, Hongxing; Ma, Yilong; Zhou, Gangqiao

2014-01-01

Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers have identified more than 3800 susceptibility loci for more than 660 diseases or traits. However, the most significantly associated variants or causative variants in these loci and their biological functions have remained to be clarified. These causative variants can help to elucidate the pathogenesis and discover new biomarkers of complex diseases. One of the main goals in the post-GWAS era is to identify the causative variants and susceptibility genes, and clarify their functional aspects by fine mapping. For common variants, imputation or re-sequencing based strategies were implemented to increase the number of analyzed variants and help to identify the most significantly associated variants. In addition, functional element, expression quantitative trait locus (eQTL) and haplotype analyses were performed to identify functional common variants and susceptibility genes. For rare variants, fine mapping was carried out by re-sequencing, rare haplotype analysis, family-based analysis, burden test, etc.This review summarizes the strategies and problems for fine mapping.

The comparison between a ground based and a space based probabilistic landslide susceptibility assessment

NASA Astrophysics Data System (ADS)

Reichenbach, P.; Mondini, A.; Guzzetti, F.; Rossi, M.; Ardizzone, F.; Cardinali, M.

2009-04-01

Probabilistic landslide susceptibility assessments attempt to predict the location and threat posed by known landslides. Under the assumption that landslides will occur in the future because of the same conditions that produced them in the past, geomorphologists use susceptibility assessments to predict the location of future landslides. We present an attempt to exploit satellite data to prepare a landslide susceptibility zonation for a the Collazzone area that extends for 79 sq km in the Umbria region, Central Italy. For the study area we have prepared a map of the Normalized Difference Vegetation Index (NDVI) obtained by processing raw NIR and RED channels (b2 and b3 bands) at 15 m x 15 m resolution of an image acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), on board the TERRA satellite, and a map of Land Surface Temperature (LST) obtained by processing raw TIR channels (b11 to b15 bands) at 90 m × 90 m resolution from the same image. Both maps, in general proxy for soil moisture maps, were obtained through standard algorithms. As expected, there is a strong correspondence between NDVI and LST, but, when the NDVI does not change, elevation effects and others are predominant in LST. For the Collazzone area we prepared two different susceptibility models. The first was prepared through multivariate analysis of thematic data (including morphometry, lithology, structure and land use) obtained through traditional methods, primarily the interpretation of aerial photographs and field work. The second susceptibility model was prepared using terrain morphology and information obtained processing satellite data. The two models were compared in term of model fit and model performance and were validated exploiting landslide inventories not used to build the models. The two susceptibility models are very similar from a geographic and a classification point of view. This is good news, as it tells us that for landslide susceptibility, thematic maps obtained processing satellite data can be an effective alternative to maps prepared using more traditional, ground based methods.

Mapping of Mcs30, a new mammary carcinoma susceptibility quantitative trait locus (QTL30) on rat chromosome 12: identification of fry as a candidate Mcs gene.

PubMed

Ren, Xuefeng; Graham, Jessica C; Jing, Lichen; Mikheev, Andrei M; Gao, Yuan; Lew, Jenny Pan; Xie, Hong; Kim, Andrea S; Shang, Xiuling; Friedman, Cynthia; Vail, Graham; Fang, Ming Zhu; Bromberg, Yana; Zarbl, Helmut

2013-01-01

Rat strains differ dramatically in their susceptibility to mammary carcinogenesis. On the assumption that susceptibility genes are conserved across mammalian species and hence inform human carcinogenesis, numerous investigators have used genetic linkage studies in rats to identify genes responsible for differential susceptibility to carcinogenesis. Using a genetic backcross between the resistant Copenhagen (Cop) and susceptible Fischer 344 (F344) strains, we mapped a novel mammary carcinoma susceptibility (Mcs30) locus to the centromeric region on chromosome 12 (LOD score of ∼8.6 at the D12Rat59 marker). The Mcs30 locus comprises approximately 12 Mbp on the long arm of rat RNO12 whose synteny is conserved on human chromosome 13q12 to 13q13. After analyzing numerous genes comprising this locus, we identified Fry, the rat ortholog of the furry gene of Drosophila melanogaster, as a candidate Mcs gene. We cloned and determined the complete nucleotide sequence of the 13 kbp Fry mRNA. Sequence analysis indicated that the Fry gene was highly conserved across evolution, with 90% similarity of the predicted amino acid sequence among eutherian mammals. Comparison of the Fry sequence in the Cop and F344 strains identified two non-synonymous single nucleotide polymorphisms (SNPs), one of which creates a putative, de novo phosphorylation site. Further analysis showed that the expression of the Fry gene is reduced in a majority of rat mammary tumors. Our results also suggested that FRY activity was reduced in human breast carcinoma cell lines as a result of reduced levels or mutation. This study is the first to identify the Fry gene as a candidate Mcs gene. Our data suggest that the SNPs within the Fry gene contribute to the genetic susceptibility of the F344 rat strain to mammary carcinogenesis. These results provide the foundation for analyzing the role of the human FRY gene in cancer susceptibility and progression.

On the influence of zero-padding on the nonlinear operations in Quantitative Susceptibility Mapping

PubMed Central

Eskreis-Winkler, Sarah; Zhou, Dong; Liu, Tian; Gupta, Ajay; Gauthier, Susan A.; Wang, Yi; Spincemaille, Pascal

2016-01-01

Purpose Zero padding is a well-studied interpolation technique that improves image visualization without increasing image resolution. This interpolation is often performed as a last step before images are displayed on clinical workstations. Here, we seek to demonstrate the importance of zero padding before rather than after performing non-linear post-processing algorithms, such as Quantitative Susceptibility Mapping (QSM). To do so, we evaluate apparent spatial resolution, relative error and depiction of multiple sclerosis (MS) lesions on images that were zero padded prior to, in the middle of, and after the application of the QSM algorithm. Materials and Methods High resolution gradient echo (GRE) data were acquired on twenty MS patients, from which low resolution data were derived using k-space cropping. Pre-, mid-, and post-zero padded QSM images were reconstructed from these low resolution data by zero padding prior to field mapping, after field mapping, and after susceptibility mapping, respectively. Using high resolution QSM as the gold standard, apparent spatial resolution, relative error, and image quality of the pre-, mid-, and post-zero padded QSM images were measured and compared. Results Both the accuracy and apparent spatial resolution of the pre-zero padded QSM was higher than that of mid-zero padded QSM (p < 0.001; p < 0.001), which was higher than that of post-zero padded QSM (p < 0.001; p < 0.001). The image quality of pre-zero padded reconstructions was higher than that of mid- and post-zero padded reconstructions (p = 0.004; p < 0.001). Conclusion Zero padding of the complex GRE data prior to nonlinear susceptibility mapping improves image accuracy and apparent resolution compared to zero padding afterwards. It also provides better delineation of MS lesion geometry, which may improve lesion subclassification and disease monitoring in MS patients. PMID:27587225

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

NASA Astrophysics Data System (ADS)

Sdao, F.; Lioi, D. S.; Pascale, S.; Caniani, D.; Mancini, I. M.

2013-02-01

The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The assessment of the degree of "spatial hazard" or "susceptibility" was carried out by the spatial prediction regardless of the return time of the events. The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network. The method described in this paper is a novel technique based on a neuro-fuzzy system. It is able to train data like neural network and it is able to shape and control uncertain and complex systems like a fuzzy system. This methodology allows us to derive susceptibility maps of the study area. These data are obtained from thematic maps representing the parameters responsible for the instability of the slopes. The parameters used in the analysis are: plan curvature, elevation (DEM), angle and aspect of the slope, lithology, fracture density, kinematic hazard index of planar and wedge sliding and toppling. Moreover, this method is characterized by the network training which uses a training matrix, consisting of input and output training data, which determine the landslide susceptibility. The neuro-fuzzy method was integrated to a sensitivity analysis in order to overcome the uncertainty linked to the used membership functions. The method was compared to the landslide inventory map and was validated by applying three methods: a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a SCAI method. The developed neuro-fuzzy method showed a good performance in the determination of the landslide susceptibility map.

Mapping of soil erosion and redistribution on two agricultural areas in Czech Republic by using of magnetic parameters.

NASA Astrophysics Data System (ADS)

Kapicka, Ales; Stejskalova, Sarka; Grison, Hana; Petrovsky, Eduard; Jaksik, Ondrej; Kodesova, Radka

2015-04-01

Soil erosion is one of the major concerns in sustainability of agricultural systems in different areas. Therefore there is a need to develop suitable innovative indirect methods of soil survey. One of this methods is based on well established differentiation in magnetic signature with depth in soil profile. Magnetic method can be applied in the field as well as in the laboratory on collected soil samples. The aim of this study is to evaluate suitability of magnetic method to assess soil degradation and construct maps of cumulative soil loss due to erosion at two morphologically diverse areas with different soil types. Dominant soil unit in the first locality (Brumovice) is chernozem, which is gradually degraded on slopes to regosols. In the second site (Vidim), the dominant soil unit is luvisol, gradualy transformed to regosol due to erosion. Field measurements of magnetic susceptibility were carried out on regular grid, resulting in 101 data points in Brumovice and 65 in Vidim locality. Mass specific magnetic susceptibility χ and its frequency dependence χFD was used to estimate the significance of SP ferrimagnetic particles of pedogenic origin in top soil horizons. Strong correlation was found between the volume magnetic susceptibility (field measurement) and mass- specific magnetic susceptibility measured in the laboratory (Kapicka et al 2013). Values of magnetic susceptibility are spatially distributed depending on terrain position. Higher values were measured at the flat parts (where the original topsoil horizon remained). The lowest values magnetic susceptibility were obtained on the steep valley sides. Here the original topsoil was eroded and mixed by tillage with the soil substrate (loess). Positive correlation between the organic carbon content and volume magnetic susceptibility (R2= 0.89) was found for chernozem area. The differences between the values of susceptibility in the undisturbed soil profile and the magnetic signal after uniform mixing of the soil material as a result of tillage and erosion are fundamental for the estimation of soil loss in the studied test field (Royall 2001). The map of soil erosion shows maximum removal of soil material in the steepest parts of the testing localities. The magnetic method is very well suitable for mapping at the chernozem locality (Brumovice) and measurement of soil magnetic susceptibility is in this case a useful and fast technique for quantitative estimation of soil loss caused by erosion and tillage. However, it is less suitable (probably due to high terrain heterogeneity) for mapping in areas with luvisol as dominant soil unit. Acknowledgement: This study was supported by NAZV Agency of the Ministry of Agriculture of the Czech Republic through grant No QJ1230319. References : Royall, D. (2001). Use of mineral magnetic measurements to investigate soil erosion and sediment delivery in small agricultural catchment in limestone terrain. Catena, 46, 15-34. Kapicka, A., Dlouha, S., Grison, H., Jaksik, O., Kodesova, R., Petrovsky, E. (2013) Magnetism of soils applied for estimation of erosion at an agricultural land. Geophys Res Abstr Vol. 15, EGU2013 -4774.

A comprehensive numerical analysis of background phase correction with V-SHARP.

PubMed

Özbay, Pinar Senay; Deistung, Andreas; Feng, Xiang; Nanz, Daniel; Reichenbach, Jürgen Rainer; Schweser, Ferdinand

2017-04-01

Sophisticated harmonic artifact reduction for phase data (SHARP) is a method to remove background field contributions in MRI phase images, which is an essential processing step for quantitative susceptibility mapping (QSM). To perform SHARP, a spherical kernel radius and a regularization parameter need to be defined. In this study, we carried out an extensive analysis of the effect of these two parameters on the corrected phase images and on the reconstructed susceptibility maps. As a result of the dependence of the parameters on acquisition and processing characteristics, we propose a new SHARP scheme with generalized parameters. The new SHARP scheme uses a high-pass filtering approach to define the regularization parameter. We employed the variable-kernel SHARP (V-SHARP) approach, using different maximum radii (R m ) between 1 and 15 mm and varying regularization parameters (f) in a numerical brain model. The local root-mean-square error (RMSE) between the ground-truth, background-corrected field map and the results from SHARP decreased towards the center of the brain. RMSE of susceptibility maps calculated with a spatial domain algorithm was smallest for R m between 6 and 10 mm and f between 0 and 0.01 mm -1 , and for maps calculated with a Fourier domain algorithm for R m between 10 and 15 mm and f between 0 and 0.0091 mm -1 . We demonstrated and confirmed the new parameter scheme in vivo. The novel regularization scheme allows the use of the same regularization parameter irrespective of other imaging parameters, such as image resolution. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Amplified fragment length polymorphism mapping of quantitative trait loci for malaria parasite susceptibility in the yellow fever mosquito Aedes aegypti.

PubMed

Zhong, Daibin; Menge, David M; Temu, Emmanuel A; Chen, Hong; Yan, Guiyun

2006-07-01

The yellow fever mosquito Aedes aegypti has been the subject of extensive genetic research due to its medical importance and the ease with which it can be manipulated in the laboratory. A molecular genetic linkage map was constructed using 148 amplified fragment length polymorphism (AFLP) and six single-strand conformation polymorphism (SSCP) markers. Eighteen AFLP primer combinations were used to genotype two reciprocal F2 segregating populations. Each primer combination generated an average of 8.2 AFLP markers eligible for linkage mapping. The length of the integrated map was 180.9 cM, giving an average marker resolution of 1.2 cM. Composite interval mapping revealed a total of six QTL significantly affecting Plasmodium susceptibility in the two reciprocal crosses of Ae. aegypti. Two common QTL on linkage group 2 were identified in both crosses that had similar effects on the phenotype, and four QTL were unique to each cross. In one cross, the four main QTL accounted for 64% of the total phenotypic variance, and digenic epistasis explained 11.8% of the variance. In the second cross, the four main QTL explained 66% of the variance, and digenic epistasis accounted for 16% of the variance. The actions of these QTL were either dominance or underdominance. Our results indicated that at least three new QTL were mapped on chromosomes 1 and 3. The polygenic nature of susceptibility to P. gallinaceum and epistasis are important factors for significant variation within or among mosquito strains. The new map provides additional information useful for further genetic investigation, such as identification of new genes and positional cloning.

GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

NASA Astrophysics Data System (ADS)

Mancini, F.; Ceppi, C.; Ritrovato, G.

2010-09-01

This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by landslide phenomena. By means of LR analysis, the tendency to landslide occurrences was, therefore, assessed by relating a landslide inventory (dependent variable) to a series of causal factors (independent variables) which were managed in the GIS, while the statistical analyses were performed by means of the SPSS (Statistical Package for the Social Sciences) software. The LR analysis produced a reliable susceptibility map of the investigated area and the probability level of landslide occurrence was ranked in four classes. The overall performance achieved by the LR analysis was assessed by local comparison between the expected susceptibility and an independent dataset extrapolated from the landslide inventory. Of the samples classified as susceptible to landslide occurrences, 85% correspond to areas where landslide phenomena have actually occurred. In addition, the consideration of the regression coefficients provided by the analysis demonstrated that a major role is played by the "land cover" and "lithology" causal factors in determining the occurrence and distribution of landslide phenomena in the Apulian Apennines.

Sinkhole Susceptibility Analysis for Karapinar/konya via Multi Criteria Decision

NASA Astrophysics Data System (ADS)

Sarı, F.

2017-11-01

Sinkholes are being a natural hazard which threads economic and human life. Sudden occurrence characteristic of sinkholes make it unable to escape. There are a lot of factor that activate sinkholes such as geology, irrigation, land use and human related factors. In Karapınar, Konya, there are over 200 sinkholes and this count is getting increased in recent years. Especially active agricultural lands, decreasing ground water level, extreme irrigation by 55267 water wells increase the risk factor of Karapınar. Nowadays, considering the economic contribution of Karapınar to Turkey economy in the field of agriculture, solar energy fields and thermal reactor which will be planned in next few years, prediction of sinkholes and searching for preventation ways are being more important issue. In this study, sinkhole susceptibility map via AHP was carried out for Karapınar in Konya. Slope, land use, elevation, geology, water wells, distance to roads and settlements criteria are included to determine susceptibility. The weights are calculated with AHP for each criterion and generated susceptibility map is overlapped with existing sinkholes. Suggestions and results are shared for this study.

Predisposing factors and susceptibility assessment for deep-seated gravitational slope deformations (DSGSDs): a case study (NW Alps, Italy)

NASA Astrophysics Data System (ADS)

Lo Russo, S.; Forno, M. G.; Taddia, G.; Gnavi, L.

2012-04-01

KEY WORDS: Deep-seated gravitational slope deformation (DSGSD); Risk; Hazard; Susceptibility; Piemonte; Italy Deep-seated gravitational slope deformations (DSGSDs) and "sackung" deformations are complex processes of gravitational movement that involve large volumes of rock, often several tens of meters thick and several kilometers long. The development and characteristics of deep-seated gravitational slope deformations (DSGSDs) have not yet been fully explained. If unrecognized, these deformations can cause serious damage to rigid infrastructures such as dams, tunnels, and water conduits. Early identification of these phenomena and their predisposing factors through detailed geological and geomorphological surveys is therefore necessary for the correct location, construction, and expansion of fixed infrastructures. The hazard evaluation component of landslide risk assessment combines measures of susceptibility and triggering variables. This approach may not be applicable to DSGSDs, given the difficulty of quantifying the probability of occurrence within a specified period of time without well-defined DSGSD triggering factors. Evaluation of DSGSDs should thus be restricted to the assessment of susceptibility. Zones of DSGSD susceptibility can be identified through geological and geomorphological analysis, by overlapping maps of the four main predisposing factors (lithology, neotectonic activity, relief energy, morphological deglaciation evidence). The attribution of a susceptibility level to a certain zone cannot replace a hazard evaluation, but it can be a good index of the potential presence of a DSGSD. A DSGSD is most likely in a territory characterized by the worst combination of predisposing factors (high susceptibility): poor rock mechanics, intense neotectonic activity (high seismicity, active faults), high energy relief, and evidence of past glacialism. The probability of a DSGSD correspondingly decreases if one or more of the predisposing factors are absent (low susceptibility). A case study of two DSGSDs located in the Rodoretto Valley (northwestern Alps, Italy) has been examined. After detailed field survey provided morphological identification of these features, the authors conducted a back-analysis to assess the susceptibility of the entire valley. Each main predisposing factor has been independently mapped, and the level of susceptibility to DSGSD has been identified through geographic information system (GIS) overlapping of the four maps. The results confirm the combined presence of four main predisposing factors for the examined DGSDs, indicating high susceptibility.

Prioritization of Disease Susceptibility Genes Using LSM/SVD.

PubMed

Gong, Lejun; Yang, Ronggen; Yan, Qin; Sun, Xiao

2013-12-01

Understanding the role of genetics in diseases is one of the most important tasks in the postgenome era. It is generally too expensive and time consuming to perform experimental validation for all candidate genes related to disease. Computational methods play important roles for prioritizing these candidates. Herein, we propose an approach to prioritize disease genes using latent semantic mapping based on singular value decomposition. Our hypothesis is that similar functional genes are likely to cause similar diseases. Measuring the functional similarity between known disease susceptibility genes and unknown genes is to predict new disease susceptibility genes. Taking autism as an instance, the analysis results of the top ten genes prioritized demonstrate they might be autism susceptibility genes, which also indicates our approach could discover new disease susceptibility genes. The novel approach of disease gene prioritization could discover new disease susceptibility genes, and latent disease-gene relations. The prioritized results could also support the interpretive diversity and experimental views as computational evidence for disease researchers.

Quantitative trait locus gene mapping: a new method for locating alcohol response genes.

PubMed

Crabbe, J C

1996-01-01

Alcoholism is a multigenic trait with important non-genetic determinants. Studies with genetic animal models of susceptibility to several of alcohol's effects suggest that several genes contributing modest effects on susceptibility (Quantitative Trait Loci, or QTLs) are important. A new technique of QTL gene mapping has allowed the identification of the location in mouse genome of several such QTLs. The method is described, and the locations of QTLs affecting the acute alcohol withdrawal reaction are described as an example of the method. Verification of these QTLs in ancillary studies is described and the strengths, limitations, and future directions to be pursued are discussed. QTL mapping is a promising method for identifying genes in rodents with the hope of directly extrapolating the results to the human genome. This review is based on a paper presented at the First International Congress of the Latin American Society for Biomedical Research on Alcoholism, Santiago, Chile, November 1994.

ShakeMap-based prediction of earthquake-induced mass movements in Switzerland calibrated on historical observations

USGS Publications Warehouse

Cauzzi, Carlo; Fah, Donat; Wald, David J.; Clinton, John; Losey, Stephane; Wiemer, Stefan

2018-01-01

In Switzerland, nearly all historical Mw ~ 6 earthquakes have induced damaging landslides, rockslides and snow avalanches that, in some cases, also resulted in damage to infrastructure and loss of lives. We describe the customisation to Swiss conditions of a globally calibrated statistical approach originally developed to rapidly assess earthquake-induced landslide likelihoods worldwide. The probability of occurrence of such earthquake-induced effects is modelled through a set of geospatial susceptibility proxies and peak ground acceleration. The predictive model is tuned to capture the observations from past events and optimised for near-real-time estimates based on USGS-style ShakeMaps routinely produced by the Swiss Seismological Service. Our emphasis is on the use of high-resolution geospatial datasets along with additional local information on ground failure susceptibility. Even if calibrated on historic events with moderate magnitudes, the methodology presented in this paper yields sensible results also for low-magnitude recent events. The model is integrated in the Swiss ShakeMap framework. This study has a high practical relevance to many Swiss ShakeMap stakeholders, especially those managing lifeline systems, and to other global users interested in conducting a similar customisation for their region of interest.

Spatial analysis of geologic and hydrologic features relating to sinkhole occurrence in Jefferson County, West Virginia

USGS Publications Warehouse

Doctor, Daniel H.; Doctor, Katarina Z.

2012-01-01

In this study the influence of geologic features related to sinkhole susceptibility was analyzed and the results were mapped for the region of Jefferson County, West Virginia. A model of sinkhole density was constructed using Geographically Weighted Regression (GWR) that estimated the relations among discrete geologic or hydrologic features and sinkhole density at each sinkhole location. Nine conditioning factors on sinkhole occurrence were considered as independent variables: distance to faults, fold axes, fracture traces oriented along bedrock strike, fracture traces oriented across bedrock strike, ponds, streams, springs, quarries, and interpolated depth to groundwater. GWR model parameter estimates for each variable were evaluated for significance, and the results were mapped. The results provide visual insight into the influence of these variables on localized sinkhole density, and can be used to provide an objective means of weighting conditioning factors in models of sinkhole susceptibility or hazard risk.

A simple landslide susceptibility analysis for hazard and risk assessment in developing countries

NASA Astrophysics Data System (ADS)

Guinau, M.; Vilaplana, J. M.

2003-04-01

In recent years, a number of techniques and methodologies have been developed for mitigating natural disasters. The complexity of these methodologies and the scarcity of material and data series justify the need for simple methodologies to obtain the necessary information for minimising the effects of catastrophic natural phenomena. The work with polygonal maps using a GIS allowed us to develop a simple methodology, which was developed in an area of 473 Km2 in the Departamento de Chinandega (NW Nicaragua). This area was severely affected by a large number of landslides (mainly debris flows), triggered by the Hurricane Mitch rainfalls in October 1998. With the aid of aerial photography interpretation at 1:40.000 scale, amplified to 1:20.000, and detailed field work, a landslide map at 1:10.000 scale was constructed. The failure zones of landslides were digitized in order to obtain a failure zone digital map. A terrain unit digital map, in which a series of physical-environmental terrain factors are represented, was also used. Dividing the studied area into two zones (A and B) with homogeneous physical and environmental characteristics, allows us to develop the proposed methodology and to validate it. In zone A, the failure zone digital map is superimposed onto the terrain unit digital map to establish the relationship between the different terrain factors and the failure zones. The numerical expression of this relationship enables us to classify the terrain by its landslide susceptibility. In zone B, this numerical relationship was employed to obtain a landslide susceptibility map, obviating the need for a failure zone map. The validity of the methodology can be tested in this area by using the degree of superposition of the susceptibility map and the failure zone map. The implementation of the methodology in tropical countries with physical and environmental characteristics similar to those of the study area allows us to carry out a landslide susceptibility analysis in areas where landslide records do not exist. This analysis is essential to landslide hazard and risk assessment, which is necessary to determine the actions for mitigating landslide effects, e.g. land planning, emergency aid actions, etc.

Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method.

PubMed

Sun, Hongfu; Ma, Yuhan; MacDonald, M Ethan; Pike, G Bruce

2018-06-15

A new dipole field inversion method for whole head quantitative susceptibility mapping (QSM) is proposed. Instead of performing background field removal and local field inversion sequentially, the proposed method performs dipole field inversion directly on the total field map in a single step. To aid this under-determined and ill-posed inversion process and obtain robust QSM images, Tikhonov regularization is implemented to seek the local susceptibility solution with the least-norm (LN) using the L-curve criterion. The proposed LN-QSM does not require brain edge erosion, thereby preserving the cerebral cortex in the final images. This should improve its applicability for QSM-based cortical grey matter measurement, functional imaging and venography of full brain. Furthermore, LN-QSM also enables susceptibility mapping of the entire head without the need for brain extraction, which makes QSM reconstruction more automated and less dependent on intermediate pre-processing methods and their associated parameters. It is shown that the proposed LN-QSM method reduced errors in a numerical phantom simulation, improved accuracy in a gadolinium phantom experiment, and suppressed artefacts in nine subjects, as compared to two-step and other single-step QSM methods. Measurements of deep grey matter and skull susceptibilities from LN-QSM are consistent with established reconstruction methods. Copyright © 2018 Elsevier Inc. All rights reserved.

Cognitive Implications of Deep Gray Matter Iron in Multiple Sclerosis.

PubMed

Fujiwara, E; Kmech, J A; Cobzas, D; Sun, H; Seres, P; Blevins, G; Wilman, A H

2017-05-01

Deep gray matter iron accumulation is increasingly recognized in association with multiple sclerosis and can be measured in vivo with MR imaging. The cognitive implications of this pathology are not well-understood, especially vis-à-vis deep gray matter atrophy. Our aim was to investigate the relationships between cognition and deep gray matter iron in MS by using 2 MR imaging-based iron-susceptibility measures. Forty patients with multiple sclerosis (relapsing-remitting, n = 16; progressive, n = 24) and 27 healthy controls were imaged at 4.7T by using the transverse relaxation rate and quantitative susceptibility mapping. The transverse relaxation rate and quantitative susceptibility mapping values and volumes (atrophy) of the caudate, putamen, globus pallidus, and thalamus were determined by multiatlas segmentation. Cognition was assessed with the Brief Repeatable Battery of Neuropsychological Tests. Relationships between cognition and deep gray matter iron were examined by hierarchic regressions. Compared with controls, patients showed reduced memory ( P < .001) and processing speed ( P = .02) and smaller putamen ( P < .001), globus pallidus ( P = .002), and thalamic volumes ( P < .001). Quantitative susceptibility mapping values were increased in patients compared with controls in the putamen ( P = .003) and globus pallidus ( P = .003). In patients only, thalamus ( P < .001) and putamen ( P = .04) volumes were related to cognitive performance. After we controlled for volume effects, quantitative susceptibility mapping values in the globus pallidus ( P = .03; trend for transverse relaxation rate, P = .10) were still related to cognition. Quantitative susceptibility mapping was more sensitive compared with the transverse relaxation rate in detecting deep gray matter iron accumulation in the current multiple sclerosis cohort. Atrophy and iron accumulation in deep gray matter both have negative but separable relationships to cognition in multiple sclerosis. © 2017 by American Journal of Neuroradiology.

Landfill site selection for municipal solid wastes in mountainous areas with landslide susceptibility.

PubMed

Eskandari, Mahnaz; Homaee, Mehdi; Falamaki, Amin

2016-06-01

Several cities across the world are located in mountainous and landslide prone areas. Any landfill siting without considering landslide susceptibility in such regions may impose additional environmental adversity. This study was aimed to propose a practical method for selecting waste disposal site that accounts for landslide exposure. The proposed method was applied to a city which is highly proneness to landslide due to its geology, morphology, and climatic conditions. First, information on the previously occurred landslides of the region was collected. Based on this information, proper landslide causative factors were selected and their thematic maps were prepared. Factors' classes were then standardized in 0-1 domain, and thematic layers were weighted by using analytical hierarchy process (AHP). The landslide susceptibility map was prepared afterwards. Unsuitable areas for landfill location were masked in GIS environment by Boolean method, retaining sufficient areas for further evaluation. Nine remaining alternatives were selected through comprehensive field visits and were ranked by using AHP. Consequently, 17 factors in three environmental, economical, and social perspectives were employed. Sensitivity analyses were performed to assess the stability of the alternatives ranking with respect to variations in criterion weights. Based on the obtained landslide susceptible map, nearly 36 % of the entire region is proneness to landslide. The prepared Boolean map indicates that potential areas for landfill construction cover 11 % of the whole region. The results further indicated that if landslide susceptible areas are not considered in landfill site selection, the potential landfill sites would become more than twice. It can be concluded that if any of these landslide prone sites are selected for landfilling, further environmental disaster would be terminated in the future. It can be further concluded that the proposed method could reasonably well be adjusted to consider landslide exposure when siting a solid waste landfill.

Quantitative Susceptibility Mapping of Amyloid-β Aggregates in Alzheimer's Disease with 7T MR.

PubMed

Tiepolt, Solveig; Schäfer, Andreas; Rullmann, Michael; Roggenhofer, Elisabeth; Gertz, Hermann-Josef; Schroeter, Matthias L; Patt, Marianne; Bazin, Pierre-Louis; Jochimsen, Thies H; Turner, Robert; Sabri, Osama; Barthel, Henryk

2018-05-28

PET imaging is an established technique to detect cerebral amyloid-β (Aβ) plaques in vivo. Some preclinical and postmortem data report an accumulation of redox-active iron near Aβ plaques. Quantitative susceptibility mapping (QSM) at high-field MRI enables iron deposits to be depicted with high spatial resolution. Aim of this study was to examine whether iron and Aβ plaque accumulation is related and thus, whether 7T MRI might be an additive diagnostic tool to Aβ PET imaging. Postmortem human Alzheimer's disease (AD) and healthy control (HC) frontal gray matter (GM) was imaged with 7T MRI which resulted in T1 maps and QSM. Aβ plaque load was determined by histopathology. In vivo, 10 Aβ PET-positive AD patients (74.1±6.0a) and 10 Aβ PET-negative HCs (67.1±4.4a) underwent 7T MR examination and QSM maps were analyzed. Severity of cognitive deficits was determined by MMSE. Postmortem, the susceptibility of Aβ plaque-containing GM were higher than those of Aβ plaque-free GM (0.011±0.002 versus - 0.008±0.003 ppm, p <  0.001). In vivo, only the bilateral globus pallidus showed significantly higher susceptibility in AD patients compared to HCs (right: 0.277±0.018 versus - 0.009±0.009 ppm; left: 0.293±0.014 versus - 0.007±0.012 ppm, p <  0.0001). The pallidal QSM values were negatively correlated with those of the MMSE (r = - 0.69, p = 0.001). The postmortem study revealed significant susceptibility differences between the Aβ plaque-containing and Aβ plaque-free GM, whereas in vivo only the QSM values of the globus pallidus differed significantly between AD and HC group. The pallidal QSM values correlated with the severity of cognitive deficits. These findings encourage efforts to optimize the 7T-QSM methodology.

Assessment of MR-based R2* and quantitative susceptibility mapping for the quantification of liver iron concentration in a mouse model at 7T.

PubMed

Simchick, Gregory; Liu, Zhi; Nagy, Tamas; Xiong, May; Zhao, Qun

2018-03-25

To assess the feasibility of quantifying liver iron concentration (LIC) using R2* and quantitative susceptibility mapping (QSM) at a high field strength of 7 Tesla (T). Five different concentrations of Fe-dextran were injected into 12 mice to produce various degrees of liver iron overload. After mice were sacrificed, blood and liver samples were harvested. Ferritin enzyme-linked immunosorbent assay (ELISA) and inductively coupled plasma mass spectrometry were performed to quantify serum ferritin concentration and LIC. Multiecho gradient echo MRI was conducted to estimate R2* and the magnetic susceptibility of each liver sample through complex nonlinear least squares fitting and a morphology enabled dipole inversion method, respectively. Average estimates of serum ferritin concentration, LIC, R2*, and susceptibility all show good linear correlations with injected Fe-dextran concentration; however, the standard deviations in the estimates of R2* and susceptibility increase with injected Fe-dextran concentration. Both R2* and susceptibility measurements also show good linear correlations with LIC (R 2  = 0.78 and R 2  = 0.91, respectively), and a susceptibility-to-LIC conversion factor of 0.829 ppm/(mg/g wet) is derived. The feasibility of quantifying LIC using MR-based  R2* and QSM at a high field strength of 7T is demonstrated. Susceptibility quantification, which is an intrinsic property of tissues and benefits from being field-strength independent, is more robust than R2* quantification in this ex vivo study. A susceptibility-to-LIC conversion factor is presented that agrees relatively well with previously published QSM derived results obtained at 1.5T and 3T. © 2018 International Society for Magnetic Resonance in Medicine.

Landslide susceptibility mapping in Mawat area, Kurdistan Region, NE Iraq: a comparison of different statistical models

NASA Astrophysics Data System (ADS)

Othman, A. A.; Gloaguen, R.; Andreani, L.; Rahnama, M.

2015-03-01

During the last decades, expansion of settlements into areas prone to landslides in Iraq has increased the importance of accurate hazard assessment. Susceptibility mapping provides information about hazardous locations and thus helps to potentially prevent infrastructure damage due to mass wasting. The aim of this study is to evaluate and compare frequency ratio (FR), weight of evidence (WOE), logistic regression (LR) and probit regression (PR) approaches in combination with new geomorphological indices to determine the landslide susceptibility index (LSI). We tested these four methods in Mawat area, Kurdistan Region, NE Iraq, where landslides occur frequently. For this purpose, we evaluated 16 geomorphological, geological and environmental predicting factors mainly derived from the advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite. The available reference inventory includes 351 landslides representing a cumulative surface of 3.127 km2. This reference inventory was mapped from QuickBird data by manual delineation and partly verified by field survey. The areas under curve (AUC) of the receiver operating characteristic (ROC), and relative landslide density (R index) show that all models perform similarly and that focus should be put on the careful selection of proxies. The results indicate that the lithology and the slope aspects play major roles for landslide occurrences. Furthermore, this paper demonstrates that using hypsometric integral as a prediction factor instead of slope curvature gives better results and increases the accuracy of the LSI.

Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

NASA Astrophysics Data System (ADS)

Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

2017-11-01

The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

Comparative study of landslides susceptibility mapping methods: Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN)

NASA Astrophysics Data System (ADS)

Salleh, S. A.; Rahman, A. S. A. Abd; Othman, A. N.; Mohd, W. M. N. Wan

2018-02-01

As different approach produces different results, it is crucial to determine the methods that are accurate in order to perform analysis towards the event. This research aim is to compare the Rank Reciprocal (MCDM) and Artificial Neural Network (ANN) analysis techniques in determining susceptible zones of landslide hazard. The study is based on data obtained from various sources such as local authority; Dewan Bandaraya Kuala Lumpur (DBKL), Jabatan Kerja Raya (JKR) and other agencies. The data were analysed and processed using Arc GIS. The results were compared by quantifying the risk ranking and area differential. It was also compared with the zonation map classified by DBKL. The results suggested that ANN method gives better accuracy compared to MCDM with 18.18% higher accuracy assessment of the MCDM approach. This indicated that ANN provides more reliable results and it is probably due to its ability to learn from the environment thus portraying realistic and accurate result.

Assessment of sand encroachment in Kuwait using GIS

NASA Astrophysics Data System (ADS)

Al-Helal, Anwar B.; Al-Awadhi, Jasem M.

2006-04-01

Assessment of sand encroachment in Kuwait using Geographical Information System (GIS) technology has been formulated as a Multi-Criteria Decision Making problem. The Delphi method and Analytical Hierarchy Process were adopted as evaluating techniques, in which experts’ judgments were analyzed for objectively estimating and weighting control factors. Seven triggering factors, depicted in the form of maps, were identified and ordered according to their priority. These factors are (1) wind energy; (2) surface sediment; (3) vegetation density; (4) land use; (5) drainage density; (6) topographic change and (7) vegetation type. The factor maps were digitized, converted to raster data and overlaid to determine their possible spatial relationships. Applying a susceptibility model, a map of sand encroachment susceptibility in Kuwait was developed. The map showed that the areas of very high and high sand encroachment susceptibility are located within the main corridor of sand pathway that coincides with the northwesterly dominant wind direction.

Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey).

PubMed

Akgun, Aykut; Kıncal, Cem; Pradhan, Biswajeet

2012-09-01

In this study, landslide risk assessment for Izmir city (west Turkey) was carried out, and the environmental effects of landslides on further urban development were evaluated using geographical information systems and remote sensing techniques. For this purpose, two different data groups, namely conditioning and triggering data, were produced. With the help of conditioning data such as lithology, slope gradient, slope aspect, distance from roads, distance from faults and distance from drainage lines, a landslide susceptibility model was constructed by using logistic regression modelling approach. The accuracy assessment of the susceptibility map was carried out by the area under curvature (AUC) approach, and a 0.810 AUC value was obtained. This value shows that the map obtained is successful. Due to the fact that the study area is located in an active seismic region, earthquake data were considered as primary triggering factor contributing to landslide occurrence. In addition to this, precipitation data were also taken into account as a secondary triggering factor. Considering the susceptibility data and triggering factors, a landslide hazard index was obtained. Furthermore, using the Aster data, a land-cover map was produced with an overall kappa value of 0.94. From this map, settlement areas were extracted, and these extracted data were assessed as elements at risk in the study area. Next, a vulnerability index was created by using these data. Finally, the hazard index and the vulnerability index were combined, and a landslide risk map for Izmir city was obtained. Based on this final risk map, it was observed that especially south and north parts of the Izmir Bay, where urbanization is dense, are threatened to future landsliding. This result can be used for preliminary land use planning by local governmental authorities.

Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)

NASA Astrophysics Data System (ADS)

Trigila, Alessandro; Iadanza, Carla; Esposito, Carlo; Scarascia-Mugnozza, Gabriele

2015-11-01

The aim of this work is to define reliable susceptibility models for shallow landslides using Logistic Regression and Random Forests multivariate statistical techniques. The study area, located in North-East Sicily, was hit on October 1st 2009 by a severe rainstorm (225 mm of cumulative rainfall in 7 h) which caused flash floods and more than 1000 landslides. Several small villages, such as Giampilieri, were hit with 31 fatalities, 6 missing persons and damage to buildings and transportation infrastructures. Landslides, mainly types such as earth and debris translational slides evolving into debris flows, were triggered on steep slopes and involved colluvium and regolith materials which cover the underlying metamorphic bedrock. The work has been carried out with the following steps: i) realization of a detailed event landslide inventory map through field surveys coupled with observation of high resolution aerial colour orthophoto; ii) identification of landslide source areas; iii) data preparation of landslide controlling factors and descriptive statistics based on a bivariate method (Frequency Ratio) to get an initial overview on existing relationships between causative factors and shallow landslide source areas; iv) choice of criteria for the selection and sizing of the mapping unit; v) implementation of 5 multivariate statistical susceptibility models based on Logistic Regression and Random Forests techniques and focused on landslide source areas; vi) evaluation of the influence of sample size and type of sampling on results and performance of the models; vii) evaluation of the predictive capabilities of the models using ROC curve, AUC and contingency tables; viii) comparison of model results and obtained susceptibility maps; and ix) analysis of temporal variation of landslide susceptibility related to input parameter changes. Models based on Logistic Regression and Random Forests have demonstrated excellent predictive capabilities. Land use and wildfire variables were found to have a strong control on the occurrence of very rapid shallow landslides.

An innovative tool for landslide susceptibility mapping in Kyrgyzstan, Central Asia

NASA Astrophysics Data System (ADS)

Saponaro, Annamaria; Pilz, Marco; Wieland, Marc; Bindi, Dino; Parolai, Stefano

2013-04-01

Kyrgyzstan is among the most exposed countries in the world to landslide susceptibility. The high seismicity of the area, the presence of high mountain ridges and topographic relieves, the geology of the local materials and the occurrence of heavy precipitations represent the main factors responsible for slope failures. In particular, the large variability of material properties and slope conditions as well as the difficulties in forecasting heavy precipitations locally and in quantifying the level of ground shaking call for harmonized procedures for reducing the negative impact of these factors. Several studies have recently been carried out aiming at preparing landslide susceptibility and hazard maps; however, some of them - qualitative-based - suffer from the application of subjective decision rules from experts in the classification of parameters that influence the occurrence of a landslide. On the other hand, statistical methods provide objectivity over qualitative ones since they allow a numerical evaluation of landslide spatial distribution with landslide potential factors. For this reason, we will make use of a bivariate technique known as Weight-Of-Evidence method to evaluate the influence of landslide predictive factors. The aim of this study is to identify areas in Kyrgyzstan being more prone to earthquake-triggered landslides. An innovative approach which exploits the new advances of GIS technology together with statistical concepts is presented. A range of conditioning factors and their potential impact on landslide activation is quantitatively assessed on the basis of landslide spatial distribution and seismic zonation. Results show areas which are more susceptible to landslides induced by earthquakes. Our approach can be used to fill the gap of subjectivity that typically affects already performed qualitative analysis. The resulting landslide susceptibility map represents a potentially supportive tool for disaster management and planning activities at regional level; furthermore, it can be used as a starting point for further constraining the analysis which might also consider meteorological influences and for carrying out an extension to all Central Asian countries in the framework of cross-border activities.

Measuring iron in the brain using quantitative susceptibility mapping and X-ray fluorescence imaging

PubMed Central

Zheng, Weili; Nichol, Helen; Liu, Saifeng; Cheng, Yu-Chung N.; Haacke, E. Mark

2013-01-01

Measuring iron content in the brain has important implications for a number of neurodegenerative diseases. Quantitative susceptibility mapping (QSM), derived from magnetic resonance images, has been used to measure total iron content in vivo and in post mortem brain. In this paper, we show how magnetic susceptibility from QSM correlates with total iron content measured by X-ray fluorescence (XRF) imaging and by inductively coupled plasma mass spectrometry (ICPMS). The relationship between susceptibility and ferritin iron was estimated at 1.10 ± 0.08 ppb susceptibility per μg iron/g wet tissue, similar to that of iron in fixed (frozen/thawed) cadaveric brain and previously published data from unfixed brains. We conclude that magnetic susceptibility can provide a direct and reliable quantitative measurement of iron content and that it can be used clinically at least in regions with high iron content. PMID:23591072

Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed.

PubMed

Altaf, Sadaff; Meraj, Gowhar; Romshoo, Shakil Ahmad

2014-12-01

Complex mountainous environments such as Himalayas are highly susceptibility to natural hazards particular those that are triggered by the action of water such as floods, soil erosion, mass movements and siltation of the hydro-electric power dams. Among all the natural hazards, soil erosion is the most implicit and the devastating hazard affecting the life and property of the millions of people living in these regions. Hence to review and devise strategies to reduce the adverse impacts of soil erosion is of utmost importance to the planners of watershed management programs in these regions. This paper demonstrates the use of satellite based remote sensing data coupled with the observational field data in a multi-criteria analytical (MCA) framework to estimate the soil erosion susceptibility of the sub-watersheds of the Rembiara basin falling in the western Himalaya, using geographical information system (GIS). In this paper, watershed morphometry and land cover are used as an inputs to the MCA framework to prioritize the sub-watersheds of this basin on the basis of their different susceptibilities to soil erosion. Methodology included the derivation of a set of drainage and land cover parameters that act as the indicators of erosion susceptibility. Further the output from the MCA resulted in the categorization of the sub-watersheds into low, medium, high and very high erosion susceptibility classes. A detailed prioritization map for the susceptible sub-watersheds based on the combined role of land cover and morphometry is finally presented. Besides, maps identifying the susceptible sub-watersheds based on morphometry and land cover only are also presented. The results of this study are part of the watershed management program in the study area and are directed to instigate appropriate measures to alleviate the soil erosion in the study area.

Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods

NASA Astrophysics Data System (ADS)

Pourghasemi, Hamid Reza; Rossi, Mauro

2017-10-01

Landslides are identified as one of the most important natural hazards in many areas throughout the world. The essential purpose of this study is to compare general linear model (GLM), general additive model (GAM), multivariate adaptive regression spline (MARS), and modified analytical hierarchy process (M-AHP) models and assessment of their performances for landslide susceptibility modeling in the west of Mazandaran Province, Iran. First, landslides were identified by interpreting aerial photographs, and extensive field works. In total, 153 landslides were identified in the study area. Among these, 105 landslides were randomly selected as training data (i.e. used in the models training) and the remaining 48 (30 %) cases were used for the validation (i.e. used in the models validation). Afterward, based on a deep literature review on 220 scientific papers (period between 2005 and 2012), eleven conditioning factors including lithology, land use, distance from rivers, distance from roads, distance from faults, slope angle, slope aspect, altitude, topographic wetness index (TWI), plan curvature, and profile curvature were selected. The Certainty Factor (CF) model was used for managing uncertainty in rule-based systems and evaluation of the correlation between the dependent (landslides) and independent variables. Finally, the landslide susceptibility zonation was produced using GLM, GAM, MARS, and M-AHP models. For evaluation of the models, the area under the curve (AUC) method was used and both success and prediction rate curves were calculated. The evaluation of models for GLM, GAM, and MARS showed 90.50, 88.90, and 82.10 % for training data and 77.52, 70.49, and 78.17 % for validation data, respectively. Furthermore, The AUC value of the produced landslide susceptibility map using M-AHP showed a training value of 77.82 % and validation value of 82.77 % accuracy. Based on the overall assessments, the proposed approaches showed reasonable results for landslide susceptibility mapping in the study area. Moreover, results obtained showed that the M-AHP model performed slightly better than the MARS, GLM, and GAM models in prediction. These algorithms can be very useful for landslide susceptibility and hazard mapping and land use planning in regional scale.

Combine bivariate statistics analysis and multivariate statistics analysis to assess landslide susceptibility in Chen-Yu-Lan watershed, Nantou, Taiwan.

NASA Astrophysics Data System (ADS)

Ngan Nguyen, Thi To; Liu, Cheng-Chien

2013-04-01

How landslides occurred and which factors triggered and sped up landslide occurrences were usually asked by researchers in the past decades. Many investigations carried out in many places in the world to finding out methods that predict and prevent damages from landslides phenomena. Chen-Yu-Lan River watershed is reputed as a 'hot pot' of landslide researches in Taiwan by its complicated geological structures with the significant tectonic fault systems and steeply mountainous terrain. Beside annual high precipitation concentration and the abrupt slopes, some natural disaster, as typhoons (Sinlaku-2008, Kalmaegi-2008, and Marakot-2009) and earthquake (Chi-Chi earthquake-1999) are also the triggered factors cause landslides with serious damages in this place. This research expresses the quantitative approaches to generate landslide susceptible map for Chen-Yu-Lan watershed, a mountainous area in the central Taiwan. Landslide inventories data, which were detected from the Formosat-2 imageries for eight years from 2004 to 2011, were applied to carry out landslide susceptibility mapping. Bivariate statistics analysis and multivariate statistics analysis would be applied to calculate susceptible index of landslides. The weights of parameters were computed based on landslide data for eight years from 2004 to 2011. To validate effective levels of factors to landslide occurrences, this method built some multivariate algorithms and compared these results with real landslide occurrences. Besides this method, the historical data of landslides were also used to assess and classify landslide susceptibility levels. From long-term landslide data, relation between landslide susceptibility levels and landslide repetition was assigned. The results demonstrated differently effective levels of potential factors, such as, slope gradient, drainage density, lithology and land use to landslide phenomena. The results also showed logical relationship between weights and characteristics of factors' classes. Depending on these results be able to help planning managers localize the high risk areas of landslide or safely areas by building and human activities.

Susceptibility-Weighted Imaging and Quantitative Susceptibility Mapping in the Brain

PubMed Central

Liu, Chunlei; Li, Wei; Tong, Karen A.; Yeom, Kristen W.; Kuzminski, Samuel

2015-01-01

Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) technique that enhances image contrast by using the susceptibility differences between tissues. It is created by combining both magnitude and phase in the gradient echo data. SWI is sensitive to both paramagnetic and diamagnetic substances which generate different phase shift in MRI data. SWI images can be displayed as a minimum intensity projection that provides high resolution delineation of the cerebral venous architecture, a feature that is not available in other MRI techniques. As such, SWI has been widely applied to diagnose various venous abnormalities. SWI is especially sensitive to deoxygenated blood and intracranial mineral deposition and, for that reason, has been applied to image various pathologies including intracranial hemorrhage, traumatic brain injury, stroke, neoplasm, and multiple sclerosis. SWI, however, does not provide quantitative measures of magnetic susceptibility. This limitation is currently being addressed with the development of quantitative susceptibility mapping (QSM) and susceptibility tensor imaging (STI). While QSM treats susceptibility as isotropic, STI treats susceptibility as generally anisotropic characterized by a tensor quantity. This article reviews the basic principles of SWI, its clinical and research applications, the mechanisms governing brain susceptibility properties, and its practical implementation, with a focus on brain imaging. PMID:25270052

Positive visualization of implanted devices with susceptibility gradient mapping using the original resolution.

PubMed

Varma, Gopal; Clough, Rachel E; Acher, Peter; Sénégas, Julien; Dahnke, Hannes; Keevil, Stephen F; Schaeffter, Tobias

2011-05-01

In magnetic resonance imaging, implantable devices are usually visualized with a negative contrast. Recently, positive contrast techniques have been proposed, such as susceptibility gradient mapping (SGM). However, SGM reduces the spatial resolution making positive visualization of small structures difficult. Here, a development of SGM using the original resolution (SUMO) is presented. For this, a filter is applied in k-space and the signal amplitude is analyzed in the image domain to determine quantitatively the susceptibility gradient for each pixel. It is shown in simulations and experiments that SUMO results in a better visualization of small structures in comparison to SGM. SUMO is applied to patient datasets for visualization of stent and prostate brachytherapy seeds. In addition, SUMO also provides quantitative information about the number of prostate brachytherapy seeds. The method might be extended to application for visualization of other interventional devices, and, like SGM, it might also be used to visualize magnetically labelled cells. Copyright © 2010 Wiley-Liss, Inc.

Mapping of a disease susceptibility locus in chromosome 6p in Japanese patients with ulcerative colitis.

PubMed

Nomura, E; Kinouchi, Y; Negoro, K; Kojima, Y; Oomori, S; Sugimura, M; Hiroki, M; Takagi, S; Aihara, H; Takahashi, S; Hiwatashi, N; Shimosegawa, T

2004-09-01

Ulcerative colitis (UC) is a multifactorial disorder with both genetic and environmental factors. HLA-B*52 and DRB1*1502 are reported to be strongly associated with UC in Japan. However, the actual susceptible gene has not been identified yet. In this study, to map precisely the susceptible locus for UC, we performed association mapping in the chromosome 6p using 24 microsatellite markers distributed over 16 Mb. A total of 183 patients with UC and 186 healthy controls (HC) were included in this study. In all, 15 markers around the human leukocyte antigen (HLA) region showed statistical significance in the genotypic differentiation test concerned with the allelic distribution between the UC and HC. Especially, the markers between the centromeric region of HLA class I and the telomeric region of class III showed remarkably low P-values and the allele239 of C2-4-4 in class I marker showed the strongest association (Pc=2.9 x 10(-9): OR=3.74, 95% CI=2.50-5.60). Furthermore, we found strong linkage disequilibrium (LD) between the allele239 of C2-4-4 and HLA-B*52 in haplotype analysis. These results provide evidence that, in Japanese, important determinants of disease susceptibility to UC may exist in HLA, especially between the centromeric region of class I and the telomeric region of class III, under the strong LD with HLA-B*52.

The impact of white matter fiber orientation in single-acquisition quantitative susceptibility mapping.

PubMed

Lancione, Marta; Tosetti, Michela; Donatelli, Graziella; Cosottini, Mirco; Costagli, Mauro

2017-11-01

The aim of this work was to assess the impact of tissue structural orientation on quantitative susceptibility mapping (QSM) reliability, and to provide a criterion to identify voxels in which measures of magnetic susceptibility (χ) are most affected by spatial orientation effects. Four healthy volunteers underwent 7-T magnetic resonance imaging (MRI). Multi-echo, gradient-echo sequences were used to obtain quantitative maps of frequency shift (FS) and χ. Information from diffusion tensor imaging (DTI) was used to investigate the relationship between tissue orientation and FS measures and QSM. After sorting voxels on the basis of their fractional anisotropy (FA), the variations in FS and χ values over tissue orientation were measured. Using a K-means clustering algorithm, voxels were separated into two groups depending on the variability of measures within each FA interval. The consistency of FS and QSM values, observed at low FA, was disrupted for FA > 0.6. The standard deviation of χ measured at high FA (0.0103 ppm) was nearly five times that at low FA (0.0022 ppm). This result was consistent through data across different head positions and for different brain regions considered separately, which confirmed that such behavior does not depend on structures with different bulk susceptibility oriented along particular angles. The reliability of single-orientation QSM anticorrelates with local FA. QSM provides replicable values with little variability in brain regions with FA < 0.6, but QSM should be interpreted cautiously in major and coherent fiber bundles, which are strongly affected by structural anisotropy and magnetic susceptibility anisotropy. Copyright © 2017 John Wiley & Sons, Ltd.

PXK locus in systemic lupus erythematosus: fine mapping and functional analysis reveals novel susceptibility gene ABHD6.

PubMed

Oparina, Nina Y; Delgado-Vega, Angelica M; Martinez-Bueno, Manuel; Magro-Checa, César; Fernández, Concepción; Castro, Rafaela Ortega; Pons-Estel, Bernardo A; D'Alfonso, Sandra; Sebastiani, Gian Domenico; Witte, Torsten; Lauwerys, Bernard R; Endreffy, Emoke; Kovács, László; Escudero, Alejandro; López-Pedrera, Chary; Vasconcelos, Carlos; da Silva, Berta Martins; Frostegård, Johan; Truedsson, Lennart; Martin, Javier; Raya, Enrique; Ortego-Centeno, Norberto; de Los Angeles Aguirre, Maria; de Ramón Garrido, Enrique; Palma, María-Jesús Castillo; Alarcon-Riquelme, Marta E; Kozyrev, Sergey V

2015-03-01

To perform fine mapping of the PXK locus associated with systemic lupus erythematosus (SLE) and study functional effects that lead to susceptibility to the disease. Linkage disequilibrium (LD) mapping was conducted by using 1251 SNPs (single nucleotide polymorphism) covering a 862 kb genomic region on 3p14.3 comprising the PXK locus in 1467 SLE patients and 2377 controls of European origin. Tag SNPs and genotypes imputed with IMPUTE2 were tested for association by using SNPTEST and PLINK. The expression QTLs data included three independent datasets for lymphoblastoid cells of European donors: HapMap3, MuTHER and the cross-platform eQTL catalogue. Correlation analysis of eQTLs was performed using Vassarstats. Alternative splicing for the PXK gene was analysed on mRNA from PBMCs. Fine mapping revealed long-range LD (>200 kb) extended over the ABHD6, RPP14, PXK, and PDHB genes on 3p14.3. The highly correlated variants tagged an SLE-associated haplotype that was less frequent in the patients compared with the controls (OR=0.89, p=0.00684). A robust correlation between the association with SLE and enhanced expression of ABHD6 gene was revealed, while neither expression, nor splicing alterations associated with SLE susceptibility were detected for PXK. The SNP allele frequencies as well as eQTL pattern analysed in the CEU and CHB HapMap3 populations indicate that the SLE association and the effect on ABHD6 expression are specific to Europeans. These results confirm the genetic association of the locus 3p14.3 with SLE in Europeans and point to the ABHD6 and not PXK, as the major susceptibility gene in the region. We suggest a pathogenic mechanism mediated by the upregulation of ABHD6 in individuals carrying the SLE-risk variants. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Advances in Landslide Hazard Forecasting: Evaluation of Global and Regional Modeling Approach

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia B.; Adler, Robert; Hone, Yang; Kumar, Sujay; Peters-Lidard, Christa; Lerner-Lam, Arthur

2010-01-01

A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that exhibit a high potential for landslide activity by combining a calculation of landslide susceptibility with satellite-derived rainfall estimates. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale landslide forecasting efforts, it requires several modifications before it can be fully realized as an operational tool. The evaluation finds that the landslide forecasting may be more feasible at a regional scale. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and forecasting at the regional scale. This case study uses a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America: Guatemala, Honduras, EI Salvador and Nicaragua. A regional susceptibility map is calculated from satellite and surface datasets using a statistical methodology. The susceptibility map is tested with a regional rainfall intensity-duration triggering relationship and results are compared to global algorithm framework for the Hurricane Mitch event. The statistical results suggest that this regional investigation provides one plausible way to approach some of the data and resolution issues identified in the global assessment, providing more realistic landslide forecasts for this case study. Evaluation of landslide hazards for this extreme event helps to identify several potential improvements of the algorithm framework, but also highlights several remaining challenges for the algorithm assessment, transferability and performance accuracy. Evaluation challenges include representation errors from comparing susceptibility maps of different spatial resolutions, biases in event-based landslide inventory data, and limited nonlandslide event data for more comprehensive evaluation. Additional factors that may improve algorithm performance accuracy include incorporating additional triggering factors such as tectonic activity, anthropogenic impacts and soil moisture into the algorithm calculation. Despite these limitations, the methodology presented in this regional evaluation is both straightforward to calculate and easy to interpret, making results transferable between regions and allowing findings to be placed within an inter-comparison framework. The regional algorithm scenario represents an important step in advancing regional and global-scale landslide hazard assessment and forecasting.

The Rock Engineering System (RES) applied to landslide susceptibility zonation of the northeastern flank of Etna: methodological approach and results

NASA Astrophysics Data System (ADS)

Apuani, Tiziana; Corazzato, Claudia

2015-04-01

Ground deformations in the northeastern flank of Etna are well known. Despite only a few landslide events have been documented, these have significantly involved and damaged lifelines and buildings. These events are mainly related to the activity of the volcano-tectonic structures and associated seismicity, as in the case of the 2002 reactivation of the Presa landslide during an increased activity of the Pernicana fault system. In order to highlight the areal distribution of potentially unstable slopes based on a detailed, site-specific study of the factors responsible for landslide, and to ultimately contribute to risk management, a landslide susceptibility analysis of the northeastern flank of Etna in the Pernicana area was carried out, and a susceptibility map at 1:10.000 scale was produced, extending over an area of 168 km2. Different methods are proposed in the literature to obtain the regional distribution of potentially unstable slopes, depending on the problem scale, the slope dynamic evolution in the geological context, and the availability of data. Among semi-quantitative approaches, the present research combines the Rock Engineering System (RES) methodology with parameter zonation mapping in a GIS environment. The RES method represents a structured approach to manage a high number of interacting factors involved in the instability problem. A numerically coded, site-specific interaction matrix (IM) analyzes the cause-effect relationship in these factors, and calculates the degree of interactivity of each parameter, normalized by the overall interactivity of the system (weight factor). In the specific Etna case, the considered parameters are: slope attitude, lithotechnical properties (lithology, structural complexity, soil and rock mass quality), land use, tectonic structures, seismic activity (horizontal acceleration) and hydrogeological conditions (groundwater and drainage). Thematic maps are prepared at 1:10.000 scale for each of these parameters, and instability-related numerical ratings are assigned to classes. An instability index map is then produced by assigning, to each areal elementary cell (in our case a 10 m pixel), the sum of the products of each weight factor to the normalized parameter rating coming from each input zonation map. This map is then opportunely classified in landslide susceptibility classes (expressed as a percentage), enabling to discriminate areas prone to instability. Overall, the study area is characterized by a low propensity to slope instability. Few areas have an instability index of more than 45% of the theoretical maximum imposed by the matrix. These are located in the few steep slopes associated with active faults, and strongly depending on the seismic activity. Some other areas correspond to limited outcrops characterized by significantly reduced lithotechnical properties (low shear strength). The produced susceptibility map combines the application of the RES with the parameter zonation, following methodology which had never been applied up to now in in active volcanic environments. The comparison of the results with the ground deformation evidence coming from monitoring networks suggests the validity of the approach.

Assessments on landslide susceptibility in the Tseng-wen reservoir watershed, Taiwan

NASA Astrophysics Data System (ADS)

Chen, Yu-Chin; Chen, Yung-Chau; Chen, Wen-Fu

2014-05-01

Typhoon Morakot under the strong influence of southwestern monsoon wind struck Taiwan on 8 August 2009, and dumped record-breaking rains in southern Taiwan. It triggered enormous landslides in mountains and severe flooding in low-lying areas. In addition, it destroyed or damaged houses, agricultural fields, roads, bridges, and other infrastructure facilities, causing massive economic loss and, more tragically, human casualties. In order to evaluate landslide hazard and risk assessment, it is important to understand the potential sites of landslide and their spatial distribution. Multi-temporal satellite images and geo-spatial data are used to build landslide susceptibility map for the post-disaster in the Tseng-wen reservoir watershed in this research. Elevation, slope, aspect, NDVI (normalized differential vegetation index), relief, roughness, distance to river, and distance to road are the considered factors for estimating landslide susceptibility. Maximum hourly rainfall and total rainfall, accompanied with typhoon event, are selected as the trigger factors of landslide events. Logistic regression analysis is adopted as the statistical method to model landslide susceptibility. The assessed susceptibility is represented in 4 levels which are high, high-intermediate, intermediate, and low level, respectively. Landslide spatial distribution can be depicted as a landslide susceptibility map with respect to each considered influence factors for a specified susceptible level. The landslide areas are about 358 ha and 1,485 ha before and after typhoon Morakot. The new landslide area, induced by typhoon Morakot, is as almost 4 times as the landslide area before typhoon Morakot. In addition, there is about 44.56% landslide area elevation ranging from 500m to 1000m and about 57.22% average slope ranging from 30° to 45° of landslide area. Furthermore, the devastating landslides were happened at those sites close to rivers, exposed area, and area with big land cover change (high human development). Among considered factors, slope, distance to river, NDVI, and maximum hourly rainfall are the major influence factors for landslide susceptibility. The results show that the accuracy of predicted landslide area is 74.74% and AUC is 0.82 corresponding to typhoon Morakot. Comparing model predicted with actual landslide areas, it shows that the predicted accuracy is 93% for high or high-intermediate level landslide area. It suggests that a landslide susceptibility map, depicted by this assessment model, is applicable on landslide prediction.

A Geomorphological Approach To Landslide Susceptibility Assessment and Its Application To The Virginio River Basin

NASA Astrophysics Data System (ADS)

Casagli, N.; Catani, F.; Delmonaco, G.; Ermini, L.; Margottini, C.; Puglisi, C.

The present note presents the results of a research project aimed at developing a ge- omorphology based method for the assessment of landslide susceptibility. The work has been carried out in the Virginio River basin, a tributary of the Arno river with confluence located 20 km downstream from Florence (Italy). Starting from a detailed landslide inventory, essentially carried out by aerial-photograph survey, the first phase of the research was directed to geomorphic field investigations, aimed at determining those hillslope factors influencing landslide triggering. This analysis was carried out for a significant sub-sample of the inventoried landslides, allowing the distinction of the most relevant hillslope factors affecting each type of mass movement. Selected factors are a) the past slope gradient in which landslide originated, b) litology and 3) land use. Once defined as thematic vector data, these factors have been handled by GIS overlay mapping, allowing to single out, for the entire Virginio River basin, homogeneous domains (Unique Condition Units) that contain, for each landslide ty- pology, unique combinations of the selected hillslope factors. Those domains are the basic Terrain Units for the following phase of landslide susceptibility assessment and mapping. The hillslope factors not selected in the first phase, however playing an im- portant role in contributing to the activation of the mass movements, have been taken into account for the definition of a landslide susceptibility function. This function is es- sentially a logic function based on the presence/absence of preparatory factors within the previous selected Unique Condition Terrain Units. The final mapping of the ar- eas characterized by different landslide susceptibility levels was performed by vector and raster based GIS techniques on the basis of the number and rank of predisposing factors. These landslide susceptibility classes, defined by a logic and easily replicable function, can be useful in the decision making procedures connected with territorial planning.

Environmental Magnetism and Geochemical Properties of Urban Soils from Baton Rouge, Louisiana: Implications for Anthropogenic Pollution Monitoring

NASA Astrophysics Data System (ADS)

Richter, C.; Taylor, D.; Schramm, W.; Day, L.; Vedrines, H.

2016-12-01

Magnetic properties (susceptibility and SIRM) of urban soils have been shown to be very effective tracers of anthropogenic pollution. They provide a highly sensitive and easily obtainable measurement of the compositional changes of the mineral and chemical composition in soils. The main objective of this study is to detect the presence of magnetic anthropogenic particles related to environmental pollution by measuring the magnetic signature of soil samples and relating it to heavy metal concentrations obtained by XRF analysis. For this large-scale study carried out over the past eight years, we sampled an area of 260 km2 in and around Baton Rouge, Louisiana, with a total of 257 sites, 5140 individual susceptibility measurements obtained with a hand-held field probe, and 514 discrete samples for laboratory analysis of SIRM, susceptibility, and XRF analysis. In this area rural, industrial, metropolitan, and suburban settings exist in close proximity and allow for the direct comparison of results without significant changes in pedological, climatic, or the bedrock, which influence the magnetic properties. Contour maps and histograms indicate a strong correlation between the magnetic susceptibility, SIRM, and the environmental setting, with the mode of the susceptibility shifting from 0.006x10-3 SI in rural areas to 0.273x10-3 SI in the industrialized parts of the city. The industrialized western area of Baton Rouge especially shows significantly enhanced magnetic properties. For selected sites we determined the concentrations of Mo, Zr, Sr, Ba, U, Rb, Th, Pb, Au, Se, As, Hg, Zn, W, Cu, Cr, Ni, Co, Fe, and Mn with an XRF scanner. A linear correlation between magnetic susceptibility and U, Ba, Cr, Pb, Th, and Zn is statistically significant and suggests that anthropogenic input of heavy metals has a significant influence on magnetic properties. Detailed rock magnetic, geochemical, and statistical analysis will be presented and used, together with soil maps and land-usage maps, to characterize the anthropogenic impact on soils and the shallow subsurface.

Susceptibility Tensor Imaging (STI) of the Brain

PubMed Central

Li, Wei; Liu, Chunlei; Duong, Timothy Q.; van Zijl, Peter C.M.; Li, Xu

2016-01-01

Susceptibility tensor imaging (STI) is a recently developed MRI technique that allows quantitative determination of orientation-independent magnetic susceptibility parameters from the dependence of gradient echo signal phase on the orientation of biological tissues with respect to the main magnetic field. By modeling the magnetic susceptibility of each voxel as a symmetric rank-2 tensor, individual magnetic susceptibility tensor elements as well as the mean magnetic susceptibility (MMS) and magnetic susceptibility anisotropy (MSA) can be determined for brain tissues that would still show orientation dependence after conventional scalar-based quantitative susceptibility mapping (QSM) to remove such dependence. Similar to diffusion tensor imaging (DTI), STI allows mapping of brain white matter fiber orientations and reconstruction of 3D white matter pathways using the principal eigenvectors of the susceptibility tensor. In contrast to diffusion anisotropy, the main determinant factor of susceptibility anisotropy in brain white matter is myelin. Another unique feature of susceptibility anisotropy of white matter is its sensitivity to gadolinium-based contrast agents. Mechanistically, MRI-observed susceptibility anisotropy is mainly attributed to the highly ordered lipid molecules in myelin sheath. STI provides a consistent interpretation of the dependence of phase and susceptibility on orientation at multiple scales. This article reviews the key experimental findings and physical theories that led to the development of STI, its practical implementations, and its applications for brain research. PMID:27120169

Areas Susceptible to Irrigation-Induced Selenium Contamination of Water and Biota in the Western United States

USGS Publications Warehouse

Seiler, Ralph L.; Skorupa, Joseph P.; Peltz, Lorri A.

1999-01-01

The U.S. Department of the Interior (DOI) studied contamination induced by irrigation drainage in 26 areas of the Western United States during 1986-95. Comprehensive compilation, synthesis, and evaluation of the data resulting from these studies were initiated by DOI in 1992. Soils and ground water in irrigated areas of the West can contain high concentrations of selenium because of (1) residual selenium from the soil's parent rock beneath irrigated land; (2) selenium derived from rocks in mountains upland from irrigated land by erosion and transport along local drainages, and (3) selenium brought into the area in surface water imported for irrigation. Application of irrigation water to seleniferous soils can dissolve and mobilize selenium and create hydraulic gradients that cause the discharge of seleniferous ground water into irrigation drains. Given a source of selenium, the magnitude of selenium contamination in drainage-affected aquatic ecosystems is strongly related to the aridity of the area and the presence of terminal lakes and ponds. Marine sedimentary rocks and deposits of Late Cretaceous or Tertiary age are generally seleniferous in the Western United States. Depending on their origin and history, some Tertiary continental sedimentary deposits also are seleniferous. Irrigation of areas associated with these rocks and deposits can result in concentrations of selenium in water that exceed criteria for the protection of freshwater aquatic life. Geologic and climatic data for the Western United States were evaluated and incorporated into a geographic information system (GIS) to produce a map identifying areas susceptible to irrigation-induced selenium contamination. Land is considered susceptible where a geologic source of selenium is in or near the area and where the evaporation rate is more than 2.5 times the precipitation rate. In the Western United States, about 160,000 square miles of land, which includes about 4,100 square miles (2.6 million acres) of land irrigated for agriculture, has been identified as being susceptible. Biological data were used to evaluate the reliability of the map. In 12 of DOl's 26 study areas, concentrations of selenium measured in bird eggs were elevated sufficiently to significantly reduce hatchability of the eggs. The GIS map identifies 9 of those 12 areas. Deformed bird embryos having classic symptoms of selenium toxicosis were found in four of the study areas, and the map identifies all four as susceptible to irrigation-induced selenium contamination.

Mapping and Sequencing of the Canine NRAMP1 Gene and Identification of Mutations in Leishmaniasis-Susceptible Dogs

PubMed Central

Altet, Laura; Francino, Olga; Solano-Gallego, Laia; Renier, Corinne; Sánchez, Armand

2002-01-01

The NRAMP1 gene (Slc11a1) encodes an ion transporter protein involved in the control of intraphagosomal replication of parasites and in macrophage activation. It has been described in mice as the determinant of natural resistance or susceptibility to infection with antigenically unrelated pathogens, including Leishmania. Our aims were to sequence and map the canine Slc11a1 gene and to identify mutations that may be associated with resistance or susceptibility to Leishmania infection. The canine Slc11a1 gene has been mapped to dog chromosome CFA37 and covers 9 kb, including a 700-bp promoter region, 15 exons, and a polymorphic microsatellite in intron 1. It encodes a 547-amino-acid protein that has over 87% identity with the Slc11a1 proteins of different mammalian species. A case-control study with 33 resistant and 84 susceptible dogs showed an association between allele 145 of the microsatellite and susceptible dogs. Sequence variant analysis was performed by direct sequencing of the cDNA and the promoter region of four unrelated beagles experimentally infected with Leishmania infantum to search for possible functional mutations. Two of the dogs were classified as susceptible and the other two were classified as resistant based on their immune responses. Two important mutations were found in susceptible dogs: a G-rich region in the promoter that was common to both animals and a complete deletion of exon 11, which encodes the consensus transport motif of the protein, in the unique susceptible dog that needed an additional and prolonged treatment to avoid continuous relapses. A study with a larger dog population would be required to prove the association of these sequence variants with disease susceptibility. PMID:12010961

A multi-annual landslide inventory for the assessment of shallow landslide susceptibility - Two test cases in Vorarlberg, Austria

NASA Astrophysics Data System (ADS)

Zieher, Thomas; Perzl, Frank; Rössel, Monika; Rutzinger, Martin; Meißl, Gertraud; Markart, Gerhard; Geitner, Clemens

2016-04-01

Geomorphological landslide inventories provide crucial input data for any study on the assessment of landslide susceptibility, hazard or risk. Several approaches for assessing landslide susceptibility have been proposed to identify areas particularly vulnerable to this natural hazard. What they have in common is the need for data of observed landslides. Therefore the first step of any study on landslide susceptibility is usually the compilation of a geomorphological landslide inventory using a geographical information system. Recent research has proved the feasibility of orthophoto interpretation for the preparation of an inventory aimed at the delineation of landslides with the use of distinctive signs in the imagery data. In this study a multi-annual landslide inventory focusing on shallow landslides (i.e. translational soil slides of 0-2 m in depth) was compiled for two study areas in Vorarlberg (Austria) from the interpretation of nine orthophoto series. In addition, derivatives of two generations of airborne laser scanning data aided the mapping procedure. Landslide scar areas were delineated on the basis of a high-resolution differential digital terrain model. The derivation of landslide volumes, depths and depth-to-length ratios are discussed. Results show that most mapped landslides meet the definition of a shallow landslide. The inventory therefore provides the data basis for the assessment of shallow landslide susceptibility and allows for the application of various modelling techniques.

Evaluating and comparing methods of sinkhole susceptibility mapping in the Ebro Valley evaporite karst (NE Spain)

NASA Astrophysics Data System (ADS)

Galve, J. P.; Gutiérrez, F.; Remondo, J.; Bonachea, J.; Lucha, P.; Cendrero, A.

2009-10-01

Multiple sinkhole susceptibility models have been generated in three study areas of the Ebro Valley evaporite karst (NE Spain) applying different methods (nearest neighbour distance, sinkhole density, heuristic scoring system and probabilistic analysis) for each sinkhole type separately (cover collapse sinkholes, cover and bedrock collapse sinkholes and cover and bedrock sagging sinkholes). The quantitative and independent evaluation of the predictive capability of the models reveals that: (1) The most reliable susceptibility models are those derived from the nearest neighbour distance and sinkhole density. These models can be generated in a simple and rapid way from detailed geomorphological maps. (2) The reliability of the nearest neighbour distance and density models is conditioned by the degree of clustering of the sinkholes. Consequently, the karst areas in which sinkholes show a higher clustering are a priori more favourable for predicting new occurrences. (3) The predictive capability of the best models obtained in this research is significantly higher (12.5-82.5%) than that of the heuristic sinkhole susceptibility model incorporated into the General Urban Plan for the municipality of Zaragoza. Although the probabilistic approach provides lower quality results than the methods based on sinkhole proximity and density, it helps to identify the most significant factors and select the most effective mitigation strategies and may be applied to model susceptibility in different future scenarios.

Maps of Quaternary Deposits and Liquefaction Susceptibility in the Central San Francisco Bay Region, California

USGS Publications Warehouse

Witter, Robert C.; Knudsen, Keith L.; Sowers, Janet M.; Wentworth, Carl M.; Koehler, Richard D.; Randolph, Carolyn E.; Brooks, Suzanna K.; Gans, Kathleen D.

2006-01-01

This report presents a map and database of Quaternary deposits and liquefaction susceptibility for the urban core of the San Francisco Bay region. It supercedes the equivalent area of U.S. Geological Survey Open-File Report 00-444 (Knudsen and others, 2000), which covers the larger 9-county San Francisco Bay region. The report consists of (1) a spatial database, (2) two small-scale colored maps (Quaternary deposits and liquefaction susceptibility), (3) a text describing the Quaternary map and liquefaction interpretation (part 3), and (4) a text introducing the report and describing the database (part 1). All parts of the report are digital; part 1 describes the database and digital files and how to obtain them by downloading across the internet. The nine counties surrounding San Francisco Bay straddle the San Andreas fault system, which exposes the region to serious earthquake hazard (Working Group on California Earthquake Probabilities, 1999). Much of the land adjacent to the Bay and the major rivers and streams is underlain by unconsolidated deposits that are particularly vulnerable to earthquake shaking and liquefaction of water-saturated granular sediment. This new map provides a consistent detailed treatment of the central part of the 9-county region in which much of the mapping of Open-File Report 00-444 was either at smaller (less detailed) scale or represented only preliminary revision of earlier work. Like Open-File Report 00-444, the current mapping uses geomorphic expression, pedogenic soils, inferred depositional environments, and geologic age to define and distinguish the map units. Further scrutiny of the factors controlling liquefaction susceptibility has led to some changes relative to Open-File Report 00-444: particularly the reclassification of San Francisco Bay mud (Qhbm) to have only MODERATE susceptibility and the rating of artificial fills according to the Quaternary map units inferred to underlie them (other than dams - adf). The two colored maps provide a regional summary of the new mapping at a scale of 1:200,000, a scale that is sufficient to show the general distribution and relationships of the map units but not to distinguish the more detailed elements that are present in the database. The report is the product of cooperative work by the National Earthquake Hazards Reduction Program (NEHRP) and National Cooperative Geologic Mapping Program of the U.S. Geological Survey, William Lettis and & Associates, Inc. (WLA), and the California Geological Survey. An earlier version was submitted to the U.S. Geological Survey by WLA as a final report for a NEHRP grant (Witter and others, 2005). The mapping has been carried out by WLA geologists under contract to the NEHRP Earthquake Program (Grant 99-HQ-GR-0095) and by the California Geological Survey.

Comprehensive Clinical Phenotyping & Genetic Mapping for the Discovery of Autism Susceptibility Genes

DTIC Science & Technology

2012-12-05

Bisgaier J, Levinson D, Cutts DB, & Rhodes KV., (2011) Access to autism evaluation appointments with developmental-behavioral and neurodevelopmental ...W403 Columbus, OH 43205 Final Report Comprehensive Clinical Phenotyping & Genetic Mapping for the Discovery of Autism Susceptibility Genes...QFOXGHDUHDFRGH 1.0 Summary In 2006, the Central Ohio Registry for Autism (CORA) was initiated as a collaboration between Wright-Patterson Air

A Novel Phytophthora sojae Resistance Rps12 Gene Mapped to a Genomic Region That Contains Several Rps Genes.

PubMed

Sahoo, Dipak K; Abeysekara, Nilwala S; Cianzio, Silvia R; Robertson, Alison E; Bhattacharyya, Madan K

2017-01-01

Phytophthora sojae Kaufmann and Gerdemann, which causes Phytophthora root rot, is a widespread pathogen that limits soybean production worldwide. Development of Phytophthora resistant cultivars carrying Phytophthora resistance Rps genes is a cost-effective approach in controlling this disease. For this mapping study of a novel Rps gene, 290 recombinant inbred lines (RILs) (F7 families) were developed by crossing the P. sojae resistant cultivar PI399036 with the P. sojae susceptible AR2 line, and were phenotyped for responses to a mixture of three P. sojae isolates that overcome most of the known Rps genes. Of these 290 RILs, 130 were homozygous resistant, 12 heterzygous and segregating for Phytophthora resistance, and 148 were recessive homozygous and susceptible. From this population, 59 RILs homozygous for Phytophthora sojae resistance and 61 susceptible to a mixture of P. sojae isolates R17 and Val12-11 or P7074 that overcome resistance encoded by known Rps genes mapped to Chromosome 18 were selected for mapping novel Rps gene. A single gene accounted for the 1:1 segregation of resistance and susceptibility among the RILs. The gene encoding the Phytophthora resistance mapped to a 5.8 cM interval between the SSR markers BARCSOYSSR_18_1840 and Sat_064 located in the lower arm of Chromosome 18. The gene is mapped 2.2 cM proximal to the NBSRps4/6-like sequence that was reported to co-segregate with the Phytophthora resistance genes Rps4 and Rps6. The gene is mapped to a highly recombinogenic, gene-rich genomic region carrying several nucleotide binding site-leucine rich repeat (NBS-LRR)-like genes. We named this novel gene as Rps12, which is expected to be an invaluable resource in breeding soybeans for Phytophthora resistance.

Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China.

PubMed

Hong, Haoyuan; Tsangaratos, Paraskevas; Ilia, Ioanna; Liu, Junzhi; Zhu, A-Xing; Chen, Wei

2018-06-01

In China, floods are considered as the most frequent natural disaster responsible for severe economic losses and serious damages recorded in agriculture and urban infrastructure. Based on the international experience prevention of flood events may not be completely possible, however identifying susceptible and vulnerable areas through prediction models is considered as a more visible task with flood susceptibility mapping being an essential tool for flood mitigation strategies and disaster preparedness. In this context, the present study proposes a novel approach to construct a flood susceptibility map in the Poyang County, JiangXi Province, China by implementing fuzzy weight of evidence (fuzzy-WofE) and data mining methods. The novelty of the presented approach is the usage of fuzzy-WofE that had a twofold purpose. Firstly, to create an initial flood susceptibility map in order to identify non-flood areas and secondly to weight the importance of flood related variables which influence flooding. Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM) were implemented considering eleven flood related variables, namely: lithology, soil cover, elevation, slope angle, aspect, topographic wetness index, stream power index, sediment transport index, plan curvature, profile curvature and distance from river network. The efficiency of this new approach was evaluated using area under curve (AUC) which measured the prediction and success rates. According to the outcomes of the performed analysis, the fuzzy WofE-SVM model was the model with the highest predictive performance (AUC value, 0.9865) which also appeared to be statistical significant different from the other predictive models, fuzzy WofE-RF (AUC value, 0.9756) and fuzzy WofE-LR (AUC value, 0.9652). The proposed methodology and the produced flood susceptibility map could assist researchers and local governments in flood mitigation strategies. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantitative assessment of biliary stent artifacts on MR images: Potential implications for target delineation in radiotherapy.

PubMed

Gurney-Champion, Oliver J; Bruins Slot, Thijs; Lens, Eelco; van der Horst, Astrid; Klaassen, Remy; van Laarhoven, Hanneke W M; van Tienhoven, Geertjan; van Hooft, Jeanin E; Nederveen, Aart J; Bel, Arjan

2016-10-01

Biliary stents may cause susceptibility artifacts, gradient-induced artifacts, and radio frequency (RF) induced artifacts on magnetic resonance images, which can hinder accurate target volume delineation in radiotherapy. In this study, the authors investigated and quantified the magnitude of these artifacts for stents of different materials. Eight biliary stents made of nitinol, platinum-cored nitinol, stainless steel, or polyethylene from seven vendors, with different lengths (57-98 mm) and diameters (3.0-11.7 mm), were placed in a phantom. To quantify the susceptibility artifacts sequence-independently, ΔB0-maps and T2 ∗ -maps were acquired at 1.5 and 3 T. To study the effect of the gradient-induced artifacts at 3 T, signal decay in images obtained with maximum readout gradient-induced artifacts was compared to signal decay in reference scans. To quantify the RF induced artifacts at 3 T, B1-maps were acquired. Finally, ΔB0-maps and T2 ∗ -maps were acquired at 3 T of two pancreatic cancer patients who had received platinum-cored nitinol biliary stents. Outside the stent, susceptibility artifacts dominated the other artifacts. The stainless steel stent produced the largest susceptibility artifacts. The other stents caused decreased T2 ∗ up to 5.1 mm (1.5 T) and 8.5 mm (3 T) from the edge of the stent. For sequences with a higher bandwidth per voxel (1.5 T: BW vox > 275 Hz/voxel; 3 T: BW vox > 500 Hz/voxel), the B0-related susceptibility artifacts were negligible (<0.2 voxels). The polyethylene stent showed no artifacts. In vivo, the changes in B0 and T2 ∗ induced by the stent were larger than typical variations in B0 and T2 ∗ induced by anatomy when the stent was at an angle of 30° with the main magnetic field. Susceptibility artifacts were dominating over the other artifacts. The magnitudes of the susceptibility artifacts were determined sequence-independently. This method allows to include additional safety margins that ensure target irradiation.

Fine Mapping of the Barley Chromosome 6H Net Form Net Blotch Susceptibility Locus

PubMed Central

Richards, Jonathan; Chao, Shiaoman; Friesen, Timothy; Brueggeman, Robert

2016-01-01

Net form net blotch, caused by the necrotrophic fungal pathogen Pyrenophora teres f. teres, is a destructive foliar disease of barley with the potential to cause significant yield loss in major production regions throughout the world. The complexity of the host–parasite genetic interactions in this pathosystem hinders the deployment of effective resistance in barley cultivars, warranting a deeper understanding of the interactions. Here, we report on the high-resolution mapping of the dominant susceptibility locus near the centromere of chromosome 6H in the barley cultivars Rika and Kombar, which are putatively targeted by necrotrophic effectors from P. teres f. teres isolates 6A and 15A, respectively. Utilization of progeny isolates derived from a cross of P. teres f. teres isolates 6A × 15A harboring single major virulence loci (VK1, VK2, and VR2) allowed for the Mendelization of single inverse gene-for-gene interactions in a high-resolution population consisting of 2976 Rika × Kombar recombinant gametes. Brachypodium distachyon synteny was exploited to develop and saturate the susceptibility region with markers, delimiting it to ∼0.24 cM and a partial physical map was constructed. This genetic and physical characterization further resolved the dominant susceptibility locus, designated Spt1 (susceptibility to P. teres f. teres). The high-resolution mapping and cosegregation of the Spt1.R and Spt1.K gene/s indicates tightly linked genes in repulsion or alleles possibly targeted by different necrotrophic effectors. Newly developed barley genomic resources greatly enhance the efficiency of positional cloning efforts in barley, as demonstrated by the Spt1 fine mapping and physical contig identification reported here. PMID:27172206

Susceptibility analysis for slides and rockfall: an example from the Northern Calcareous Alps (Vorarlberg, Austria)

NASA Astrophysics Data System (ADS)

Ruff, Michael; Rohn, Joachim

2008-07-01

In this paper a tool for semi-quantitative susceptibility assessment at a regional scale is presented which is applicable at areas with complex geological setting. At a study area within the Northern Calcareous Alps geotechnical mappings were implemented into a Geographical Information System and analysed as grid data with a cell size of 25 m. The susceptibility to sliding and falling processes was considered according to five classes (very low, low, medium, high, very high). Susceptibility to sliding was analysed using an index method. The layers of lithology, bedding conditions, tectonic faults, slope angle, slope aspect, vegetation and erosion were combined iteratively. Dropout zones of rockfall material were determined with help of a Digital Elevation Model. The movement of rolling rock samples was modelled by a cost analysis of all potential rockfall trajectories. These trajectories were also divided into five susceptibility classes. The susceptibility maps are presented in a general way to be used by communities and spatial planners. Conflict areas of susceptibility and landuse were located and can be presented destinctively.

Utilisation de la teledetection, des SIG et de l'intelligence artificielle pour determiner le niveau de susceptibilite aux mouvements de terrain: Application dans les Andes de la Bolivie

NASA Astrophysics Data System (ADS)

Peloquin, Stephane

1999-11-01

The socio-economic impact of mass movements for our society is getting more and more serious. The loss of lives and economic losses are now ten times greater than they were at the beginning of the decade. In the hope of reducing these impacts, it is essential to adopt a preventive policy that will encourage mapping of mass movement susceptibility level (MMSL) in critical zones. However, this task is complex and only experts using present techniques can provide satisfactory results. To make possible the production of these maps by a larger number of individuals, we have developed an expert system called EXPERIM that uses remote sensing data and geographic information systems to facilitate the complex tasks without requiring the user to be highly competent in this field of study. This thesis presents the results obtained from a complete strategy developed for a region surrounding Cochabamba, Bolivia. The operational expert system prototype will soon be integrated within the watershed management program directed by the local executing organisation PROMIC. The knowledge acquisition and its expression in concrete terms constitute the principal axis of this research, while the results obtained are the heart of the EXPERIM expert system. These strategic steps aim to establish a knowledge base of data and rules that describe field conditions for each MMSL. We have been able to extract this information by using binary discriminant analysis of a MMSL map produced by an expert for a pilot zone called Cuenca Taquina, which is geoecologically representative of the 38 neighbouring watersheds. Using this technique, we were able to establish a sensitivity model that recreates the expert's map with a success rate of 89% and 78% when two or three MMS levels are used. Based on a detailed analysis of the susceptibility model it was evident that stability conditions are the result of the topographic, geologic and geomorphologic environments. The level of susceptibility was found to be independent of the vegetation condition. In order to apply the model to the surrounding watersheds, we integrated remotely sensed data within the spatial database to map the presence/absence of five essential geoecological units required by the susceptibility model. This was done using a hierarchical classification method. Three sensors were evaluated: Landsat, SPOT and RADARSAT. In the elaboration of this specific step, we evaluated the most efficient spectral band combinations within each image and between images for each of the five geoecological units. For each of the land cover types, the analysis shows that LANDSAT constitutes the most powerful sensor to map these units and that image fusion does not provide significantly better results when compared to the extra amount of work that this requires. Using remote sensing data instead of field data or airphotograph interpretation in watersheds where only topographic data are available decreases the level of accuracy by less than 10%.

Mapping of risk and susceptibility of shallow-landslide in the city of São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Listo, Fabrizio de Luiz Rosito; Carvalho Vieira, Bianca

2012-10-01

In the city of São Paulo, where about 11 million people live, landslides and flooding occur frequently, especially during the summer. These landslides cause the destruction of houses and urban equipment, economic damage, and the loss of lives. The number of areas threatened by landslides has been increasing each year. The objective of this article is to analyze the probability of risk and susceptibility to shallow landslides in the Limoeiro River basin, which is located at the head of the Aricanduva River basin, one of the main hydrographic basins in the city of São Paulo. To map areas of risk, we created a cadastral survey form to evaluate landslide risk in the field. Risk was categorized into four levels based on natural and anthropogenic factors: R1 (low risk), R2 (average risk), R3 (high risk), and R4 (very high risk). To analyze susceptibility to shallow landslides, we used the SHALSTAB (Shallow Landsliding Stability) mathematical model and calculated the Distribution Frequency (DF) of the susceptibility classes for the entire basin. Finally, we performed a joint analysis of the average Risk Concentration (RC) and Risk Potential (RP). We mapped 14 risk sectors containing approximately 685 at-risk homes, more than half of which presented a high (R3) or very high (R4) probability of risk to the population. In the susceptibility map, 41% of the area was classified as stable and 20% as unconditionally unstable. Although the latter category accounted a smaller proportion of the total area, it contained a concentration (RC) of 41% of the mapped risk areas with a risk potential (RP) of 12%. We found that the locations of areas predicted to be unstable by the model coincided with the risk areas mapped in the field. This combination of methods can be applied to evaluate the risk of shallow landslides in densely populated areas and can assist public managers in defining areas that are unstable and inappropriate for occupation.

Multirisk analysis along the Road 7, Mendoza Province, Argentina

NASA Astrophysics Data System (ADS)

Wick, Emmanuel; Baumann, Valérie; Michoud, Clément; Derron, Marc-Henri; Jaboyedoff, Michel; Rune Lauknes, Tom; Marengo, Hugo; Rosas, Mario

2010-05-01

The National Road 7 crosses Argentina from East to West, linking Buenos Aires to the Chile border. This road is an extremely important corridor crossing the Andes Cordillera, but it is exposed to numerous natural hazards, such as rockfalls, debris flows and snow avalanches. The study area is located in the Mendoza Province, between Potrerillos and Las Cuevas in the Chilean border. This study has for main goals to achieve a regional mapping of geohazards susceptibility along the Road 7 corridor using modern remote sensing and numerical modelling techniques completed by field investigations. The main topics are: - Detection and monitoring of deep-seated gravitational slope deformations by time-series satellite radar interferometry (InSAR) methods. The area of interest is mountainous with almost no vegetation permitting an optimized InSAR processing. Our results are based on applying the small-baseline subset (SBAS) method to a time-series of Envisat ASAR images. - Rockfalls susceptibility mapping is realized using statistical analysis of the slope angle distribution, including external knowledge on the geology and land cover, to detect the potential source areas (quantitative DEM analysis). The run-outs are assessed with numerical methods based on the shallow angle method with Conefall. A second propagation is performed using the alpha-beta methodology (3D numerical modelling) with RAS and is compared to the first one. - Debris flow susceptibility mapping is realized using DF-IGAR to detect starting and spreading areas. Slope, flow accumulations, contributive surfaces, plan curvature, geological and land use dataset are used. The spreading is simulated by a multiple flow algorithm (rules the path that the debris flow will follow) coupled to a run-out distance calculation (energy-based). - Snow avalanches susceptibility mapping is realized using DF-IGAR to map sources areas and propagations. To detect the sources areas, slope, altitude, land-use and minimum surfaces are needed. DF-IGAR simulates the spreading by means of the "Perla" methodology. Furthermore, RAS performs the spreading based on the "alpha-beta" method. All these methods are based on Aster and SRTM DEM (grid 30 m) and observations of both optical and radar satellite imagery (Aster, Quickbird, Worldview, Ikonos, Envisat ASAR) and aerial photographs. Several field campaigns are performed to calibrate the regional models with adapted parameters. Susceptibility maps of the entire area for rockfalls, debris flows and snow avalanches at a scale of 1:100'000 are created. Those maps and the field investigations are cross-checked to identify and prioritize hotspots. It appears that numerous road sectors are subject to highly active phenomena. Some mitigation works already exist but they are often under-dimensioned, inadequate or neglected. Recommendations for priority and realistic mitigation measures along the endangered road sectors identified are proposed.

The contribution of PSInSAR interferometry to landslide susceptibility assessment in weak rock-dominated areas

NASA Astrophysics Data System (ADS)

Oliveira, Sérgio C.; Zêzere, José L.; Catalão, João; Nico, Giovanni

2015-04-01

In the Grande da Pipa river basin (north of Lisbon, Portugal), 64% of the landslides inventoried occur on a particular weak rock lithological unit composed by clay and with sandstone intercalations, that is present in 58% of the study (Oliveira et al., 2014). Deep-seated slow moving rotational slides occur essentially on this lithological unit and are responsible for the major damages verified along roads and buildings in the study area. Within this context, landslide hazard assessment, is limited by two major constrains: (i) the slope instability signs may not be sufficiently clear and observable and consequently may not be correctly identifiable through traditional geomorphologic survey techniques and (ii) the non-timely recognition of precursor signs of instability both in landslides activated for the first time and in previously landslide-affected areas (landslide reactivation). To encompass these limitations, the Persistent Scatterer synthetic aperture radar interferometry technique is applied to a data set of 16 TerraSAR-X SAR images, from April of 2010 to March of 2011, available for a small test site of 12.5 square kilometers (Laje-Salema) located on south-central part of the study area. This work's specific objectives are the following: (i) to evaluate the capacity of the Persistent Scatterer displacement maps in assessing landslide susceptibility at the regional scale, and (ii) to assess the capacity of landslide susceptibility maps based on historical landslide inventories to predict the location of actual terrain displacement measured by the Persistent Scatterers technique. Landslide susceptibility was assessed for the test site using the Information Value bivariate statistical method and the susceptibility scores were exported to the Grande da Pipa river basin. The independent validation of the landslide susceptibility maps was made using the historical landslide inventory and the Persistent Scatterer displacement map. Results are compared by computing the respective Receiver Operator Characteristic curves and calculating the corresponding Area Under the Curve. Reference: Oliveira, S.C.; Zêzere, J.L.; Catalão, J.; Nico, G. (2014) - The contribution of PSInSAR interferometry to landslide hazard in weak rock-dominated areas. Landslides, DOI 10.1007/s10346-014-0522-9 This work was supported by the FCT - Portuguese Foundation for Science and Technology and is within the framework of the Project Pan-European and nation-wide landslide susceptibility assessment, European and Mediterranean Major Hazards Agreement (EUR-OPA). The first author was funded by a postdoctoral grant (SFRH/BPD/85827/2012) from the Portuguese Foundation for Science and Technology (FCT).

Landslide Hazard Assessment and Mapping in the Guil Catchment (Queyras, Southern French Alps): From Landslide Inventory to Susceptibility Modelling

NASA Astrophysics Data System (ADS)

Roulleau, Louise; Bétard, François; Carlier, Benoît; Lissak, Candide; Fort, Monique

2016-04-01

Landslides are common natural hazards in the Southern French Alps, where they may affect human lives and cause severe damages to infrastructures. As a part of the SAMCO research project dedicated to risk evaluation in mountain areas, this study focuses on the Guil river catchment (317 km2), Queyras, to assess landslide hazard poorly studied until now. In that area, landslides are mainly occasional, low amplitude phenomena, with limited direct impacts when compared to other hazards such as floods or snow avalanches. However, when interacting with floods during extreme rainfall events, landslides may have indirect consequences of greater importance because of strong hillslope-channel connectivity along the Guil River and its tributaries (i.e. positive feedbacks). This specific morphodynamic functioning reinforces the need to have a better understanding of landslide hazards and their spatial distribution at the catchment scale to prevent local population from disasters with multi-hazard origin. The aim of this study is to produce a landslide susceptibility mapping at 1:50 000 scale as a first step towards global estimation of landslide hazard and risk. The three main methodologies used for assessing landslide susceptibility are qualitative (i.e. expert opinion), deterministic (i.e. physics-based models) and statistical methods (i.e. probabilistic models). Due to the rapid development of geographical information systems (GIS) during the last two decades, statistical methods are today widely used because they offer a greater objectivity and reproducibility at large scales. Among them, multivariate analyses are considered as the most robust techniques, especially the logistic regression method commonly used in landslide susceptibility mapping. However, this method like others is strongly dependent on the accuracy of the input data to avoid significant errors in the final results. In particular, a complete and accurate landslide inventory is required before the modelling. The methodology used in our study includes five main steps: (i) a landslide inventory was compiled through extraction of landslide occurrences in existing national databases (BDMvt, RTM), photointerpretation of aerial photographs and extensive field surveys; (ii) the main predisposing factors were identified and implemented as digital layers into a GIS together with the landslide inventory map, thus constituting the predictive variables to introduce into the model; (iii) a logistic regression model was applied to analyze the spatial and mathematical relationships between the response variable (i.e. absence/presence of landslides) and the set of predictive variables (i.e. predisposing factors), after a selection procedure based on statistical tests (χ2-test and Cramer's V coefficient); (iv) an evaluation of the model performance and quality results was conducted using a validation strategy based on ROC curve and AUC analyses; (v) a final susceptibility map in four classes was proposed using a discretization method based on success/prediction rate curves. The results of the susceptibility modelling were finally interpreted and discussed in the light of what was previously known about landslide occurrence and triggering in the study area. The major influence of the distance-to-streams variable on the model confirms the strong hillslope-channel coupling observed empirically during rainfall-induced landslide events.

Thalamic white matter in multiple sclerosis: A combined diffusion-tensor imaging and quantitative susceptibility mapping study.

PubMed

Bergsland, Niels; Schweser, Ferdinand; Dwyer, Michael G; Weinstock-Guttman, Bianca; Benedict, Ralph H B; Zivadinov, Robert

2018-06-19

Thalamic white matter (WM) injury in multiple sclerosis (MS) remains relatively poorly understood. Combining multiple imaging modalities, sensitive to different tissue properties, may aid in further characterizing thalamic damage. Forty-five MS patients and 17 demographically-matched healthy controls (HC) were scanned with 3T MRI to obtain quantitative measures of diffusivity and magnetic susceptibility. Participants underwent cognitive evaluation with the Brief International Cognitive Assessment for Multiple Sclerosis battery. Tract-based spatial statistics identified thalamic WM. Non-parametric combination (NPC) analysis was used to perform joint inference on fractional anisotropy (FA), mean diffusivity (MD) and magnetic susceptibility measures. The association of surrounding WM lesions and thalamic WM pathology was investigated with lesion probability mapping. Compared to HCs, the greatest extent of thalamic WM damage was reflected by the combination of increased MD and decreased magnetic susceptibility (63.0% of thalamic WM, peak p = .001). Controlling for thalamic volume resulted in decreased FA and magnetic susceptibility (34.1%, peak p = .004) as showing the greatest extent. In MS patients, the most widespread association with information processing speed was found with the combination of MD and magnetic susceptibility (67.6%, peak p = .0005), although this was not evident after controlling for thalamic volume. For memory measures, MD alone yielded the most widespread associations (45.9%, peak p = .012 or 76.7%, peak p = .001), even after considering thalamic volume, albeit with smaller percentages. White matter lesions were related to decreased FA (peak p = .0063) and increased MD (peak p = .007), but not magnetic susceptibility, of thalamic WM. Our study highlights the complex nature of thalamic pathology in MS. © 2018 Wiley Periodicals, Inc.

Simultaneous MR quantification of hepatic fat content, fatty acid composition, transverse relaxation time and magnetic susceptibility for the diagnosis of non-alcoholic steatohepatitis.

PubMed

Leporq, B; Lambert, S A; Ronot, M; Vilgrain, V; Van Beers, B E

2017-10-01

Non-alcoholic steatohepatitis (NASH) is characterized at histology by steatosis, hepatocyte ballooning and inflammatory infiltrates, with or without fibrosis. Although diamagnetic material in fibrosis and inflammation can be detected with quantitative susceptibility imaging, fatty acid composition changes in NASH relative to simple steatosis have also been reported. Therefore, our aim was to develop a single magnetic resonance (MR) acquisition and post-processing scheme for the diagnosis of steatohepatitis by the simultaneous quantification of hepatic fat content, fatty acid composition, T 2 * transverse relaxation time and magnetic susceptibility in patients with non-alcoholic fatty liver disease. MR acquisition was performed at 3.0 T using a three-dimensional, multi-echo, spoiled gradient echo sequence. Phase images were unwrapped to compute the B 0 field inhomogeneity (ΔB 0 ) map. The ΔB 0 -demodulated real part images were used for fat-water separation, T 2 * and fatty acid composition quantification. The external and internal fields were separated with the projection onto dipole field method. Susceptibility maps were obtained after dipole inversion from the internal field map with single-orientation Bayesian regularization including spatial priors. Method validation was performed in 32 patients with biopsy-proven, non-alcoholic fatty liver disease from which 12 had simple steatosis and 20 NASH. Liver fat fraction and T 2 * did not change significantly between patients with simple steatosis and NASH. In contrast, the saturated fatty acid fraction increased in patients with NASH relative to patients with simple steatosis (48 ± 2% versus 44 ± 4%; p < 0.05) and the magnetic susceptibility decreased (-0.30 ± 0.27 ppm versus 0.10 ± 0.14 ppm; p < 0.001). The area under the receiver operating characteristic curve for magnetic susceptibility as NASH marker was 0.91 (95% CI: 0.79-1.0). Simultaneous MR quantification of fat content, fatty acid composition, T 2 * and magnetic susceptibility is feasible in the liver. Our preliminary results suggest that quantitative susceptibility imaging has a high diagnostic performance for the diagnosis of NASH. Copyright © 2017 John Wiley & Sons, Ltd.

Alkylation Damage by Lipid Electrophiles Targets Functional Protein Systems*

PubMed Central

Codreanu, Simona G.; Ullery, Jody C.; Zhu, Jing; Tallman, Keri A.; Beavers, William N.; Porter, Ned A.; Marnett, Lawrence J.; Zhang, Bing; Liebler, Daniel C.

2014-01-01

Protein alkylation by reactive electrophiles contributes to chemical toxicities and oxidative stress, but the functional impact of alkylation damage across proteomes is poorly understood. We used Click chemistry and shotgun proteomics to profile the accumulation of proteome damage in human cells treated with lipid electrophile probes. Protein target profiles revealed three damage susceptibility classes, as well as proteins that were highly resistant to alkylation. Damage occurred selectively across functional protein interaction networks, with the most highly alkylation-susceptible proteins mapping to networks involved in cytoskeletal regulation. Proteins with lower damage susceptibility mapped to networks involved in protein synthesis and turnover and were alkylated only at electrophile concentrations that caused significant toxicity. Hierarchical susceptibility of proteome systems to alkylation may allow cells to survive sublethal damage while protecting critical cell functions. PMID:24429493

PSF mapping-based correction of eddy-current-induced distortions in diffusion-weighted echo-planar imaging.

PubMed

In, Myung-Ho; Posnansky, Oleg; Speck, Oliver

2016-05-01

To accurately correct diffusion-encoding direction-dependent eddy-current-induced geometric distortions in diffusion-weighted echo-planar imaging (DW-EPI) and to minimize the calibration time at 7 Tesla (T). A point spread function (PSF) mapping based eddy-current calibration method is newly presented to determine eddy-current-induced geometric distortions even including nonlinear eddy-current effects within the readout acquisition window. To evaluate the temporal stability of eddy-current maps, calibration was performed four times within 3 months. Furthermore, spatial variations of measured eddy-current maps versus their linear superposition were investigated to enable correction in DW-EPIs with arbitrary diffusion directions without direct calibration. For comparison, an image-based eddy-current correction method was additionally applied. Finally, this method was combined with a PSF-based susceptibility-induced distortion correction approach proposed previously to correct both susceptibility and eddy-current-induced distortions in DW-EPIs. Very fast eddy-current calibration in a three-dimensional volume is possible with the proposed method. The measured eddy-current maps are very stable over time and very similar maps can be obtained by linear superposition of principal-axes eddy-current maps. High resolution in vivo brain results demonstrate that the proposed method allows more efficient eddy-current correction than the image-based method. The combination of both PSF-based approaches allows distortion-free images, which permit reliable analysis in diffusion tensor imaging applications at 7T. © 2015 Wiley Periodicals, Inc.

Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy

NASA Astrophysics Data System (ADS)

Wang, Liang-Jie; Sawada, Kazuhide; Moriguchi, Shuji

2013-08-01

Several mathematical models are used to predict the spatial distribution characteristics of landslides to mitigate damage caused by landslide disasters. Although some studies have achieved excellent results around the world, few studies take the inter-relationship of the selected points (training points) into account. In this paper, we present the Fuzzy c-means (FCM) algorithm as an optimal method for choosing the appropriate input landslide points as training data. Based on different combinations of the Fuzzy exponent (m) and the number of clusters (c), five groups of sampling points were derived from formal seed cells points and applied to analyze the landslide susceptibility in Mizunami City, Gifu Prefecture, Japan. A logistic regression model is applied to create the models of the relationships between landslide-conditioning factors and landslide occurrence. The pre-existing landslide bodies and the area under the relative operative characteristic (ROC) curve were used to evaluate the performance of all the models with different m and c. The results revealed that Model no. 4 (m=1.9, c=4) and Model no. 5 (m=1.9, c=5) have significantly high classification accuracies, i.e., 90.0%. Moreover, over 30% of the landslide bodies were grouped under the very high susceptibility zone. Otherwise, Model no. 4 and Model no. 5 had higher area under the ROC curve (AUC) values, which were 0.78 and 0.79, respectively. Therefore, Model no. 4 and Model no. 5 offer better model results for landslide susceptibility mapping. Maps derived from Model no. 4 and Model no. 5 would offer the local authorities crucial information for city planning and development.

Mapping the magnonic landscape in patterned magnetic structures

NASA Astrophysics Data System (ADS)

Davies, C. S.; Poimanov, V. D.; Kruglyak, V. V.

2017-09-01

We report the development of a hybrid numerical/analytical model capable of mapping the spatially varying distributions of the local ferromagnetic resonance (FMR) frequency and dynamic magnetic susceptibility in a wide class of patterned and compositionally modulated magnetic structures. Starting from the numerically simulated static micromagnetic state, the magnetization is deliberately deflected orthogonally to its equilibrium orientation, and the magnetic fields generated in response to this deflection are evaluated using micromagnetic software. This allows us to calculate the elements of the effective demagnetizing tensor, which are then used within a linear analytical formalism to map the local FMR frequency and dynamic magnetic susceptibility. To illustrate the typical results that one can obtain using this model, we analyze three micromagnetic systems boasting nonuniformity in either one or two dimensions, and successfully explain the spin-wave emission observed in each case, demonstrating the ubiquitous nature of the Schlömann excitation mechanism underpinning the observations. Finally, the developed model of local FMR frequency can be used to explain how spin waves could be confined and steered using magnetic nonuniformities of various origins, rendering it a powerful tool for the mapping of the graded magnonic index in magnonics.

Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms

PubMed Central

Horikoshi, Momoko; Pasquali, Lorenzo; Wiltshire, Steven; Huyghe, Jeroen R.; Mahajan, Anubha; Asimit, Jennifer L.; Ferreira, Teresa; Locke, Adam E.; Robertson, Neil R.; Wang, Xu; Sim, Xueling; Fujita, Hayato; Hara, Kazuo; Young, Robin; Zhang, Weihua; Choi, Sungkyoung; Chen, Han; Kaur, Ismeet; Takeuchi, Fumihiko; Fontanillas, Pierre; Thuillier, Dorothée; Yengo, Loic; Below, Jennifer E.; Tam, Claudia H.T.; Wu, Ying; Abecasis, Gonçalo; Altshuler, David; Bell, Graeme I.; Blangero, John; Burtt, Noél P.; Duggirala, Ravindranath; Florez, Jose C.; Hanis, Craig L.; Seielstad, Mark; Atzmon, Gil; Chan, Juliana C.N.; Ma, Ronald C.W.; Froguel, Philippe; Wilson, James G.; Bharadwaj, Dwaipayan; Dupuis, Josee; Meigs, James B.; Cho, Yoon Shin; Park, Taesung; Kooner, Jaspal S.; Chambers, John C.; Saleheen, Danish; Kadowaki, Takashi; Tai, E. Shyong; Mohlke, Karen L.; Cox, Nancy J.; Ferrer, Jorge; Zeggini, Eleftheria; Kato, Norihiro; Teo, Yik Ying; Boehnke, Michael; McCarthy, Mark I.; Morris, Andrew P.

2016-01-01

To gain insight into potential regulatory mechanisms through which the effects of variants at four established type 2 diabetes (T2D) susceptibility loci (CDKAL1, CDKN2A-B, IGF2BP2 and KCNQ1) are mediated, we undertook transancestral fine-mapping in 22 086 cases and 42 539 controls of East Asian, European, South Asian, African American and Mexican American descent. Through high-density imputation and conditional analyses, we identified seven distinct association signals at these four loci, each with allelic effects on T2D susceptibility that were homogenous across ancestry groups. By leveraging differences in the structure of linkage disequilibrium between diverse populations, and increased sample size, we localised the variants most likely to drive each distinct association signal. We demonstrated that integration of these genetic fine-mapping data with genomic annotation can highlight potential causal regulatory elements in T2D-relevant tissues. These analyses provide insight into the mechanisms through which T2D association signals are mediated, and suggest future routes to understanding the biology of specific disease susceptibility loci. PMID:26911676

Mapping Magnetic Susceptibility Anisotropies of White Matter in vivo in the Human Brain at 7 Tesla

PubMed Central

Li, Xu; Vikram, Deepti S; Lim, Issel Anne L; Jones, Craig K; Farrell, Jonathan A.D.; van Zijl, Peter C. M.

2012-01-01

High-resolution magnetic resonance phase- or frequency- shift images acquired at high field show contrast related to magnetic susceptibility differences between tissues. Such contrast varies with the orientation of the organ in the field, but the development of quantitative susceptibility mapping (QSM) has made it possible to reproducibly image the intrinsic tissue susceptibility contrast. However, recent studies indicate that magnetic susceptibility is anisotropic in brain white matter and, as such, needs to be described by a symmetric second-rank tensor (χ¯¯). To fully determine the elements of this tensor, it would be necessary to acquire frequency data at six or more orientations. Assuming cylindrical symmetry of the susceptibility tensor in myelinated white matter fibers, we propose a simplified method to reconstruct the susceptibility tensor in terms of a mean magnetic susceptibility, MMS = (χ∥ + 2χ⊥)/3 and a magnetic susceptibility anisotropy, MSA = χ∥ − χ⊥, where χ∥ and χ⊥ are susceptibility parallel and perpendicular to the white matter fiber direction, respectively. Computer simulations show that with a practical head rotation angle of around 20°–30°, four head orientations suffice to reproducibly reconstruct the tensor with good accuracy. We tested this approach on whole brain 1×1×1 mm3 frequency data acquired from five healthy subjects at 7 T. The frequency information from phase images collected at four head orientations was combined with the fiber direction information extracted from diffusion tensor imaging (DTI) to map the white matter susceptibility tensor. The MMS and MSA were quantified for regions in several large white matter fiber structures, including the corona radiata, posterior thalamic radiation and corpus callosum. MMS ranged from −0.037 to −0.053 ppm (referenced to CSF being about zero). MSA values could be quantified without the need for a reference and ranged between 0.004 and 0.029 ppm, in line with the expectation that the susceptibility perpendicular to the fiber is more diamagnetic than the one parallel to it. PMID:22561358

High Spatial Resolution and Temporally Resolved T2 * Mapping of Normal Human Myocardium at 7.0 Tesla: An Ultrahigh Field Magnetic Resonance Feasibility Study

PubMed Central

Hezel, Fabian; Thalhammer, Christof; Waiczies, Sonia; Schulz-Menger, Jeanette; Niendorf, Thoralf

2012-01-01

Myocardial tissue characterization using T2 * relaxation mapping techniques is an emerging application of (pre)clinical cardiovascular magnetic resonance imaging. The increase in microscopic susceptibility at higher magnetic field strengths renders myocardial T2 * mapping at ultrahigh magnetic fields conceptually appealing. This work demonstrates the feasibility of myocardial T2 * imaging at 7.0 T and examines the applicability of temporally-resolved and high spatial resolution myocardial T2 * mapping. In phantom experiments single cardiac phase and dynamic (CINE) gradient echo imaging techniques provided similar T2 * maps. In vivo studies showed that the peak-to-peak B0 difference following volume selective shimming was reduced to approximately 80 Hz for the four chamber view and mid-ventricular short axis view of the heart and to 65 Hz for the left ventricle. No severe susceptibility artifacts were detected in the septum and in the lateral wall for T2 * weighting ranging from TE = 2.04 ms to TE = 10.2 ms. For TE >7 ms, a susceptibility weighting induced signal void was observed within the anterior and inferior myocardial segments. The longest T2 * values were found for anterior (T2 * = 14.0 ms), anteroseptal (T2 * = 17.2 ms) and inferoseptal (T2 * = 16.5 ms) myocardial segments. Shorter T2 * values were observed for inferior (T2 * = 10.6 ms) and inferolateral (T2 * = 11.4 ms) segments. A significant difference (p = 0.002) in T2 * values was observed between end-diastole and end-systole with T2 * changes of up to approximately 27% over the cardiac cycle which were pronounced in the septum. To conclude, these results underscore the challenges of myocardial T2 * mapping at 7.0 T but demonstrate that these issues can be offset by using tailored shimming techniques and dedicated acquisition schemes. PMID:23251708

Accuracy of magnetic resonance based susceptibility measurements

NASA Astrophysics Data System (ADS)

Erdevig, Hannah E.; Russek, Stephen E.; Carnicka, Slavka; Stupic, Karl F.; Keenan, Kathryn E.

2017-05-01

Magnetic Resonance Imaging (MRI) is increasingly used to map the magnetic susceptibility of tissue to identify cerebral microbleeds associated with traumatic brain injury and pathological iron deposits associated with neurodegenerative diseases such as Parkinson's and Alzheimer's disease. Accurate measurements of susceptibility are important for determining oxygen and iron content in blood vessels and brain tissue for use in noninvasive clinical diagnosis and treatment assessments. Induced magnetic fields with amplitude on the order of 100 nT, can be detected using MRI phase images. The induced field distributions can then be inverted to obtain quantitative susceptibility maps. The focus of this research was to determine the accuracy of MRI-based susceptibility measurements using simple phantom geometries and to compare the susceptibility measurements with magnetometry measurements where SI-traceable standards are available. The susceptibilities of paramagnetic salt solutions in cylindrical containers were measured as a function of orientation relative to the static MRI field. The observed induced fields as a function of orientation of the cylinder were in good agreement with simple models. The MRI susceptibility measurements were compared with SQUID magnetometry using NIST-traceable standards. MRI can accurately measure relative magnetic susceptibilities while SQUID magnetometry measures absolute magnetic susceptibility. Given the accuracy of moment measurements of tissue mimicking samples, and the need to look at small differences in tissue properties, the use of existing NIST standard reference materials to calibrate MRI reference structures is problematic and better reference materials are required.

Assessing landslide susceptibility by statistical data analysis and GIS: the case of Daunia (Apulian Apennines, Italy)

NASA Astrophysics Data System (ADS)

Ceppi, C.; Mancini, F.; Ritrovato, G.

2009-04-01

This study aim at the landslide susceptibility mapping within an area of the Daunia (Apulian Apennines, Italy) by a multivariate statistical method and data manipulation in a Geographical Information System (GIS) environment. Among the variety of existing statistical data analysis techniques, the logistic regression was chosen to produce a susceptibility map all over an area where small settlements are historically threatened by landslide phenomena. By logistic regression a best fitting between the presence or absence of landslide (dependent variable) and the set of independent variables is performed on the basis of a maximum likelihood criterion, bringing to the estimation of regression coefficients. The reliability of such analysis is therefore due to the ability to quantify the proneness to landslide occurrences by the probability level produced by the analysis. The inventory of dependent and independent variables were managed in a GIS, where geometric properties and attributes have been translated into raster cells in order to proceed with the logistic regression by means of SPSS (Statistical Package for the Social Sciences) package. A landslide inventory was used to produce the bivariate dependent variable whereas the independent set of variable concerned with slope, aspect, elevation, curvature, drained area, lithology and land use after their reductions to dummy variables. The effect of independent parameters on landslide occurrence was assessed by the corresponding coefficient in the logistic regression function, highlighting a major role played by the land use variable in determining occurrence and distribution of phenomena. Once the outcomes of the logistic regression are determined, data are re-introduced in the GIS to produce a map reporting the proneness to landslide as predicted level of probability. As validation of results and regression model a cell-by-cell comparison between the susceptibility map and the initial inventory of landslide events was performed and an agreement at 75% level achieved.

Background field removal using a region adaptive kernel for quantitative susceptibility mapping of human brain

NASA Astrophysics Data System (ADS)

Fang, Jinsheng; Bao, Lijun; Li, Xu; van Zijl, Peter C. M.; Chen, Zhong

2017-08-01

Background field removal is an important MR phase preprocessing step for quantitative susceptibility mapping (QSM). It separates the local field induced by tissue magnetic susceptibility sources from the background field generated by sources outside a region of interest, e.g. brain, such as air-tissue interface. In the vicinity of air-tissue boundary, e.g. skull and paranasal sinuses, where large susceptibility variations exist, present background field removal methods are usually insufficient and these regions often need to be excluded by brain mask erosion at the expense of losing information of local field and thus susceptibility measures in these regions. In this paper, we propose an extension to the variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP) background field removal method using a region adaptive kernel (R-SHARP), in which a scalable spherical Gaussian kernel (SGK) is employed with its kernel radius and weights adjustable according to an energy "functional" reflecting the magnitude of field variation. Such an energy functional is defined in terms of a contour and two fitting functions incorporating regularization terms, from which a curve evolution model in level set formation is derived for energy minimization. We utilize it to detect regions of with a large field gradient caused by strong susceptibility variation. In such regions, the SGK will have a small radius and high weight at the sphere center in a manner adaptive to the voxel energy of the field perturbation. Using the proposed method, the background field generated from external sources can be effectively removed to get a more accurate estimation of the local field and thus of the QSM dipole inversion to map local tissue susceptibility sources. Numerical simulation, phantom and in vivo human brain data demonstrate improved performance of R-SHARP compared to V-SHARP and RESHARP (regularization enabled SHARP) methods, even when the whole paranasal sinus regions are preserved in the brain mask. Shadow artifacts due to strong susceptibility variations in the derived QSM maps could also be largely eliminated using the R-SHARP method, leading to more accurate QSM reconstruction.

Background field removal using a region adaptive kernel for quantitative susceptibility mapping of human brain.

PubMed

Fang, Jinsheng; Bao, Lijun; Li, Xu; van Zijl, Peter C M; Chen, Zhong

2017-08-01

Background field removal is an important MR phase preprocessing step for quantitative susceptibility mapping (QSM). It separates the local field induced by tissue magnetic susceptibility sources from the background field generated by sources outside a region of interest, e.g. brain, such as air-tissue interface. In the vicinity of air-tissue boundary, e.g. skull and paranasal sinuses, where large susceptibility variations exist, present background field removal methods are usually insufficient and these regions often need to be excluded by brain mask erosion at the expense of losing information of local field and thus susceptibility measures in these regions. In this paper, we propose an extension to the variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP) background field removal method using a region adaptive kernel (R-SHARP), in which a scalable spherical Gaussian kernel (SGK) is employed with its kernel radius and weights adjustable according to an energy "functional" reflecting the magnitude of field variation. Such an energy functional is defined in terms of a contour and two fitting functions incorporating regularization terms, from which a curve evolution model in level set formation is derived for energy minimization. We utilize it to detect regions of with a large field gradient caused by strong susceptibility variation. In such regions, the SGK will have a small radius and high weight at the sphere center in a manner adaptive to the voxel energy of the field perturbation. Using the proposed method, the background field generated from external sources can be effectively removed to get a more accurate estimation of the local field and thus of the QSM dipole inversion to map local tissue susceptibility sources. Numerical simulation, phantom and in vivo human brain data demonstrate improved performance of R-SHARP compared to V-SHARP and RESHARP (regularization enabled SHARP) methods, even when the whole paranasal sinus regions are preserved in the brain mask. Shadow artifacts due to strong susceptibility variations in the derived QSM maps could also be largely eliminated using the R-SHARP method, leading to more accurate QSM reconstruction. Copyright © 2017. Published by Elsevier Inc.

Meta-analysis and genome-wide interpretation of genetic susceptibility to drug addiction

PubMed Central

2011-01-01

Background Classical genetic studies provide strong evidence for heritable contributions to susceptibility to developing dependence on addictive substances. Candidate gene and genome-wide association studies (GWAS) have sought genes, chromosomal regions and allelic variants likely to contribute to susceptibility to drug addiction. Results Here, we performed a meta-analysis of addiction candidate gene association studies and GWAS to investigate possible functional mechanisms associated with addiction susceptibility. From meta-data retrieved from 212 publications on candidate gene association studies and 5 GWAS reports, we linked a total of 843 haplotypes to addiction susceptibility. We mapped the SNPs in these haplotypes to functional and regulatory elements in the genome and estimated the magnitude of the contributions of different molecular mechanisms to their effects on addiction susceptibility. In addition to SNPs in coding regions, these data suggest that haplotypes in gene regulatory regions may also contribute to addiction susceptibility. When we compared the lists of genes identified by association studies and those identified by molecular biological studies of drug-regulated genes, we observed significantly higher participation in the same gene interaction networks than expected by chance, despite little overlap between the two gene lists. Conclusions These results appear to offer new insights into the genetic factors underlying drug addiction. PMID:21999673

Synergies between geomorphic hazard and risk and sediment cascade research fields: exploiting geomorphic processes' susceptibility analyses to derive potential sediment sources in the Oltet, river catchment, southern Romania

NASA Astrophysics Data System (ADS)

Jurchescu, Marta-Cristina

2015-04-01

Identifying sediment sources and sediment availability represents a major problem and one of the first concerns in the field of sediment cascade. This paper addresses the on-site effects associated with sediment transfer, investigating the degree to which studies pertaining to the field of geomorphic hazard and risk research could be exploited in sediment budget estimations. More precisely, the paper investigates whether results obtained in assessing susceptibility to various geomorphic processes (landslides, soil erosion, gully erosion) could be transferred to the study of sediment sources within a basin. The study area is a medium-sized catchment (> 2400 km2) in southern Romania encompassing four different geomorphic units (mountains, hills, piedmont and plain). The region is highly affected by a wide range of geomorphic processes which supply sediments to the drainage network. The presence of a reservoir at the river outlet emphasizes the importance of estimating sediment budgets. The susceptibility analyses are conducted separately for each type of the considered processes in a top-down framework, i.e. at two different scales, using scale-adapted methods and validation techniques in each case, as widely-recognized in the hazard and risk research literature. The analyses start at a regional scale, which has in view the entire catchment, using readily available data on conditioning factors. In a second step, the suceptibility analyses are carried out at a medium scale for selected hotspot-compartments of the catchment. In order to appraise the extent to which susceptibility results are relevant in interpreting sediment sources at catchment scale, scale-induced differences are analysed in the case of each process. Based on the amount of uncertainty revealed by each regional-scale analysis in comparison to the medium-scale ones, decisions are made on whether the first are acceptable to the aim of identifying potential sediment source areas or if they should be refined using more precise methods and input data. The three final basin-wide susceptibility maps are eventually coverted, on a threshold basis, to maps showing the potential areas of sediment production by landslides, soil erosion and gully erosion respectively. These are then combined into one single map of potential sediment sources. The susceptibility assessments indicate that the basin compartments most prone to landslides and soil erosion correspond to the Subcarpathian hills, while the one most threatened by gully erosion corresponds to the piedmont relief. The final map of potential sediment sources shows that approximately 34% of the study catchment is occupied by areas potentially generating sediment through landslides and gully erosion, extending over most of the high piedmont and Subcarpathian hills. The results prove that there is an important link between the two research fields, i.e. geomorphic hazard and risk and sediment cascade, by allowing the transfer of knowledge from geomorphic processes' susceptibility analyses to the estimation of potential sediment sources within catchments. The synergy between the two fields raises further challenges to be tackled in future (e.g. how to derive sediment transfer rates from quantitative hazard estimates).

Recent developments in machine learning applications in landslide susceptibility mapping

NASA Astrophysics Data System (ADS)

Lun, Na Kai; Liew, Mohd Shahir; Matori, Abdul Nasir; Zawawi, Noor Amila Wan Abdullah

2017-11-01

While the prediction of spatial distribution of potential landslide occurrences is a primary interest in landslide hazard mitigation, it remains a challenging task. To overcome the scarceness of complete, sufficiently detailed geomorphological attributes and environmental conditions, various machine-learning techniques are increasingly applied to effectively map landslide susceptibility for large regions. Nevertheless, limited review papers are devoted to this field, particularly on the various domain specific applications of machine learning techniques. Available literature often report relatively good predictive performance, however, papers discussing the limitations of each approaches are quite uncommon. The foremost aim of this paper is to narrow these gaps in literature and to review up-to-date machine learning and ensemble learning techniques applied in landslide susceptibility mapping. It provides new readers an introductory understanding on the subject matter and researchers a contemporary review of machine learning advancements alongside the future direction of these techniques in the landslide mitigation field.

Towards a Quasi-global precipitation-induced Landslide Detection System using Remote Sensing Information

NASA Astrophysics Data System (ADS)

Adler, B.; Hong, Y.; Huffman, G.; Negri, A.; Pando, M.

2006-05-01

Landslides and debris flows are one of the most widespread natural hazards on Earth, responsible for thousands of deaths and billions of dollars in property damage per year. Currently, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides. In this study, global landslide susceptibility is mapped using USGS GTOPO30 Digital Elevation, hydrological derivatives (slopes and wetness index etc.) from HYDRO1k data, soil type information downscaled from Digital Soil Map of the World (Sand, Loam, Silt, or Clay etc.), and MODIS land cover/use classification data. These variables are then combined with empirical landslide inventory data, if available, to derive a global landslide susceptibility map at elemental resolution of 1 x 1 km. This map can then be overlain with the driving force, namely rainfall estimates from the TRMM-based Multiple-satellite Precipitation Analysis to identify when areas with significant landslide potential receive heavy rainfall. The relations between rainfall intensity and rainstorm duration are regionally specific and often take the form of a power-law relation. Several empirical landslide-triggering Rainfall Intensity-Duration thresholds are implemented regionally using the 8-year TRMM-based precipitation with or without the global landslide susceptibility map at continuous space and time domain. Finally, the effectiveness of this system is validated by studying several recent deadly landslide/mudslide events. This study aims to build up a prototype quasi-global potential landslide warning system. Spatially-distributed landslide susceptibility maps and regional empirical rainfall intensity-duration thresholds, in combination with real-time rainfall measurements from space and rainfall forecasts from models, will be the basis for this experimental system.

The combination of two Sle2 lupus-susceptibility loci and Cdkn2c deficiency leads to T cell-mediated pathology in B6.Faslpr mice

PubMed Central

Xu, Zhiwei; Croker, Byron P.; Morel, Laurence

2013-01-01

The NZM2410 Sle2c1 lupus susceptibility locus is responsible for the expansion of the B1a cell compartment and for the induction of T-cell induced renal and skin pathology on a CD95 deficient (Faslpr)-background. We have previously shown that deficiency in cyclin-dependent kinase inhibitor p18INK4c (p18) was responsible for the B1a cell expansion but was not sufficient to account for the pathology in B6.lpr mice. This study was designed to map the additional Sle2c1 loci responsible for autoimmune pathology when co-expressed with CD95 deficiency. The production, fine-mapping and phenotypic characterization of five recombinant intervals indicated that three interacting sub-loci were responsive for inducting autoimmune pathogenesis in B6.lpr mice. One of these sub-loci corresponds most likely to p18-deficiency. Another major locus mapping to a 2 Mb region at the telomeric end of Sle2c1 is necessary to both renal and skin pathology. Finally, a third locus centromeric to p18 enhances the severity of lupus nephritis. These results provide new insights into the genetic interactions leading to SLE disease presentation, and represent a major step towards the identification of novel susceptibility genes involved in T-cell mediated organ damage. PMID:23698709

Comprehensive assessment of rheumatoid arthritis susceptibility loci in a large psoriatic arthritis cohort

PubMed Central

Bowes, John; Ho, Pauline; Flynn, Edw; Ali, Faisal; Marzo-Ortega, Helena; Coates, Laura C; Warren, Rich B; McManus, Ross; Ryan, Anthony W; Kane, David; Korendowych, Eleanor; McHugh, Neil; FitzGerald, Oliver; Packham, Jonathon; Morgan, Ann W; Bruce, Ian N; Barton, Anne

2012-01-01

Objective A number of rheumatoid arthritis (RA) susceptibility genes have been identified in recent years. Given the overlap in phenotypic expression of synovial joint inflammation between RA and psoriatic arthritis (PsA), the authors explored whether RA susceptibility genes are also associated with PsA. Methods 56 single nucleotide polymorphisms (SNPs) mapping to 41 genes previously reported as RA susceptibility loci were selected for investigation. PsA was defined as an inflammatory arthritis associated with psoriasis and subjects were recruited from the UK and Ireland. Genotyping was performed using the Sequenom MassArray platform and frequencies compared with data derived from large UK control collections. Results Significant evidence for association with susceptibility to PsA was found toa SNP mapping to the REL (rs13017599, ptrend=5.2×104) gene, while nominal evidence for association (ptrend<0.05) was found to seven other loci including PLCL2 (rs4535211, p=1.7×10−3); STAT4 (rs10181656, p=3.0×10−3) and the AFF3, CD28, CCL21, IL2 and KIF5A loci. Interestingly, three SNPs demonstrated opposite effects to those reported for RA. Conclusions The REL gene, a key modulator of the NFκB pathway, is associated with PsA but the allele conferring risk to RA is protective in PsA suggesting that there are fundamental differences in the aetiological mechanisms underlying these two types of inflammatory arthritis. PMID:22328738

Detection and assessment of flood susceptible irrigation networks in Licab, Nueva Ecija, Philippines using LiDAR DTM

NASA Astrophysics Data System (ADS)

Alberto, R. T.; Hernando, P. J. C.; Tagaca, R. C.; Celestino, A. B.; Palado, G. C.; Camaso, E. E.; Damian, G. B.

2017-09-01

Climate change has wide-ranging effects on the environment and socio-economic and related sectors which includes water resources, agriculture and food security, human health, terrestrial ecosystems, coastal zones and biodiversity. Farmers are under pressure to the changing weather and increasing unpredictable water supply. Because of rainfall deficiencies, artificial application of water has been made through irrigation. Irrigation is a basic determinant of agriculture because its inadequacies are the most powerful constraints on the increase of agricultural production. Irrigation networks are permanent and temporary conduits that supply water to agricultural areas from an irrigation source. Detection of irrigation networks using LiDAR DTM, and flood susceptible assessment of irrigation networks could give baseline information on the development and management of sustainable agriculture. Map Gully Depth (MGD) in Whitebox GAT was used to generate the potential irrigation networks. The extracted MGD was overlaid in ArcGIS as guide in the digitization of potential irrigation networks. A flood hazard map was also used to identify the flood susceptible irrigation networks in the study area. The study was assessed through field validation of points which were generated using random sampling method. Results of the study showed that most of the detected irrigation networks have low to moderate susceptibility to flooding while the rest have high susceptibility to flooding which is due to shifting weather. These irrigation networks may cause flood when it overflows that could also bring huge damage to rice and other agricultural areas.

Pattern Analysis of Dynamic Susceptibility Contrast-enhanced MR Imaging Demonstrates Peritumoral Tissue Heterogeneity

PubMed Central

Akbari, Hamed; Macyszyn, Luke; Da, Xiao; Wolf, Ronald L.; Bilello, Michel; Verma, Ragini; O’Rourke, Donald M.

2014-01-01

Purpose To augment the analysis of dynamic susceptibility contrast material–enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma. Materials and Methods Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score. Results The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score. Conclusion Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication. © RSNA, 2014 PMID:24955928

Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

NASA Astrophysics Data System (ADS)

Melchiorre, C.; Castellanos Abella, E. A.; van Westen, C. J.; Matteucci, M.

2011-04-01

This paper describes a procedure for landslide susceptibility assessment based on artificial neural networks, and focuses on the estimation of the prediction capability, robustness, and sensitivity of susceptibility models. The study is carried out in the Guantanamo Province of Cuba, where 186 landslides were mapped using photo-interpretation. Twelve conditioning factors were mapped including geomorphology, geology, soils, landuse, slope angle, slope direction, internal relief, drainage density, distance from roads and faults, rainfall intensity, and ground peak acceleration. A methodology was used that subdivided the database in 3 subsets. A training set was used for updating the weights. A validation set was used to stop the training procedure when the network started losing generalization capability, and a test set was used to calculate the performance of the network. A 10-fold cross-validation was performed in order to show that the results are repeatable. The prediction capability, the robustness analysis, and the sensitivity analysis were tested on 10 mutually exclusive datasets. The results show that by means of artificial neural networks it is possible to obtain models with high prediction capability and high robustness, and that an exploration of the effect of the individual variables is possible, even if they are considered as a black-box model.

Magnetic Susceptibility as a B0 Field Strength Independent MRI Biomarker of Liver Iron Overload

PubMed Central

Hernando, Diego; Cook, Rachel J.; Diamond, Carol; Reeder, Scott B.

2013-01-01

Purpose MR-based quantification of liver magnetic susceptibility may enable field strength-independent measurement of liver iron concentration (LIC). However, susceptibility quantification is challenging, due to non-local effects of susceptibility on the B0 field. The purpose of this work is to demonstrate feasibility of susceptibility-based LIC quantification using a fat-referenced approach. Methods Phantoms consisting of vials with increasing iron concentrations immersed between oil/water layers, and twenty-seven subjects (9 controls/18 subjects with liver iron overload) were scanned. Ferriscan (1.5T) provided R2-based reference LIC. Multi-echo 3D-SPGR (1.5T/3T) enabled fat-water, B0- and R2*-mapping. Phantom iron concentration (mg Fe/l) was estimated from B0 differences (ΔB0) between vials and neighboring oil. Liver susceptibility and LIC (mg Fe/g dry tissue) was estimated from ΔB0 between the lateral right lobe of the liver and adjacent subcutaneous adipose tissue (SAT). Results Estimated phantom iron concentrations had good correlation with true iron concentrations (1.5T:slope=0.86, intercept=0.72, r2=0.98; 3T:slope=0.85, intercept=1.73, r2=0.98). In liver, ΔB0 correlated strongly with R2* (1.5T:r2=0.86; 3T:r2=0.93) and B0-LIC had good agreement with Ferriscan-LIC (slopes/intercepts nearly 1.0/0.0, 1.5T:r2=0.67, slope=0.93±0.13, p≈0.50, intercept=1.93±0.78, p≈0.02; 3T:r2=0.84, slope=1.01±0.09, p≈0.90, intercept=0.23±0.52, p≈0.68). Discussion Fat-referenced, susceptibility-based LIC estimation is feasible at both field strengths. This approach may enable improved susceptibility mapping in the abdomen. PMID:23801540

Disseminating Landslide Hazard Information for California Local Government

NASA Astrophysics Data System (ADS)

Wills, C. J.

2010-12-01

Since 1969, the California Geological Survey has produced numerous maps showing landslide features and delineating potential slope-stability problem areas. These maps have been provided to local governments to encourage consideration of landslide hazards in planning and development decisions. Maps produced from 1986 through 1995 under the Landslide Hazard Mapping Act were advisory only, and their use by local government was never consistent. By contrast, maps of Zones of Required Investigation for seismically induced landslides produced under the Seismic Hazard Zoning Act since 1997 come with detailed guidelines and legal requirements. A legislative act that required landslide hazards be mapped and hazard maps disseminated to local government proved ineffective in landslide hazard mitigation. A later act with requirements that the hazard zone maps be used by local government proved more effective. Planning scenarios have proven to be an effective way of transmitting scientific information about natural hazards to emergency response professionals. Numerous earthquake planning scenarios have been prepared and used as the basis for emergency response exercises. An advantage of scenarios that include loss estimates is that the effects can be put in units of measure that everyone understands, principally deaths and dollars. HAZUS software available from FEMA allows calculation of losses for earthquake scenarios, but similar methods for landslides have not been developed. As part of the USGS Multi-Hazard Demonstration Project, we have estimated the landslide losses for a major west-coast winter storm scenario by developing a system based loosely on HAZUS. Data on landslide damage in past storms has been sparse and inconsistent, but a few data sets are available. The most detailed and complete available data on landslide damage was gathered by the City of Los Angeles following the 1978 storms. We extrapolate from that data to the entire state by first generalizing a landslide susceptibility map to give a single value of susceptibility for each census tract. We then calculated the loss ratio, the cost of landslide damage from the 1978 storms divided by the value of light wood frame structures in the census tract. The comparison suggests three general categories of damage: tracts with low landslide susceptibility have no landslide damage: tracts with moderate susceptibility have loss ratios of about 0.016%: and tracts with high susceptibility have loss ratios of 0.096%. Using these values, the susceptibility map becomes a landslide loss ratio map for the average storm intensity and landslide vulnerability of Los Angeles in 1978. Generalization to other storm intensities uses differences in storm intensity and landslide damage data from the 1982 storm in the Bay Area. In Santa Cruz County, that storm had a recurrence interval of over 100 years, and over 3 times the damage as our projection from the 1978 data. In Sonoma County, that storm had a recurrence interval of only 10 years and damage that was only 2% of our projection. If a relationship between storm intensity and the projections from the 1978 Los Angeles data can be developed, we may be able to estimate landslide losses for any projected storm intensity.

The Calcitonin Receptor Gene Is a Candidate for Regulation of Susceptibility to Herpes simplex Type 1 Neuronal Infection Leading to Encephalitis in Rat

PubMed Central

Abdelmagid, Nada; Bereczky-Veress, Biborka; Guerreiro-Cacais, André Ortlieb; Bergman, Petra; Luhr, Katarina M.; Bergström, Tomas; Sköldenberg, Birgit; Piehl, Fredrik

2012-01-01

Herpes simplex encephalitis (HSE) is a fatal infection of the central nervous system (CNS) predominantly caused by Herpes simplex virus type 1. Factors regulating the susceptibility to HSE are still largely unknown. To identify host gene(s) regulating HSE susceptibility we performed a genome-wide linkage scan in an intercross between the susceptible DA and the resistant PVG rat. We found one major quantitative trait locus (QTL), Hse1, on rat chromosome 4 (confidence interval 24.3–31 Mb; LOD score 29.5) governing disease susceptibility. Fine mapping of Hse1 using recombinants, haplotype mapping and sequencing, as well as expression analysis of all genes in the interval identified the calcitonin receptor gene (Calcr) as the main candidate, which also is supported by functional studies. Thus, using unbiased genetic approach variability in Calcr was identified as potentially critical for infection and viral spread to the CNS and subsequent HSE development. PMID:22761571

Introduction: Hazard mapping

USGS Publications Warehouse

Baum, Rex L.; Miyagi, Toyohiko; Lee, Saro; Trofymchuk, Oleksandr M

2014-01-01

Twenty papers were accepted into the session on landslide hazard mapping for oral presentation. The papers presented susceptibility and hazard analysis based on approaches ranging from field-based assessments to statistically based models to assessments that combined hydromechanical and probabilistic components. Many of the studies have taken advantage of increasing availability of remotely sensed data and nearly all relied on Geographic Information Systems to organize and analyze spatial data. The studies used a range of methods for assessing performance and validating hazard and susceptibility models. A few of the studies presented in this session also included some element of landslide risk assessment. This collection of papers clearly demonstrates that a wide range of approaches can lead to useful assessments of landslide susceptibility and hazard.

Rome: sinkhole events and network of underground cavities (Italy)

NASA Astrophysics Data System (ADS)

Nisio, Stefania; Ciotoli, Giancarlo

2016-04-01

The anthropogenic sinkholes in the city of Rome are closely linked to the network of underground cavities produced by human activities in more than two thousand years of history. Over the past fifteen years the increased frequency of intense rainfall events, favors sinkhole formation. The risk assessment induced by anthropogenic sinkhole is really difficult. However, a susceptibility of the territory to sinkholes can be more easily determined as the probability that an event may occur in a given space, with unique geological-morphological characteristics, and in an infinite time. A sinkhole susceptibility map of the Rome territory, up to the ring road, has been constructed by using Geographically Weighted Regression technique and geostatistics. The spatial regression model includes the analysis of more than 2700 anthropogenic sinkholes (recorded from 1875 to 2015), as well as geological, morphological, hydrological and predisposing anthropogenic characteristics of the study area. The numerous available data (underground cavities, the ancient entrances to the quarry, bunkers, etc.) facilitate the creation of a series of maps. The density map of the cavity, updated to 2015, showed that more than 20 km2 of the Roman territory are affected by underground cavities. The census of sinkholes (over 2700) shows that over 30 km2 has been affected by sinkholes. The final susceptibility map highlights that inside the Ring Road about 40 km2 of the territory (about 11%) have a very high probability of triggering a sinkhole event. The susceptibility map was also compared with the data of ground subsidence (InSAR) to obtain a predictive model.

Utilizing NASA Earth Observations to Assess Landslide Characteristics and Devlelop Susceptibility and Exposure Maps in Malawi

NASA Astrophysics Data System (ADS)

Klug, M.; Cissell, J.; Grossman, M.

2017-12-01

Malawi has become increasingly prone to landslides in the past few decades. This can be attributed to the terrain, types of soil and vegetation, increased human interference, and heavy flooding after long periods of drought. In addition to the floods and droughts, landslides cause extra stress to farmlands, thus exacerbating the current food security crisis in the country. It can be difficult to pinpoint just how many people are affected by landslides in Malawi because landslides often occur in rural areas or are grouped with other disasters, such as floods or earthquakes. This project created a Landslide Susceptibility Map to assess landslide-prone areas in Malawi using variables such as slope, distance to roads, distance to streams, soil type, and precipitation. These variables were derived using imagery from Landsat 8 Operational Land Imager (OLI), Shuttle Radar Topography Mission Version 3 (SRTM-v3), Global Precipitation Measurement (GPM), and Tropical Rainfall Measuring Mission (TRMM) satellites. Furthermore, this project created a Landslide Exposure Map to estimate how much of the local population lives in susceptible areas by intersecting population data with the Landslide Susceptibility Map. Additionally, an assessment of GPM and TRMM precipitation measurements was generated to better understand the reliability of both measurements for landslide monitoring. Finally, this project updated NASA SERVIR's Global Landslide Catalog (GLC) for Malawi by using WorldView data from Google Earth and Landsat 8 OLI. These end products were used by NASA SERVIR and the Regional Centre for Mapping of Resources for Development (RCMRD) for aiding in disaster management throughout Malawi.

Germany wide seasonal flood risk analysis for agricultural crops

NASA Astrophysics Data System (ADS)

Klaus, Stefan; Kreibich, Heidi; Kuhlmann, Bernd; Merz, Bruno; Schröter, Kai

2016-04-01

In recent years, large-scale flood risk analysis and mapping has gained attention. Regional to national risk assessments are needed, for example, for national risk policy developments, for large-scale disaster management planning and in the (re-)insurance industry. Despite increasing requests for comprehensive risk assessments some sectors have not received much scientific attention, one of these is the agricultural sector. In contrast to other sectors, agricultural crop losses depend strongly on the season. Also flood probability shows seasonal variation. Thus, the temporal superposition of high flood susceptibility of crops and high flood probability plays an important role for agricultural flood risk. To investigate this interrelation and provide a large-scale overview of agricultural flood risk in Germany, an agricultural crop loss model is used for crop susceptibility analyses and Germany wide seasonal flood-frequency analyses are undertaken to derive seasonal flood patterns. As a result, a Germany wide map of agricultural flood risk is shown as well as the crop type most at risk in a specific region. The risk maps may provide guidance for federal state-wide coordinated designation of retention areas.

Modelling soil erosion in a head catchment of Jemma Basin on the Ethiopian highlands

NASA Astrophysics Data System (ADS)

Cama, Mariaelena; Schillaci, Calogero; Kropáček, Jan; Hochschild, Volker; Maerker, Michael

2017-04-01

Soil erosion represents one of the most important global issues with serious effects on agriculture and water quality especially in developing countries such as Ethiopia where rapid population growth and climatic changes affect wide mountainous areas. The catchment of Andit-Tid is a head catchment of Jemma Basin draining to the Blue Nile (Central Ethiopia). It is located in an extremely variable topographical environment and it is exposed to high degradation dynamics especially in the lower part of the catchment. The increasing agricultural activity and grazing, lead to an intense use of the steep slopes which altered the soil structure. As a consequence, water erosion processes accelerated leading to the evolution of sheet erosion, gullies and badlands. This study is aimed at a geomorphological assessment of soil erosion susceptibility. First, a geomorphological map is generated using high resolution digital elevation model (DEM) derived from high resolution stereoscopic satellite data, multispectral imagery from Rapid Eye satellite system . The map was then validated by a detailed field survey. The final maps contains three inventories of landforms: i) sheet, ii) gully erosion and iii) badlands. The water erosion susceptibility is calculated with a Maximum Entropy approach. In particular, three different models are built using the three inventories as dependent variables and a set of spatial attributes describing the lithology, terrain, vegetation and land cover from remote sensing data and DEMs as independent variables. The single susceptibility maps for sheet, gully erosion as well as badlands showed good to excellent predictive performances. Moreover, we reveal and discuss the importance of different sets of variables among the three models. In order to explore the mutual overlap of the three susceptibility maps we generated a combined map as color composite whereas each color represents one component of water erosion. The latter map yield a useful information for land use managers and planning purposes.

Slope, Scarp and Sea Cliff Instability Susceptibility Mapping for Planning Regulations in Almada County, Portugal

NASA Astrophysics Data System (ADS)

Marques, Fernando; Queiroz, Sónia; Gouveia, Luís; Vasconcelos, Manuel

2017-12-01

In Portugal, the modifications introduced in 2008 and 2012 in the National Ecological Reserve law (REN) included the mandatory study of slope instability, including slopes, natural scarps, and sea cliffs, at municipal or regional scale, with the purpose of avoiding the use of hazardous zones with buildings and other structures. The law also indicates specific methods to perform these studies, with different approaches for slope instability, natural scarps and sea cliffs. The methods used to produce the maps required by REN law, with modifications and improvements to the law specified methods, were applied to the 71 km2 territory of Almada County, and included: 1) Slope instability mapping using the statistically based Information Value method validated with the landslide inventory using ROC curves, which provided an AAC=0.964, with the higher susceptibility zones which cover at least 80% of the landslides of the inventory to be included in REN map. The map was object of a generalization process to overcome the inconveniences of the use of a pixel based approach. 2) Natural scarp mapping including setback areas near the top, defined according to the law and setback areas near the toe defined by the application of the shadow angle calibrated with the major rockfalls which occurred in the study area; 3) Sea cliffs mapping including two levels of setback zones near the top, and one setback zone at the cliffs toe, which were based on systematic inventories of cliff failures occurred between 1947 and 2010 in a large scale regional littoral monitoring project. In the paper are described the methods used and the results obtained in this study, which correspond to the final maps of areas to include in REN. The results obtained in this study may be considered as an example of good practice of the municipal authorities in terms of solid, technical and scientifically supported regulation definitions, hazard prevention and safe and sustainable land use management.

Comparison of non-landslide sampling strategies to counteract inventory-based biases within national-scale statistical landslide susceptibility models

NASA Astrophysics Data System (ADS)

Lima, Pedro; Steger, Stefan; Glade, Thomas

2017-04-01

Landslides can represent a significant threat for people and infrastructure in hilly and mountainous landscapes worldwide. The understanding and prediction of those geomorphic processes is crucial to avoid economic loses or even casualties to people and their properties. Statistical based landslide susceptibility models are well known for being highly reliant on the quality, representativeness and availability of input data. In this context, several studies indicate that the landslide inventory represents the most important input data. However each landslide mapping technique or data collection has its drawbacks. Consequently, biased landslide inventories may be commonly introduced into statistical models, especially at regional or even national scale. It remains to the researcher to be aware of potential limitations and design strategies to avoid or reduce the potential propagation of input data errors and biases influences on the modelling outcomes. Previous studies have proven that such erroneous landslide inventories may lead to unrealistic landslide susceptibility maps. We assume that one possibility to tackle systematic landslide inventory-based biases might be a concentration on sampling strategies that focus on the distribution of non-landslide locations. For this purpose, we test an approach for the Austrian territory that concentrates on a modified non-landslide sampling strategy, instead the traditional applied random sampling. It is expected that the way non-landslide locations are represented (e.g. equally over the area or within those areas where mapping campaigns have been conducted) is important to reduce a potential over- or underestimation of landslide susceptibility within specific areas caused by bias. As presumably each landslide inventory is known to be systematically incomplete, especially in those areas where no mapping campaign was previously conducted. This is also applicable to the one currently available for the Austrian territory, composed by 14,519 shallow landslides. Within this study, we introduce the following explanatory variables to test the effect of different non-landslide strategies: Lithological units, grouped by their geotechnical properties and topographic parameters such as aspect, elevation, slope gradient and the topographic position. Landslide susceptibility maps will be derived by applying logistic regression, while systematic comparisons will be carried out based on models created by different non-landslide sampling strategies. Models generated by the conventional random sampling are presented against models based on stratified and clustered sampling strategies. The modelling results will be compared in terms of their prediction performance measured by the AUROC (Area Under the Receiver Operating Characteristic Curve) obtained by means of a k-fold cross-validation and also by the spatial pattern of the maps. The outcomes of this study are intended to contribute to the understanding on how landslide-inventory based biases may be counteracted.

Using the information value method in a geographic information system and remote sensing for malaria mapping: a case study from India.

PubMed

Rai, Praveen Kumar; Nathawat, Mahendra Singh; Rai, Shalini

2013-01-01

This paper explores the scope of malaria-susceptibility modelling to predict malaria occurrence in an area. An attempt has been made in Varanasi district, India, to evaluate the status of malaria disease and to develop a model by which malaria-prone zones could be predicted using five classes of relative malaria susceptibility, i.e.very low, low, moderate, high and very high categories. The information value (Info Val) method was used to assess malaria occurrence and various time-were used as the independent variables. A geographical information system (GIS) is employed to investigate associations between such variables and distribution of different mosquitoes responsible for malaria transmission. Accurate prediction of risk depends on a number of variables, such as land use, NDVI, climatic factors, population, distance to health centres, ponds, streams and roads etc., all of which have an influence on malaria transmission or reporting. Climatic factors, particularly rainfall, temperature and relative humidity, are known to have a major influence on the biology of mosquitoes. To produce a malaria-susceptibility map using this method, weightings are calculated for various classes in each group. The groups are then superimposed to prepare a Malaria Susceptibility Index (MSI) map. We found that 3.87% of the malaria cases were found in areas with a low malaria-susceptibility level predicted from the model, whereas 39.86% and 26.29% of malaria cases were found in predicted high and very high susceptibility level areas, respectively. Malaria susceptibility modelled using a GIS may have a role in predicting the risks of malaria and enable public health interventions to be better targeted.

Susceptibility mapping in the Río El Estado watershed, Pico de Orizaba volcano, Mexico

NASA Astrophysics Data System (ADS)

Legorreta Paulin, G.; Bursik, M. I.; Lugo Hubp, J.; Paredes Mejía, L.; Aceves Quesada, F.

2013-12-01

In volcanic terrains, dormant stratovolcanoes are very common and can trigger landslides and debris flows continually along stream systems, thereby affecting human settlements and economic activities. It is important to assess their potential impact and damage through the use of landslide inventory maps and landslide models. This poster provides an overview of the on-going research project (Grant SEP-CONACYT no 167495) from the Institute of Geography at the National Autonomous University of Mexico (UNAM) that seeks to conduct a multi-temporal landslide inventory and produce a landslide susceptibility map by using Geographic Information Systems (GIS). The Río El Estado watershed on the southwestern flank of Pico de Orizaba volcano, the highest mountain in Mexico, is selected as a study area. The catchment covers 5.2 km2 with elevations ranging from 2676.79 to 4248.2 m a.s.l. and hillslopes between 5° and 56°. The stream system of Río El Estado catchment erodes Tertiary and Quaternary lavas, pyroclastic flows, and fall deposits. The geologic and geomorphologic factors in combination with high seasonal precipitation, high degree of weathering, and steep slopes predispose the study area to landslides. The method encompasses two main levels of analysis to assess landslide susceptibility. The first level builds a historic landslide inventory. In the study area, an inventory of more than 100 landslides was mapped from interpretation of multi-temporal aerial orthophotographs and local field surveys to assess and describe landslide distribution. All landslides were digitized into a GIS, and the spatial geo-database of landslides was constructed from standardized GIS datasets. The second level calculates the susceptibility for the watershed. Multiple Logistic Regression (MLR) was used to examine the relationship between landsliding and several independent variables (elevation, slope, terrain curvature, flow direction, saturation, contributing area, land use, and geology) to create the susceptibility map. Finally, the model was compared with the reality expressed by the inventory map. The technique and its implementation of each level in a GIS-based technology is presented and discussed.

Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms.

PubMed

Horikoshi, Momoko; Pasquali, Lorenzo; Wiltshire, Steven; Huyghe, Jeroen R; Mahajan, Anubha; Asimit, Jennifer L; Ferreira, Teresa; Locke, Adam E; Robertson, Neil R; Wang, Xu; Sim, Xueling; Fujita, Hayato; Hara, Kazuo; Young, Robin; Zhang, Weihua; Choi, Sungkyoung; Chen, Han; Kaur, Ismeet; Takeuchi, Fumihiko; Fontanillas, Pierre; Thuillier, Dorothée; Yengo, Loic; Below, Jennifer E; Tam, Claudia H T; Wu, Ying; Abecasis, Gonçalo; Altshuler, David; Bell, Graeme I; Blangero, John; Burtt, Noél P; Duggirala, Ravindranath; Florez, Jose C; Hanis, Craig L; Seielstad, Mark; Atzmon, Gil; Chan, Juliana C N; Ma, Ronald C W; Froguel, Philippe; Wilson, James G; Bharadwaj, Dwaipayan; Dupuis, Josee; Meigs, James B; Cho, Yoon Shin; Park, Taesung; Kooner, Jaspal S; Chambers, John C; Saleheen, Danish; Kadowaki, Takashi; Tai, E Shyong; Mohlke, Karen L; Cox, Nancy J; Ferrer, Jorge; Zeggini, Eleftheria; Kato, Norihiro; Teo, Yik Ying; Boehnke, Michael; McCarthy, Mark I; Morris, Andrew P

2016-05-15

To gain insight into potential regulatory mechanisms through which the effects of variants at four established type 2 diabetes (T2D) susceptibility loci (CDKAL1, CDKN2A-B, IGF2BP2 and KCNQ1) are mediated, we undertook transancestral fine-mapping in 22 086 cases and 42 539 controls of East Asian, European, South Asian, African American and Mexican American descent. Through high-density imputation and conditional analyses, we identified seven distinct association signals at these four loci, each with allelic effects on T2D susceptibility that were homogenous across ancestry groups. By leveraging differences in the structure of linkage disequilibrium between diverse populations, and increased sample size, we localised the variants most likely to drive each distinct association signal. We demonstrated that integration of these genetic fine-mapping data with genomic annotation can highlight potential causal regulatory elements in T2D-relevant tissues. These analyses provide insight into the mechanisms through which T2D association signals are mediated, and suggest future routes to understanding the biology of specific disease susceptibility loci. © The Author 2016. Published by Oxford University Press.

A Tractography Comparison between Turboprop and Spin-Echo Echo-Planar Diffusion Tensor Imaging

PubMed Central

Gui, Minzhi; Peng, Huiling; Carew, John D.; Lesniak, Maciej S.; Arfanakis, Konstantinos

2008-01-01

The development of accurate, non-invasive methods for mapping white matter fiber-tracts is of critical importance. However, fiber-tracking is typically performed on diffusion tensor imaging (DTI) data obtained with echo-planar-based imaging techniques (EPI), which suffer from susceptibility-related image artifacts, and image warping due to eddy-currents. Thus, a number of white matter fiber-bundles mapped using EPI-based DTI data are distorted and/or terminated early. This severely limits the clinical potential of fiber-tracking. In contrast, Turboprop-MRI provides images with significantly fewer susceptibility and eddy-current-related artifacts than EPI. The purpose of this work was to compare fiber-tracking results obtained from DTI data acquired with Turboprop-DTI and EPI-based DTI. It was shown that, in brain regions near magnetic field inhomogeneities, white matter fiber-bundles obtained with EPI-based DTI were distorted and/or partially detected, when magnetic susceptibility-induced distortions were not corrected. After correction, residual distortions were still present and several fiber-tracts remained partially detected. In contrast, when using Turboprop-DTI data, all traced fiber-tracts were in agreement with known anatomy. The inter-session reproducibility of tractography results was higher for Turboprop than EPI-based DTI data in regions near field inhomogeneities. Thus, Turboprop may be a more appropriate DTI data acquisition technique for tracing white matter fibers near regions with significant magnetic susceptibility differences, as well as in longitudinal studies of such fibers. However, the intra-session reproducibility of tractography results was higher for EPI-based than Turboprop DTI data. Thus, EPI-based DTI may be more advantageous for tracing fibers minimally affected by field inhomogeneities. PMID:18621131

A tractography comparison between turboprop and spin-echo echo-planar diffusion tensor imaging.

PubMed

Gui, Minzhi; Peng, Huiling; Carew, John D; Lesniak, Maciej S; Arfanakis, Konstantinos

2008-10-01

The development of accurate, non-invasive methods for mapping white matter fiber-tracts is of critical importance. However, fiber-tracking is typically performed on diffusion tensor imaging (DTI) data obtained with echo-planar-based imaging techniques (EPI), which suffer from susceptibility-related image artifacts, and image warping due to eddy-currents. Thus, a number of white matter fiber-bundles mapped using EPI-based DTI data are distorted and/or terminated early. This severely limits the clinical potential of fiber-tracking. In contrast, Turboprop-MRI provides images with significantly fewer susceptibility and eddy-current-related artifacts than EPI. The purpose of this work was to compare fiber-tracking results obtained from DTI data acquired with Turboprop-DTI and EPI-based DTI. It was shown that, in brain regions near magnetic field inhomogeneities, white matter fiber-bundles obtained with EPI-based DTI were distorted and/or partially detected, when magnetic susceptibility-induced distortions were not corrected. After correction, residual distortions were still present and several fiber-tracts remained partially detected. In contrast, when using Turboprop-DTI data, all traced fiber-tracts were in agreement with known anatomy. The inter-session reproducibility of tractography results was higher for Turboprop than EPI-based DTI data in regions near field inhomogeneities. Thus, Turboprop may be a more appropriate DTI data acquisition technique for tracing white matter fibers near regions with significant magnetic susceptibility differences, as well as in longitudinal studies of such fibers. However, the intra-session reproducibility of tractography results was higher for EPI-based than Turboprop DTI data. Thus, EPI-based DTI may be more advantageous for tracing fibers minimally affected by field inhomogeneities.

Disaster mitigation at drainage basin of Kuranji Padang City

NASA Astrophysics Data System (ADS)

Utama, L.; Yamin, M.

2017-06-01

Floods is flooding of effect of exit water groove river because big river debit sudden its accomodation energy, happened swiftly knock over areas which is debasement, in river basin and hollow. Flow debris or which is recognized with galodo have knock over river of Kuranji year 2012 in Padang city. Area is floods disaster are: 19 Sub-District in 7 district, and hard that is district of Pauh and district of Nanggalo. Governmental claim tired loss of Rp 263,9 Billion while Government of Provinsi West Sumatera appraise loss estimated by Fourty Billion Rupiah (Padang Ekspress 28 July 2012), with detail of damage house counted 878 unit, damage religious service house 15 unit, damage irrigation 12 unit, damage bridge 6 unit, damage school 2 unit, damage health post 1 unit. Result of calculation, by using rainfall of year 2003 until year 2015 with method Gumbel, Hasper and Wedwen, got high rainfall plan is 310,00 mm, and method Melchior and Hasper floods is 1125,86 m³ / second. From result of study analyse at Citra map of correlation and image to parameters cause of floods, and use software Watershed Modelling System (WMS) this region have two class that is middle susceptance and low susceptance. Middle susceptance area is there are in middle river and downstream river, with inclination level off. Low susceptance area there is middle river. Area which have potency result the happening of floods is headwaters, because having keen ramp storey level ( 45 - 55%) and is hilly. For the mitigasi of floods disaster determined by three area evacuate that are: Sub-District Of Kelurahan Limau Manis District Of Pauh, Sub-District Of Surau Gadang District Of Nanggalo, and Sub-District Of Lambung Bukik District of Pauh, in the form of map.

Advances in Landslide Nowcasting: Evaluation of a Global and Regional Modeling Approach

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia Bach; Peters-Lidard, Christa; Adler, Robert; Hong, Yang; Kumar, Sujay; Lerner-Lam, Arthur

2011-01-01

The increasing availability of remotely sensed data offers a new opportunity to address landslide hazard assessment at larger spatial scales. A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that may experience landslide activity. This system combines a calculation of static landslide susceptibility with satellite-derived rainfall estimates and uses a threshold approach to generate a set of nowcasts that classify potentially hazardous areas. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale near real-time landslide hazard assessment efforts, it requires several modifications before it can be fully realized as an operational tool. This study draws upon a prior work s recommendations to develop a new approach for considering landslide susceptibility and hazard at the regional scale. This case study calculates a regional susceptibility map using remotely sensed and in situ information and a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America. The susceptibility map is evaluated with a regional rainfall intensity duration triggering threshold and results are compared with the global algorithm framework for the same event. Evaluation of this regional system suggests that this empirically based approach provides one plausible way to approach some of the data and resolution issues identified in the global assessment. The presented methodology is straightforward to implement, improves upon the global approach, and allows for results to be transferable between regions. The results also highlight several remaining challenges, including the empirical nature of the algorithm framework and adequate information for algorithm validation. Conclusions suggest that integrating additional triggering factors such as soil moisture may help to improve algorithm performance accuracy. The regional algorithm scenario represents an important step forward in advancing regional and global-scale landslide hazard assessment.

Spatial modelling for tsunami evacuation route in Parangtritis Village

NASA Astrophysics Data System (ADS)

Juniansah, A.; Tyas, B. I.; Tama, G. C.; Febriani, K. R.; Farda, N. M.

2018-04-01

Tsunami is a series of huge sea waves that commonly occurs because of the oceanic plate movement or tectonic activity under the sea. As a sudden hazard, the tsunami has damaged many people over the years. Parangtritis village is one of high tsunami hazard risk area in Indonesia which needs an effective tsunami risk reduction. This study aims are modelling a tsunami susceptibility map, existing assembly points evaluation, and suggesting effective evacuation routes. The susceptibility map was created using ALOS PALSAR DEM and surface roughness coefficient. The method of tsunami modelling employed inundation model developed by Berryman (2006). The results are used to determine new assembly points based on the Sentinel 2A imagery and to determine the most effective evacuation route by using network analyst. This model can be used to create detailed scale of evacuation route, but unrepresentative for assembly point that far from road network.

Gene for familial psoriasis susceptibility mapped to the distal end of human chromosome 17q

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tomfohrde, J.; Barnes, R.; Bowcock, A.

1994-05-20

A gene involved in psoriasis susceptibility was localized to the distal region of human chromosomes 17q as a result of a genome-wide linkage analysis with polymorphic microsatellites and eight multiply affected psoriasis kindreds. In the family which showed the strongest evidence for linkage, the recombination fraction between a psoriasis susceptibility locus and D17S784 was 0.04 with a maximum two-point lod score of 5.33. There was also evidence for genetic heterogeneity and although none of the linked families showed any association with HLA-Cw6, two unlinked families showed weak levels of association. This study demonstrates that is some families, psoriasis susceptibility ismore » due to variation at a single major genetic locus other than the human lymphocyte antigen locus. 28 refs., 2 figs., 1 tab.« less

Mapping soil total nitrogen of cultivated land at county scale by using hyperspectral image

NASA Astrophysics Data System (ADS)

Gu, Xiaohe; Zhang, Li Yan; Shu, Meiyan; Yang, Guijun

2018-02-01

Monitoring total nitrogen content (TNC) in the soil of cultivated land quantitively and mastering its spatial distribution are helpful for crop growing, soil fertility adjustment and sustainable development of agriculture. The study aimed to develop a universal method to map total nitrogen content in soil of cultivated land by HSI image at county scale. Several mathematical transformations were used to improve the expression ability of HSI image. The correlations between soil TNC and the reflectivity and its mathematical transformations were analyzed. Then the susceptible bands and its transformations were screened to develop the optimizing model of map soil TNC in the Anping County based on the method of multiple linear regression. Results showed that the bands of 14th, 16th, 19th, 37th and 60th with different mathematical transformations were screened as susceptible bands. Differential transformation was helpful for reducing the noise interference to the diagnosis ability of the target spectrum. The determination coefficient of the first order differential of logarithmic transformation was biggest (0.505), while the RMSE was lowest. The study confirmed the first order differential of logarithm transformation as the optimal inversion model for soil TNC, which was used to map soil TNC of cultivated land in the study area.

Landslide susceptibility mapping in three selected target zones in Afghanistan

NASA Astrophysics Data System (ADS)

Schwanghart, Wolfgang; Seegers, Joe; Zeilinger, Gerold

2015-04-01

In May 2014, a large and mobile landslide destroyed the village Ab Barek, a village in Badakshan Province, Afghanistan. The landslide caused several hundred fatalities and once again demonstrated the vulnerability of Afghanistan's population to extreme natural events following more than 30 years of civil war and violent conflict. Increasing the capacity of Afghanistan's population by strengthening the disaster preparedness and management of responsible government authorities and institutions is thus a major component of international cooperation and development strategies. Afghanistan is characterized by high relief and widely varying rock types that largely determine the spatial distribution as well as emplacement modes of mass movements. The major aim of our study is to characterize this variability by conducting a landslide susceptibility analysis in three selected target zones: Greater Kabul Area, Badakhshan Province and Takhar Province. We expand on an existing landslide database by mapping landforms diagnostic for landslides (e.g. head scarps, normal faults and tension cracks), and historical landslide scars and landslide deposits by visual interpretation of high-resolution satellite imagery. We conduct magnitude frequency analysis within subregional physiogeographic classes based on geological maps, climatological and topographic data to identify regional parameters influencing landslide magnitude and frequency. In addition, we prepare a landslide susceptibility map for each area using the Weight-of-Evidence model. Preliminary results show that the three selected target zones vastly differ in modes of landsliding. Low magnitude but frequent rockfall events are a major hazard in the Greater Kabul Area threatening buildings and infrastructure encroaching steep terrain in the city's outskirts. Mass movements in loess covered areas of Badakshan are characterized by medium to large magnitudes. This spatial variability of characteristic landslide magnitudes and modes of emplacement necessitates different strategies to assess, mitigate, and prepare for landslides in the three different target zones.

Genotyping-by-Sequencing derived High-Density Linkage Map and its Application to QTL Mapping of Flag Leaf Traits in Bread Wheat

USDA-ARS?s Scientific Manuscript database

Hard red winter wheat parents ‘Harry’ (drought tolerant) and ‘Wesley’ (drought susceptible) was used to develop a recombinant inbred population to identify genomic regions associated with drought and adaptation. To precisely map genomic regions high-density linkage maps are a prerequisite. In this s...

Genetically based location from triploid populations and gene ontology of a 3.3-mb genome region linked to Alternaria brown spot resistance in citrus reveal clusters of resistance genes.

PubMed

Cuenca, José; Aleza, Pablo; Vicent, Antonio; Brunel, Dominique; Ollitrault, Patrick; Navarro, Luis

2013-01-01

Genetic analysis of phenotypical traits and marker-trait association in polyploid species is generally considered as a challenge. In the present work, different approaches were combined taking advantage of the particular genetic structures of 2n gametes resulting from second division restitution (SDR) to map a genome region linked to Alternaria brown spot (ABS) resistance in triploid citrus progeny. ABS in citrus is a serious disease caused by the tangerine pathotype of the fungus Alternaria alternata. This pathogen produces ACT-toxin, which induces necrotic lesions on fruit and young leaves, defoliation and fruit drop in susceptible genotypes. It is a strong concern for triploid breeding programs aiming to produce seedless mandarin cultivars. The monolocus dominant inheritance of susceptibility, proposed on the basis of diploid population studies, was corroborated in triploid progeny. Bulk segregant analysis coupled with genome scan using a large set of genetically mapped SNP markers and targeted genetic mapping by half tetrad analysis, using SSR and SNP markers, allowed locating a 3.3 Mb genomic region linked to ABS resistance near the centromere of chromosome III. Clusters of resistance genes were identified by gene ontology analysis of this genomic region. Some of these genes are good candidates to control the dominant susceptibility to the ACT-toxin. SSR and SNP markers were developed for efficient early marker-assisted selection of ABS resistant hybrids.

Genetically Based Location from Triploid Populations and Gene Ontology of a 3.3-Mb Genome Region Linked to Alternaria Brown Spot Resistance in Citrus Reveal Clusters of Resistance Genes

PubMed Central

Cuenca, José; Aleza, Pablo; Vicent, Antonio; Brunel, Dominique; Ollitrault, Patrick; Navarro, Luis

2013-01-01

Genetic analysis of phenotypical traits and marker-trait association in polyploid species is generally considered as a challenge. In the present work, different approaches were combined taking advantage of the particular genetic structures of 2n gametes resulting from second division restitution (SDR) to map a genome region linked to Alternaria brown spot (ABS) resistance in triploid citrus progeny. ABS in citrus is a serious disease caused by the tangerine pathotype of the fungus Alternaria alternata. This pathogen produces ACT-toxin, which induces necrotic lesions on fruit and young leaves, defoliation and fruit drop in susceptible genotypes. It is a strong concern for triploid breeding programs aiming to produce seedless mandarin cultivars. The monolocus dominant inheritance of susceptibility, proposed on the basis of diploid population studies, was corroborated in triploid progeny. Bulk segregant analysis coupled with genome scan using a large set of genetically mapped SNP markers and targeted genetic mapping by half tetrad analysis, using SSR and SNP markers, allowed locating a 3.3 Mb genomic region linked to ABS resistance near the centromere of chromosome III. Clusters of resistance genes were identified by gene ontology analysis of this genomic region. Some of these genes are good candidates to control the dominant susceptibility to the ACT-toxin. SSR and SNP markers were developed for efficient early marker-assisted selection of ABS resistant hybrids. PMID:24116149

Landslide susceptibility revealed by LIDAR imagery and historical records, Seattle, Washington

USGS Publications Warehouse

Schulz, W.H.

2007-01-01

Light detection and ranging (LIDAR) data were used to visually map landslides, headscarps, and denuded slopes in Seattle, Washington. Four times more landslides were mapped than by previous efforts that used aerial photographs. The mapped landforms (landslides, headscarps, and denuded slopes) were created by many individual landslides. The spatial distribution of mapped landforms and 1308 historical landslides show that historical landslide activity has been concentrated on the mapped landforms, and that most of the landslide activity that created the landforms was prehistoric. Thus, the spatial densities of historical landslides on the landforms provide approximations of the landforms' relative susceptibilities to future landsliding. Historical landslide characteristics appear to be closely related to landform type so relative susceptibilities were determined for landslides with various characteristics. No strong relations were identified between stratigraphy and landslide occurrence; however, landslide characteristics and slope morphology appear to be related to stratigraphic conditions. Human activity is responsible for causing about 80% of historical Seattle landslides. The distribution of mapped landforms and human-caused landslides suggests the probable characteristics of future human-caused landslides on each of the landforms. The distribution of mapped landforms and historical landslides suggests that erosion of slope-toes by surface water has been a necessary condition for causing Seattle landslides. Human activity has largely arrested this erosion, which implies that landslide activity will decrease with time as hillsides naturally stabilize. However, evaluation of glacial-age analogs of areas of recent slope-toe erosion suggests that landslide activity in Seattle will continue for the foreseeable future. ?? 2006 Elsevier B.V. All rights reserved.

Producing landslide susceptibility maps by utilizing machine learning methods. The case of Finikas catchment basin, North Peloponnese, Greece.

NASA Astrophysics Data System (ADS)

Tsangaratos, Paraskevas; Ilia, Ioanna; Loupasakis, Constantinos; Papadakis, Michalis; Karimalis, Antonios

2017-04-01

The main objective of the present study was to apply two machine learning methods for the production of a landslide susceptibility map in the Finikas catchment basin, located in North Peloponnese, Greece and to compare their results. Specifically, Logistic Regression and Random Forest were utilized, based on a database of 40 sites classified into two categories, non-landslide and landslide areas that were separated into a training dataset (70% of the total data) and a validation dataset (remaining 30%). The identification of the areas was established by analyzing airborne imagery, extensive field investigation and the examination of previous research studies. Six landslide related variables were analyzed, namely: lithology, elevation, slope, aspect, distance to rivers and distance to faults. Within the Finikas catchment basin most of the reported landslides were located along the road network and within the residential complexes, classified as rotational and translational slides, and rockfalls, mainly caused due to the physical conditions and the general geotechnical behavior of the geological formation that cover the area. Each landslide susceptibility map was reclassified by applying the Geometric Interval classification technique into five classes, namely: very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility, and very high susceptibility. The comparison and validation of the outcomes of each model were achieved using statistical evaluation measures, the receiving operating characteristic and the area under the success and predictive rate curves. The computation process was carried out using RStudio an integrated development environment for R language and ArcGIS 10.1 for compiling the data and producing the landslide susceptibility maps. From the outcomes of the Logistic Regression analysis it was induced that the highest b coefficient is allocated to lithology and slope, which was 2.8423 and 1.5841, respectively. From the estimation of the mean decrease in Gini coefficient performed during the application of Random Forest and the mean decrease in accuracy the most important variable is slope followed by lithology, aspect, elevation, distance from river network, and distance from faults, while the most used variables during the training phase were the variable aspect (21.45%), slope (20.53%) and lithology (19.84%). The outcomes of the analysis are consistent with previous studies concerning the area of research, which have indicated the high influence of lithology and slope in the manifestation of landslides. High percentage of landslide occurrence has been observed in Plio-Pleistocene sediments, flysch formations, and Cretaceous limestone. Also the presences of landslides have been associated with the degree of weathering and fragmentation, the orientation of the discontinuities surfaces and the intense morphological relief. The most accurate model was Random Forest which identified correctly 92.00% of the instances during the training phase, followed by the Logistic Regression 89.00%. The same pattern of accuracy was calculated during the validation phase, in which the Random Forest achieved a classification accuracy of 93.00%, while the Logistic Regression model achieved an accuracy of 91.00%. In conclusion, the outcomes of the study could be a useful cartographic product to local authorities and government agencies during the implementation of successful decision-making and land use planning strategies. Keywords: Landslide Susceptibility, Logistic Regression, Random Forest, GIS, Greece.

Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

NASA Astrophysics Data System (ADS)

Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

2014-06-01

This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

SINMAP Modeling of an active landslide area in the Swabian Alb

NASA Astrophysics Data System (ADS)

Terhorst, Birgit; Jaeger, Daniel

2015-04-01

Landslides are a common hazard in German low mountain areas such as the Swabian Alb. As areas of former landslides are highly prone to secondary movements, this study aims to assess the susceptibility for landslide hazard around Mössingen-Öschingen, a region consistently affected by landslides during the last decades. Based on the history and development of mass movements and a detailed geomorphological map, slope stability was calculated using SINMAP (Stability Index Mapping). SINMAP (Pack et al., 1998; Tarboton, 1997) is based on the "infinite slope stability model" by Hammond et al. (1992) and Montgomery and Dietrich (1994) describing the ratio of slope stabilizing factors (e.g. cohesion) and slope destabilizing factors (e.g. gravitation) on a slip surface parallel to the slope. Most input parameters are determined by the relief and therefore, can be calculated from a digital terrain model (DTM, resolution 5 m). Based on the local morphology and geology, a total of 10 'calibration regions', each with similar hydrogeological characteristics, were defined. Further input parameters were: Shear strength via friction angle (Phi), cohesion (C) and hydraulic conductivity (T/R). The data was obtained from soil mechanical assessments and field/laboratory analyses. As a result, a specific stability index is calculated, describing the susceptibility of a slope movement. In a first step, the 'topographic wetness index' (derived from catchment area, slope gradient and hydraulic conductivity) was calculated. Results show several preferred (natural) drainage channels with generally higher water saturations in morphological depressions. Several of them can be linked to the location of damaged houses in the settlement area on the lower slope. The SINMAP calculation clearly revealed the impermeable Callovian clay layers as most prone to slope movements. A comparison of the susceptibility map with slide masses which were mapped during a field survey showed generally good agreements. This was in particular true for the slopes of the "Landhaussiedlung", a small settlement area east of Mössingen-Öschingen. In the uphill areas, a large landslide was triggered on June 3rd, 2013, mainly caused by heavy rainfalls during the days before. The scarp/slip surface was situated in the Callovian clay layers and in an area which was shown as susceptible for slope movements by the SINMAP model earlier Terhorst and Kreja (2009). The movement processes reactivated an old slide mass, which reached the outermost parts of the settlement area and damaged the densely built-up underground of the Landhaussiedlung. Although no house was destroyed completely by the slide mass, the induced pressure caused severe damages, rendering the buildings uninhabitable and leading to the evacuation of the Landhaussiedlung. The results show, that the modeling provided a solid identification of the vulnerable slope areas. The recent landslide area is almost completely situated in a region modeled as vulnerable for slope movements. Therefore, the landslide event of 2013 practically validated the susceptibility map. On the base of solid data and under consideration of detailed and differentiated information, SINMAP is a powerful tool for the assessment of susceptibilities for translational slides. Hammond, C., Hall, D., Miller, S., Swetik, P., 1992. Level I Stability Analysis (LISA) documentation for version 2.0. General Technical Report, INT-285. U.S. Deptartment of Agriculture, Forest Service, Intermountain Research Station, Ogden. Montgomery, D.R., Dietrich, W.E., 1994. A Physically Based Model for the Topographic Control on Shallow Landsliding. Water Resources Research, 30(4), 1153-1171. Pack, R.T., Tarboton, D.G., Goodwin, C.N., 1998. The SINMAP approach to terrain stability mapping, 8th Congress of the International Association of Engineering Geology, Vancouver, Canada, pp. 8. Tarboton, D.G., 1997. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research, 33(2), 309-319. Terhorst, B., Kreja, R., 2009. Slope stability modelling with SINMAP in a settlement area of the Swabian Alb. Landslides, 6(4), 309-319.

Distortion correction in EPI at ultra-high-field MRI using PSF mapping with optimal combination of shift detection dimension.

PubMed

Oh, Se-Hong; Chung, Jun-Young; In, Myung-Ho; Zaitsev, Maxim; Kim, Young-Bo; Speck, Oliver; Cho, Zang-Hee

2012-10-01

Despite its wide use, echo-planar imaging (EPI) suffers from geometric distortions due to off-resonance effects, i.e., strong magnetic field inhomogeneity and susceptibility. This article reports a novel method for correcting the distortions observed in EPI acquired at ultra-high-field such as 7 T. Point spread function (PSF) mapping methods have been proposed for correcting the distortions in EPI. The PSF shift map can be derived either along the nondistorted or the distorted coordinates. Along the nondistorted coordinates more information about compressed areas is present but it is prone to PSF-ghosting artifacts induced by large k-space shift in PSF encoding direction. In contrast, shift maps along the distorted coordinates contain more information in stretched areas and are more robust against PSF-ghosting. In ultra-high-field MRI, an EPI contains both compressed and stretched regions depending on the B0 field inhomogeneity and local susceptibility. In this study, we present a new geometric distortion correction scheme, which selectively applies the shift map with more information content. We propose a PSF-ghost elimination method to generate an artifact-free pixel shift map along nondistorted coordinates. The proposed method can correct the effects of the local magnetic field inhomogeneity induced by the susceptibility effects along with the PSF-ghost artifact cancellation. We have experimentally demonstrated the advantages of the proposed method in EPI data acquisitions in phantom and human brain using 7-T MRI. Copyright © 2011 Wiley Periodicals, Inc.

MR Measurement of Alloy Magnetic Susceptibility: Towards Developing Tissue-Susceptibility Matched Metals

PubMed Central

Astary, Garrett W.; Peprah, Marcus K.; Fisher, Charles R.; Stewart, Rachel L.; Carney, Paul R.; Sarntinoranont, Malisa; Meisel, Mark W.; Manuel, Michele V.; Mareci, Thomas H.

2013-01-01

Magnetic resonance imaging (MRI) can be used to relate structure to function mapped with high-temporal resolution electrophysiological recordings using metal electrodes. Additionally, MRI may be used to guide the placement of electrodes or conductive cannula in the brain. However, the magnetic susceptibility mismatch between implanted metals and surrounding brain tissue can severely distort MR images and spectra, particularly in high magnetic fields. In this study, we present a modified MR method of characterizing the magnetic susceptibility of materials that can be used to develop biocompatible, metal alloys that match the susceptibility of host tissue in order to eliminate MR distortions proximal to the implant. This method was applied at 4.7 T and 11.1 T to measure the susceptibility of a model solid-solution alloy of Cu and Sn, which is inexpensive but not biocompatible. MR-derived relative susceptibility values of four different compositions of Cu-Sn alloy deviated by less than 3.1% from SQUID magnetometry absolute susceptibility measurements performed up to 7 T. These results demonstrate that the magnetic susceptibility varies linearly with atomic percentage in these solid-solution alloys, but are not simply the weighted average of Cu and Sn magnetic susceptibilities. Therefore susceptibility measurements are necessary when developing susceptibility-matched, solid-solution alloys for the elimination of susceptibility artifacts in MR. This MR method does not require any specialized equipment and is free of geometrical constraints, such as sample shape requirements associated with SQUID magnetometry, so the method can be used at all stages of fabrication to guide the development of a susceptibility matched, biocompatible device. PMID:23727587

Crustal interpretation of the MAGSAT data in the continental United States

NASA Technical Reports Server (NTRS)

Won, I. J.; Son, K. H.

1982-01-01

The processing of MAGSAT scalar data to construct a crustal magnetic anomaly map over the continental U.S. involves removal of the reference field model, a path-by-path subtraction of a low order polynomial through a least-squares fit to reduce orbital offset errors, and a two dimensional spectral filtering to mitigate the spectral bias induced by the path-by-path orbital correction scheme. The resultant anomaly map shows reasonably good correlations with an aeromagnetic map derived from the project MAGNET. Prominent satellite magnetic anomalies are identified in terms of geological provinces and age boundaries. An inversion method was applied to MAGSAT data which produces both the Curie depth topography and laterally varying magnetic susceptibility of the crust. A contoured Curie depth map thus derived shows general agreements with a crustal thickness map based on seismic data.

Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression

NASA Astrophysics Data System (ADS)

García-Rodríguez, M. J.; Malpica, J. A.; Benito, B.; Díaz, M.

2008-03-01

This work has evaluated the probability of earthquake-triggered landslide occurrence in the whole of El Salvador, with a Geographic Information System (GIS) and a logistic regression model. Slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness are the predictor variables used to determine the dependent variable of occurrence or non-occurrence of landslides within an individual grid cell. The results illustrate the importance of terrain roughness and soil type as key factors within the model — using only these two variables the analysis returned a significance level of 89.4%. The results obtained from the model within the GIS were then used to produce a map of relative landslide susceptibility.

Scale and spatial distribution assessment of rainfall-induced landslides in a catchment with mountain roads

NASA Astrophysics Data System (ADS)

Tseng, Chih-Ming; Chen, Yie-Ruey; Wu, Szu-Mi

2018-03-01

This study focused on landslides in a catchment with mountain roads that were caused by Nanmadol (2011) and Kong-rey (2013) typhoons. Image interpretation techniques were employed to for satellite images captured before and after the typhoons to derive the surface changes. A multivariate hazard evaluation method was adopted to establish a landslide susceptibility assessment model. The evaluation of landslide locations and relationship between landslide and predisposing factors is preparatory for assessing and mapping landslide susceptibility. The results can serve as a reference for preventing and mitigating slope disasters on mountain roads.

Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China

NASA Astrophysics Data System (ADS)

Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Li, Yuanyao; Catani, Filippo; Pourghasemi, Hamid Reza

2018-03-01

Landslide is a common natural hazard and responsible for extensive damage and losses in mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was taken as a case study for landslide susceptibility assessment in order to develop effective risk prevention and mitigation strategies. To begin, 202 landslides were identified, including 95 colluvial landslides and 107 rockfalls. Twelve landslide causal factor maps were prepared initially, and the relationship between these factors and each landslide type was analyzed using the information value model. Later, the unimportant factors were selected and eliminated using the information gain ratio technique. The landslide locations were randomly divided into two groups: 70% for training and 30% for verifying. Two machine learning models: the support vector machine (SVM) and artificial neural network (ANN), and a multivariate statistical model: the logistic regression (LR), were applied for landslide susceptibility modeling (LSM) for each type. The LSM index maps, obtained from combining the assessment results of the two landslide types, were classified into five levels. The performance of the LSMs was evaluated using the receiver operating characteristics curve and Friedman test. Results show that the elimination of noise-generating factors and the separated modeling of each landslide type have significantly increased the prediction accuracy. The machine learning models outperformed the multivariate statistical model and SVM model was found ideal for the case study area.

On the influence of zero-padding on the nonlinear operations in Quantitative Susceptibility Mapping.

PubMed

Eskreis-Winkler, Sarah; Zhou, Dong; Liu, Tian; Gupta, Ajay; Gauthier, Susan A; Wang, Yi; Spincemaille, Pascal

2017-01-01

Zero padding is a well-studied interpolation technique that improves image visualization without increasing image resolution. This interpolation is often performed as a last step before images are displayed on clinical workstations. Here, we seek to demonstrate the importance of zero padding before rather than after performing non-linear post-processing algorithms, such as Quantitative Susceptibility Mapping (QSM). To do so, we evaluate apparent spatial resolution, relative error and depiction of multiple sclerosis (MS) lesions on images that were zero padded prior to, in the middle of, and after the application of the QSM algorithm. High resolution gradient echo (GRE) data were acquired on twenty MS patients, from which low resolution data were derived using k-space cropping. Pre-, mid-, and post-zero padded QSM images were reconstructed from these low resolution data by zero padding prior to field mapping, after field mapping, and after susceptibility mapping, respectively. Using high resolution QSM as the gold standard, apparent spatial resolution, relative error, and image quality of the pre-, mid-, and post-zero padded QSM images were measured and compared. Both the accuracy and apparent spatial resolution of the pre-zero padded QSM was higher than that of mid-zero padded QSM (p<0.001; p<0.001), which was higher than that of post-zero padded QSM (p<0.001; p<0.001). The image quality of pre-zero padded reconstructions was higher than that of mid- and post-zero padded reconstructions (p=0.004; p<0.001). Zero padding of the complex GRE data prior to nonlinear susceptibility mapping improves image accuracy and apparent resolution compared to zero padding afterwards. It also provides better delineation of MS lesion geometry, which may improve lesion subclassification and disease monitoring in MS patients. Copyright © 2016 Elsevier Inc. All rights reserved.

TRIGRS Application for landslide susceptibility mapping

NASA Astrophysics Data System (ADS)

Sugiarti, K.; Sukristiyanti, S.

2018-02-01

Research on landslide susceptibility has been carried out using several different methods. TRIGRS is a modeling program for landslide susceptibility by considering pore water pressure changes due to infiltration of rainfall. This paper aims to present a current state-of-the-art science on the development and application of TRIGRS. Some limitations of TRIGRS, some developments of it to improve its modeling capability, and some examples of the applications of some versions of it to model the effect of rainfall variation on landslide susceptibility are reviewed and discussed.

Debris flows susceptibility mapping under tropical rain conditions in Rwanda.

NASA Astrophysics Data System (ADS)

Nduwayezu, Emmanuel; Nsengiyumva, Jean-Baptiste; BUgnon, Pierre-Charles; Jaboyedoff, Michel; Derron, Marc-Henri

2017-04-01

Rwanda is a densely populated country. It means that all the space is exploited, including sometimes areas with very steep slopes. This has as for consequences that during the rainy season slopes with human activities are affected by gravitational processes, mostly debris and mud flows and shallow landslides. The events of early May 2016 (May 8 and 9), with more than 50 deaths, are an illustration of these frequents landslides and inundations. The goal of this work is to produce a susceptibility map for debris/mud flows at regional/national scale. Main available pieces of data are a national digital terrain model at 10m resolution, bedrock and soil maps, and information collected during field visits on some specific localities. The first step is the characterization of the slope angle distribution for the different types of bedrock or soils (decomposition in Gaussian populations). Then, the combination of this information with other geomorphic and hydrologic parameters is used to define potential source areas of debris flows. Finally, propagation maps of debris flows are produced using FLOW-R (Horton et al. 2013). Horton, P., Jaboyedoff, M., Rudaz, B., and Zimmermann, M.: Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale, Nat. Hazards Earth Syst. Sci., 13, 869-885, doi:10.5194/nhess-13-869-2013, 2013. The paper is in open access.

Assessment of landslide distribution map reliability in Niigata prefecture - Japan using frequency ratio approach

NASA Astrophysics Data System (ADS)

Rahardianto, Trias; Saputra, Aditya; Gomez, Christopher

2017-07-01

Research on landslide susceptibility has evolved rapidly over the few last decades thanks to the availability of large databases. Landslide research used to be focused on discreet events but the usage of large inventory dataset has become a central pillar of landslide susceptibility, hazard, and risk assessment. Indeed, extracting meaningful information from the large database is now at the forth of geoscientific research, following the big-data research trend. Indeed, the more comprehensive information of the past landslide available in a particular area is, the better the produced map will be, in order to support the effective decision making, planning, and engineering practice. The landslide inventory data which is freely accessible online gives an opportunity for many researchers and decision makers to prevent casualties and economic loss caused by future landslides. This data is advantageous especially for areas with poor landslide historical data. Since the construction criteria of landslide inventory map and its quality evaluation remain poorly defined, the assessment of open source landslide inventory map reliability is required. The present contribution aims to assess the reliability of open-source landslide inventory data based on the particular topographical setting of the observed area in Niigata prefecture, Japan. Geographic Information System (GIS) platform and statistical approach are applied to analyze the data. Frequency ratio method is utilized to model and assess the landslide map. The outcomes of the generated model showed unsatisfactory results with AUC value of 0.603 indicate the low prediction accuracy and unreliability of the model.

Dissection of Host Susceptibility to Bacterial Infections and Its Toxins.

PubMed

Nashef, Aysar; Agbaria, Mahmoud; Shusterman, Ariel; Lorè, Nicola Ivan; Bragonzi, Alessandra; Wiess, Ervin; Houri-Haddad, Yael; Iraqi, Fuad A

2017-01-01

Infection is one of the leading causes of human mortality and morbidity. Exposure to microbial agents is obviously required. However, also non-microbial environmental and host factors play a key role in the onset, development and outcome of infectious disease, resulting in large of clinical variability between individuals in a population infected with the same microbe. Controlled and standardized investigations of the genetics of susceptibility to infectious disease are almost impossible to perform in humans whereas mouse models allow application of powerful genomic techniques to identify and validate causative genes underlying human diseases with complex etiologies. Most of current animal models used in complex traits diseases genetic mapping have limited genetic diversity. This limitation impedes the ability to create incorporated network using genetic interactions, epigenetics, environmental factors, microbiota, and other phenotypes. A novel mouse genetic reference population for high-resolution mapping and subsequently identifying genes underlying the QTL, namely the Collaborative Cross (CC) mouse genetic reference population (GRP) was recently developed. In this chapter, we discuss a variety of approaches using CC mice for mapping genes underlying quantitative trait loci (QTL) to dissect the host response to polygenic traits, including infectious disease caused by bacterial agents and its toxins.

Increased Iron Deposition on Brain Quantitative Susceptibility Mapping Correlates with Decreased Cognitive Function in Alzheimer's Disease.

PubMed

Du, Lei; Zhao, Zifang; Cui, Ailing; Zhu, Yijiang; Zhang, Lu; Liu, Jing; Shi, Sumin; Fu, Chao; Han, Xiaowei; Gao, Wenwen; Song, Tianbin; Xie, Lizhi; Wang, Lei; Sun, Shilong; Guo, Runcai; Ma, Guolin

2018-05-15

The excessive accumulation of iron in deep gray structures is an important pathological characteristic in patients with Alzheimer's disease (AD). Quantitative susceptibility mapping (QSM) is more specific than other imaging-based iron measurement modalities and allows noninvasive assessment of tissue magnetic susceptibility, which has been shown to correlate well with brain iron levels. This study aimed to investigate the correlations between the magnetic susceptibility values of deep gray matter nuclei and the cognitive functions assessed by mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) in patients with mild and moderate AD. Thirty subjects with mild and moderate AD and 30 age- and sex-matched healthy controls were scanned with a 3.0 T magnetic resonance imaging (MRI) scanner. The magnetic susceptibilities of the regions of interest (ROIs), including caudate nucleus (Cd), putamen (Pt), globus pallidus (Gp), thalamus (Th), red nucleus (Rn), substantia nigra (Sn), and dentate nucleus (Dn), were quantified by QSM. We found that the susceptibility values of the bilateral Cd and Pt were significantly higher in AD patients than the controls ( P < 0.05). In contrast, bilateral Rn had significantly lower susceptibility values in AD than the controls. Regardless of gender and age, the increase of magnetic susceptibility in the left Cd was significantly correlated with the decrease of MMSE scores and MoCA scores ( P < 0.05). Our study indicated that magnetic susceptibility value of left Cd could be potentially used as a biomarker of disease severity in mild and moderate AD.

Carcinogen susceptibility is regulated by genome architecture and predicts cancer mutagenesis.

PubMed

García-Nieto, Pablo E; Schwartz, Erin K; King, Devin A; Paulsen, Jonas; Collas, Philippe; Herrera, Rafael E; Morrison, Ashby J

2017-10-02

The development of many sporadic cancers is directly initiated by carcinogen exposure. Carcinogens induce malignancies by creating DNA lesions (i.e., adducts) that can result in mutations if left unrepaired. Despite this knowledge, there has been remarkably little investigation into the regulation of susceptibility to acquire DNA lesions. In this study, we present the first quantitative human genome-wide map of DNA lesions induced by ultraviolet (UV) radiation, the ubiquitous carcinogen in sunlight that causes skin cancer. Remarkably, the pattern of carcinogen susceptibility across the genome of primary cells significantly reflects mutation frequency in malignant melanoma. Surprisingly, DNase-accessible euchromatin is protected from UV, while lamina-associated heterochromatin at the nuclear periphery is vulnerable. Many cancer driver genes have an intrinsic increase in carcinogen susceptibility, including the BRAF oncogene that has the highest mutation frequency in melanoma. These findings provide a genome-wide snapshot of DNA injuries at the earliest stage of carcinogenesis. Furthermore, they identify carcinogen susceptibility as an origin of genome instability that is regulated by nuclear architecture and mirrors mutagenesis in cancer. © 2017 The Authors.

Ex-vivo quantitative susceptibility mapping of human brain hemispheres

PubMed Central

Kotrotsou, Aikaterini; Tamhane, Ashish A.; Dawe, Robert J.; Kapasi, Alifiya; Leurgans, Sue E.; Schneider, Julie A.; Bennett, David A.; Arfanakis, Konstantinos

2017-01-01

Ex-vivo brain quantitative susceptibility mapping (QSM) allows investigation of brain characteristics at essentially the same point in time as histopathologic examination, and therefore has the potential to become an important tool for determining the role of QSM as a diagnostic and monitoring tool of age-related neuropathologies. In order to be able to translate the ex-vivo QSM findings to in-vivo, it is crucial to understand the effects of death and chemical fixation on brain magnetic susceptibility measurements collected ex-vivo. Thus, the objective of this work was twofold: a) to assess the behavior of magnetic susceptibility in both gray and white matter of human brain hemispheres as a function of time postmortem, and b) to establish the relationship between in-vivo and ex-vivo gray matter susceptibility measurements on the same hemispheres. Five brain hemispheres from community-dwelling older adults were imaged ex-vivo with QSM on a weekly basis for six weeks postmortem, and the longitudinal behavior of ex-vivo magnetic susceptibility in both gray and white matter was assessed. The relationship between in-vivo and ex-vivo gray matter susceptibility measurements was investigated using QSM data from eleven older adults imaged both antemortem and postmortem. No systematic change in ex-vivo magnetic susceptibility of gray or white matter was observed over time postmortem. Additionally, it was demonstrated that, gray matter magnetic susceptibility measured ex-vivo may be well modeled as a linear function of susceptibility measured in-vivo. In conclusion, magnetic susceptibility in gray and white matter measured ex-vivo with QSM does not systematically change in the first six weeks after death. This information is important for future cross-sectional ex-vivo QSM studies of hemispheres imaged at different postmortem intervals. Furthermore, the linear relationship between in-vivo and ex-vivo gray matter magnetic susceptibility suggests that ex-vivo QSM captures information linked to antemortem gray matter magnetic susceptibility, which is important for translation of ex-vivo QSM findings to in-vivo. PMID:29261693

Admixture Aberration Analysis: Application to Mapping in Admixed Population Using Pooled DNA

NASA Astrophysics Data System (ADS)

Bercovici, Sivan; Geiger, Dan

Admixture mapping is a gene mapping approach used for the identification of genomic regions harboring disease susceptibility genes in the case of recently admixed populations such as African Americans. We present a novel method for admixture mapping, called admixture aberration analysis (AAA), that uses a DNA pool of affected admixed individuals. We demonstrate through simulations that AAA is a powerful and economical mapping method under a range of scenarios, capturing complex human diseases such as hypertension and end stage kidney disease. The method has a low false-positive rate and is robust to deviation from model assumptions. Finally, we apply AAA on 600 prostate cancer-affected African Americans, replicating a known risk locus. Simulation results indicate that the method can yield over 96% reduction in genotyping. Our method is implemented as a Java program called AAAmap and is freely available.

A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea

NASA Astrophysics Data System (ADS)

Vasu, Nikhil N.; Lee, Seung-Rae

2016-06-01

An ever-increasing trend of extreme rainfall events in South Korea owing to climate change is causing shallow landslides and debris flows in mountains that cover 70% of the total land area of the nation. These catastrophic, gravity-driven processes cost the government several billion KRW (South Korean Won) in losses in addition to fatalities every year. The most common type of landslide observed is the shallow landslide, which occurs at 1-3 m depth, and may mobilize into more catastrophic flow-type landslides. Hence, to predict potential landslide areas, susceptibility maps are developed in a geographical information system (GIS) environment utilizing available morphological, hydrological, geotechnical, and geological data. Landslide susceptibility models were developed using 163 landslide points and an equal number of nonlandslide points in Mt. Woomyeon, Seoul, and 23 landslide conditioning factors. However, because not all of the factors contribute to the determination of the spatial probability for landslide initiation, and a simple filter or wrapper-based approach is not efficient in identifying all of the relevant features, a feedback-loop-based hybrid algorithm was implemented in conjunction with a learning scheme called an extreme learning machine, which is based on a single-layer, feed-forward network. Validation of the constructed susceptibility model was conducted using a testing set of landslide inventory data through a prediction rate curve. The model selected 13 relevant conditioning factors out of the initial 23; and the resulting susceptibility map shows a success rate of 85% and a prediction rate of 89.45%, indicating a good performance, in contrast to the low success and prediction rate of 69.19% and 56.19%, respectively, as obtained using a wrapper technique.

Assessing Landslide Characteristics and Developing a Landslide Potential Hazard Map in Rwanda and Uganda Using NASA Earth Observations

NASA Astrophysics Data System (ADS)

Sinclair, L.; Conner, P.; le Roux, J.; Finley, T.

2015-12-01

The International Emergency Disasters Database indicates that a total of 482 people have been killed and another 27,530 have been affected by landslides in Rwanda and Uganda, although the actual numbers are thought to be much higher. Data for individual countries are poorly tracked, but hotspots for devastating landslides occur throughout Rwanda and Uganda due to the local topography and soil type, intense rainfall events, and deforestation. In spite of this, there has been little research in this region that utilizes satellite imagery to estimate areas susceptible to landslides. This project utilized Landsat 8 Operational Land Imager (OLI) data and Google Earth to identify landslides that occurred within the study area. These landslides were then added to SERVIR's Global Landslide Catalog (GLC). Next, Landsat 8 OLI, the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM), and Shuttle Radar Topography Mission Version 2 (SRTM V2) data were used to create a Landslide Susceptibility Map. This was combined with population data from the Socioeconomic Data and Applications Center (SEDAC) to create a Landslide Hazard map. A preliminary assessment of the relative performance of GPM and TRMM in identifying landslide conditions was also performed. The additions to the GLC, the Landslide Susceptibility Map, the Landslide Hazard Map, and the preliminary assessment of satellite rainfall performance will be used by SERVIR and the Regional Centre for Mapping of Resources for Development (RCMRD) for disaster risk management, land use planning, and determining landslide conditions and moisture thresholds.

Shelf life extension of whole Norway lobster Nephrops norvegicus using modified atmosphere packaging.

PubMed

Gornik, Sebastian G; Albalat, Amaya; Theethakaew, Chonchanok; Neil, Douglas M

2013-11-01

Once a nuisance by-catch, today the Norway lobster (Nephrops norvegicus) is a valuable UK fisheries commodity. Unfortunately, the species is very susceptible to quality deterioration post harvest as it quickly develops black spots and also spoils rapidly due to bacterial growth. Treatment with chemicals can stop the blackening and carefully monitored cold storage can result in a sensory shelf life of up to 6.5 days. The high susceptibility to spoilage greatly restricts the extent to which N. norvegicus can be distributed to retailers and displayed for sale. The application of modified atmosphere (MA) could be extremely beneficial, allowing the chilled product to stay fresh for a long period of time, thus ensuring higher sales. In the present study, we identified a gas mix for the MA packaging (MAP) of whole N. norvegicus lobster into 200 g retail packs. Our results show that a shelf life extension to 13 days can be achieved when retail packs are stored in MAP at 1 °C. Effectiveness of the MAP was evaluated by using a newly developed QIM for MA-packaged whole N. norvegicus and also by analyzing bacterial plate counts. Changes in the microflora and effects of different storage temperatures on the quality of the MA packs are also presented. The main specific spoilage organism (SSO) of modified atmosphere packaged Norway lobster is Photobacterium phosphoreum. © 2013.

The genetic architecture of resistance to virus infection in Drosophila.

PubMed

Cogni, Rodrigo; Cao, Chuan; Day, Jonathan P; Bridson, Calum; Jiggins, Francis M

2016-10-01

Variation in susceptibility to infection has a substantial genetic component in natural populations, and it has been argued that selection by pathogens may result in it having a simpler genetic architecture than many other quantitative traits. This is important as models of host-pathogen co-evolution typically assume resistance is controlled by a small number of genes. Using the Drosophila melanogaster multiparent advanced intercross, we investigated the genetic architecture of resistance to two naturally occurring viruses, the sigma virus and DCV (Drosophila C virus). We found extensive genetic variation in resistance to both viruses. For DCV resistance, this variation is largely caused by two major-effect loci. Sigma virus resistance involves more genes - we mapped five loci, and together these explained less than half the genetic variance. Nonetheless, several of these had a large effect on resistance. Models of co-evolution typically assume strong epistatic interactions between polymorphisms controlling resistance, but we were only able to detect one locus that altered the effect of the main effect loci we had mapped. Most of the loci we mapped were probably at an intermediate frequency in natural populations. Overall, our results are consistent with major-effect genes commonly affecting susceptibility to infectious diseases, with DCV resistance being a near-Mendelian trait. © 2016 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

Evaluation of subsidence hazard in mantled karst setting: a case study from Val d'Orléans (France)

NASA Astrophysics Data System (ADS)

Perrin, Jérôme; Cartannaz, Charles; Noury, Gildas; Vanoudheusden, Emilie

2015-04-01

Soil subsidence/collapse is a major geohazard occurring in karst region. It occurs as suffosion or dropout sinkholes developing in the soft cover. Less frequently it corresponds to a breakdown of karst void ceiling (i.e., collapse sinkhole). This hazard can cause significant engineering challenges. Therefore decision-makers require the elaboration of methodologies for reliable predictions of such hazards (e.g., karst subsidence susceptibility and hazards maps, early-warning monitoring systems). A methodological framework was developed to evaluate relevant conditioning factors favouring subsidence (Perrin et al. submitted) and then to combine these factors to produce karst subsidence susceptibility maps. This approach was applied to a mantled karst area south of Paris (Val d'Orléans). Results show the significant roles of the overburden lithology (presence/absence of low-permeability layer) and of the karst aquifer piezometric surface position within the overburden. In parallel, an experimental site has been setup to improve the understanding of key processes leading to subsidence/collapse and includes piezometers for measurements of water levels and physico-chemical parameters in both the alluvial and karst aquifers as well as surface deformation monitoring. Results should help in designing monitoring systems to anticipate occurrence of subsidence/collapse. Perrin J., Cartannaz C., Noury G., Vanoudheusden E. 2015. A multicriteria approach to karst subsidence hazard mapping supported by Weights-of-Evidence analysis. Submitted to Engineering Geology.

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants.

PubMed

Dadaev, Tokhir; Saunders, Edward J; Newcombe, Paul J; Anokian, Ezequiel; Leongamornlert, Daniel A; Brook, Mark N; Cieza-Borrella, Clara; Mijuskovic, Martina; Wakerell, Sarah; Olama, Ali Amin Al; Schumacher, Fredrick R; Berndt, Sonja I; Benlloch, Sara; Ahmed, Mahbubl; Goh, Chee; Sheng, Xin; Zhang, Zhuo; Muir, Kenneth; Govindasami, Koveela; Lophatananon, Artitaya; Stevens, Victoria L; Gapstur, Susan M; Carter, Brian D; Tangen, Catherine M; Goodman, Phyllis; Thompson, Ian M; Batra, Jyotsna; Chambers, Suzanne; Moya, Leire; Clements, Judith; Horvath, Lisa; Tilley, Wayne; Risbridger, Gail; Gronberg, Henrik; Aly, Markus; Nordström, Tobias; Pharoah, Paul; Pashayan, Nora; Schleutker, Johanna; Tammela, Teuvo L J; Sipeky, Csilla; Auvinen, Anssi; Albanes, Demetrius; Weinstein, Stephanie; Wolk, Alicja; Hakansson, Niclas; West, Catharine; Dunning, Alison M; Burnet, Neil; Mucci, Lorelei; Giovannucci, Edward; Andriole, Gerald; Cussenot, Olivier; Cancel-Tassin, Géraldine; Koutros, Stella; Freeman, Laura E Beane; Sorensen, Karina Dalsgaard; Orntoft, Torben Falck; Borre, Michael; Maehle, Lovise; Grindedal, Eli Marie; Neal, David E; Donovan, Jenny L; Hamdy, Freddie C; Martin, Richard M; Travis, Ruth C; Key, Tim J; Hamilton, Robert J; Fleshner, Neil E; Finelli, Antonio; Ingles, Sue Ann; Stern, Mariana C; Rosenstein, Barry; Kerns, Sarah; Ostrer, Harry; Lu, Yong-Jie; Zhang, Hong-Wei; Feng, Ninghan; Mao, Xueying; Guo, Xin; Wang, Guomin; Sun, Zan; Giles, Graham G; Southey, Melissa C; MacInnis, Robert J; FitzGerald, Liesel M; Kibel, Adam S; Drake, Bettina F; Vega, Ana; Gómez-Caamaño, Antonio; Fachal, Laura; Szulkin, Robert; Eklund, Martin; Kogevinas, Manolis; Llorca, Javier; Castaño-Vinyals, Gemma; Penney, Kathryn L; Stampfer, Meir; Park, Jong Y; Sellers, Thomas A; Lin, Hui-Yi; Stanford, Janet L; Cybulski, Cezary; Wokolorczyk, Dominika; Lubinski, Jan; Ostrander, Elaine A; Geybels, Milan S; Nordestgaard, Børge G; Nielsen, Sune F; Weisher, Maren; Bisbjerg, Rasmus; Røder, Martin Andreas; Iversen, Peter; Brenner, Hermann; Cuk, Katarina; Holleczek, Bernd; Maier, Christiane; Luedeke, Manuel; Schnoeller, Thomas; Kim, Jeri; Logothetis, Christopher J; John, Esther M; Teixeira, Manuel R; Paulo, Paula; Cardoso, Marta; Neuhausen, Susan L; Steele, Linda; Ding, Yuan Chun; De Ruyck, Kim; De Meerleer, Gert; Ost, Piet; Razack, Azad; Lim, Jasmine; Teo, Soo-Hwang; Lin, Daniel W; Newcomb, Lisa F; Lessel, Davor; Gamulin, Marija; Kulis, Tomislav; Kaneva, Radka; Usmani, Nawaid; Slavov, Chavdar; Mitev, Vanio; Parliament, Matthew; Singhal, Sandeep; Claessens, Frank; Joniau, Steven; Van den Broeck, Thomas; Larkin, Samantha; Townsend, Paul A; Aukim-Hastie, Claire; Gago-Dominguez, Manuela; Castelao, Jose Esteban; Martinez, Maria Elena; Roobol, Monique J; Jenster, Guido; van Schaik, Ron H N; Menegaux, Florence; Truong, Thérèse; Koudou, Yves Akoli; Xu, Jianfeng; Khaw, Kay-Tee; Cannon-Albright, Lisa; Pandha, Hardev; Michael, Agnieszka; Kierzek, Andrzej; Thibodeau, Stephen N; McDonnell, Shannon K; Schaid, Daniel J; Lindstrom, Sara; Turman, Constance; Ma, Jing; Hunter, David J; Riboli, Elio; Siddiq, Afshan; Canzian, Federico; Kolonel, Laurence N; Le Marchand, Loic; Hoover, Robert N; Machiela, Mitchell J; Kraft, Peter; Freedman, Matthew; Wiklund, Fredrik; Chanock, Stephen; Henderson, Brian E; Easton, Douglas F; Haiman, Christopher A; Eeles, Rosalind A; Conti, David V; Kote-Jarai, Zsofia

2018-06-11

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.

Mapping debris flow susceptibility using analytical network process in Kodaikkanal Hills, Tamil Nadu (India)

NASA Astrophysics Data System (ADS)

Sujatha, Evangelin Ramani; Sridhar, Venkataramana

2017-12-01

Rapid debris flows, a mixture of unconsolidated sediments and water travelling at speeds > 10 m/s are the most destructive water related mass movements that affect hill and mountain regions. The predisposing factors setting the stage for the event are the availability of materials, type of materials, stream power, slope gradient, aspect and curvature, lithology, land use and land cover, lineament density, and drainage. Rainfall is the most common triggering factor that causes debris flow in the Palar subwatershed and seismicity is not considered as it is a stable continental region and moderate seismic zone. Also, there are no records of major seismic activities in the past. In this study, one of the less explored heuristic methods known as the analytical network process (ANP) is used to map the spatial propensity of debris flow. This method is based on top-down decision model and is a multi-criteria, decision-making tool that translates subjective assessment of relative importance to weights or scores and is implemented in the Palar subwatershed which is part of the Western Ghats in southern India. The results suggest that the factors influencing debris flow susceptibility in this region are the availability of material on the slope, peak flow, gradient of the slope, land use and land cover, and proximity to streams. Among all, peak discharge is identified as the chief factor causing debris flow. The use of micro-scale watersheds demonstrated in this study to develop the susceptibility map can be very effective for local level planning and land management.

Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion

NASA Astrophysics Data System (ADS)

Rahmati, Omid; Tahmasebipour, Nasser; Haghizadeh, Ali; Pourghasemi, Hamid Reza; Feizizadeh, Bakhtiar

2017-12-01

Gully erosion constitutes a serious problem for land degradation in a wide range of environments. The main objective of this research was to compare the performance of seven state-of-the-art machine learning models (SVM with four kernel types, BP-ANN, RF, and BRT) to model the occurrence of gully erosion in the Kashkan-Poldokhtar Watershed, Iran. In the first step, a gully inventory map consisting of 65 gully polygons was prepared through field surveys. Three different sample data sets (S1, S2, and S3), including both positive and negative cells (70% for training and 30% for validation), were randomly prepared to evaluate the robustness of the models. To model the gully erosion susceptibility, 12 geo-environmental factors were selected as predictors. Finally, the goodness-of-fit and prediction skill of the models were evaluated by different criteria, including efficiency percent, kappa coefficient, and the area under the ROC curves (AUC). In terms of accuracy, the RF, RBF-SVM, BRT, and P-SVM models performed excellently both in the degree of fitting and in predictive performance (AUC values well above 0.9), which resulted in accurate predictions. Therefore, these models can be used in other gully erosion studies, as they are capable of rapidly producing accurate and robust gully erosion susceptibility maps (GESMs) for decision-making and soil and water management practices. Furthermore, it was found that performance of RF and RBF-SVM for modelling gully erosion occurrence is quite stable when the learning and validation samples are changed.

Characterizing Genetic Susceptibility to Breast Cancer in Women of African Ancestry.

PubMed

Feng, Ye; Rhie, Suhn Kyong; Huo, Dezheng; Ruiz-Narvaez, Edward A; Haddad, Stephen A; Ambrosone, Christine B; John, Esther M; Bernstein, Leslie; Zheng, Wei; Hu, Jennifer J; Ziegler, Regina G; Nyante, Sarah; Bandera, Elisa V; Ingles, Sue A; Press, Michael F; Deming, Sandra L; Rodriguez-Gil, Jorge L; Zheng, Yonglan; Yao, Song; Han, Yoo-Jeong; Ogundiran, Temidayo O; Rebbeck, Timothy R; Adebamowo, Clement; Ojengbede, Oladosu; Falusi, Adeyinka G; Hennis, Anselm; Nemesure, Barbara; Ambs, Stefan; Blot, William; Cai, Qiuyin; Signorello, Lisa; Nathanson, Katherine L; Lunetta, Kathryn L; Sucheston-Campbell, Lara E; Bensen, Jeannette T; Chanock, Stephen J; Marchand, Loic Le; Olshan, Andrew F; Kolonel, Laurence N; Conti, David V; Coetzee, Gerhard A; Stram, Daniel O; Olopade, Olufunmilayo I; Palmer, Julie R; Haiman, Christopher A

2017-07-01

Background: Genome-wide association studies have identified approximately 100 common genetic variants associated with breast cancer risk, the majority of which were discovered in women of European ancestry. Because of different patterns of linkage disequilibrium, many of these genetic markers may not represent signals in populations of African ancestry. Methods: We tested 74 breast cancer risk variants and conducted fine-mapping of these susceptibility regions in 6,522 breast cancer cases and 7,643 controls of African ancestry from three genetic consortia (AABC, AMBER, and ROOT). Results: Fifty-four of the 74 variants (73%) were found to have ORs that were directionally consistent with those previously reported, of which 12 were nominally statistically significant ( P < 0.05). Through fine-mapping, in six regions ( 3p24, 12p11, 14q13, 16q12/FTO, 16q23, 19p13 ), we observed seven markers that better represent the underlying risk variant for overall breast cancer or breast cancer subtypes, whereas in another two regions ( 11q13, 16q12/TOX3 ), we identified suggestive evidence of signals that are independent of the reported index variant. Overlapping chromatin features and regulatory elements suggest that many of the risk alleles lie in regions with biological functionality. Conclusions: Through fine-mapping of known susceptibility regions, we have revealed alleles that better characterize breast cancer risk in women of African ancestry. Impact: The risk alleles identified represent genetic markers for modeling and stratifying breast cancer risk in women of African ancestry. Cancer Epidemiol Biomarkers Prev; 26(7); 1016-26. ©2017 AACR . ©2017 American Association for Cancer Research.

Geologic Map and Map Database of Eastern Sonoma and Western Napa Counties, California

USGS Publications Warehouse

Graymer, R.W.; Brabb, E.E.; Jones, D.L.; Barnes, J.; Nicholson, R.S.; Stamski, R.E.

2007-01-01

Introduction This report contains a new 1:100,000-scale geologic map, derived from a set of geologic map databases (Arc-Info coverages) containing information at 1:62,500-scale resolution, and a new description of the geologic map units and structural relations in the map area. Prepared as part of the San Francisco Bay Region Mapping Project, the study area includes the north-central part of the San Francisco Bay region, and forms the final piece of the effort to generate new, digital geologic maps and map databases for an area which includes Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Santa Cruz, Solano, and Sonoma Counties. Geologic mapping in Lake County in the north-central part of the map extent was not within the scope of the Project. The map and map database integrates both previously published reports and new geologic mapping and field checking by the authors (see Sources of Data index map on the map sheet or the Arc-Info coverage eswn-so and the textfile eswn-so.txt). This report contains new ideas about the geologic structures in the map area, including the active San Andreas Fault system, as well as the geologic units and their relations. Together, the map (or map database) and the unit descriptions in this report describe the composition, distribution, and orientation of geologic materials and structures within the study area at regional scale. Regional geologic information is important for analysis of earthquake shaking, liquifaction susceptibility, landslide susceptibility, engineering materials properties, mineral resources and hazards, as well as groundwater resources and hazards. These data also assist in answering questions about the geologic history and development of the California Coast Ranges.

Fine mapping of breast cancer genome-wide association studies loci in women of African ancestry identifies novel susceptibility markers

PubMed Central

Huo, Dezheng

2013-01-01

Numerous single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility have been identified by genome-wide association studies (GWAS). However, these SNPs were primarily discovered and validated in women of European and Asian ancestry. Because linkage disequilibrium is ancestry-dependent and heterogeneous among racial/ethnic populations, we evaluated common genetic variants at 22 GWAS-identified breast cancer susceptibility loci in a pooled sample of 1502 breast cancer cases and 1378 controls of African ancestry. None of the 22 GWAS index SNPs could be validated, challenging the direct generalizability of breast cancer risk variants identified in Caucasians or Asians to other populations. Novel breast cancer risk variants for women of African ancestry were identified in regions including 5p12 (odds ratio [OR] = 1.40, 95% confidence interval [CI] = 1.11–1.76; P = 0.004), 5q11.2 (OR = 1.22, 95% CI = 1.09–1.36; P = 0.00053) and 10p15.1 (OR = 1.22, 95% CI = 1.08–1.38; P = 0.0015). We also found positive association signals in three regions (6q25.1, 10q26.13 and 16q12.1–q12.2) previously confirmed by fine mapping in women of African ancestry. In addition, polygenic model indicated that eight best markers in this study, compared with 22 GWAS-identified SNPs, could better predict breast cancer risk in women of African ancestry (per-allele OR = 1.21, 95% CI = 1.16–1.27; P = 9.7 × 10–16). Our results demonstrate that fine mapping is a powerful approach to better characterize the breast cancer risk alleles in diverse populations. Future studies and new GWAS in women of African ancestry hold promise to discover additional variants for breast cancer susceptibility with clinical implications throughout the African diaspora. PMID:23475944

A Rb1 promoter variant with reduced activity contributes to osteosarcoma susceptibility in irradiated mice

PubMed Central

2014-01-01

Background Syndromic forms of osteosarcoma (OS) account for less than 10% of all recorded cases of this malignancy. An individual OS predisposition is also possible by the inheritance of low penetrance alleles of tumor susceptibility genes, usually without evidence of a syndromic condition. Genetic variants involved in such a non-syndromic form of tumor predisposition are difficult to identify, given the low incidence of osteosarcoma cases and the genetic heterogeneity of patients. We recently mapped a major OS susceptibility QTL to mouse chromosome 14 by comparing alpha-radiation induced osteosarcoma in mouse strains which differ in their tumor susceptibility. Methods Tumor-specific allelic losses in murine osteosacoma were mapped along chromosome 14 using microsatellite markers and SNP allelotyping. Candidate gene search in the mapped interval was refined using PosMed data mining and mRNA expression analysis in normal osteoblasts. A strain-specific promoter variant in Rb1 was tested for its influence on mRNA expression using reporter assay. Results A common Rb1 allele derived from the BALB/cHeNhg strain was identified as the major determinant of radiation-induced OS risk at this locus. Increased OS-risk is linked with a hexanucleotide deletion in the promoter region which is predicted to change WT1 and SP1 transcription factor-binding sites. Both in-vitro reporter and in-vivo expression assays confirmed an approx. 1.5 fold reduced gene expression by this promoter variant. Concordantly, the 50% reduction in Rb1 expression in mice bearing a conditional hemizygous Rb1 deletion causes a significant rise of OS incidence following alpha-irradiation. Conclusion This is the first experimental demonstration of a functional and genetic link between reduced Rb1 expression from a common promoter variant and increased tumor risk after radiation exposure. We propose that a reduced Rb1 expression by common variants in regulatory regions can modify the risk for a malignant transformation of bone cells after radiation exposure. PMID:25092376

Simultaneous quantitative susceptibility mapping and Flutemetamol-PET suggests local correlation of iron and β-amyloid as an indicator of cognitive performance at high age.

PubMed

van Bergen, J M G; Li, X; Quevenco, F C; Gietl, A F; Treyer, V; Meyer, R; Buck, A; Kaufmann, P A; Nitsch, R M; van Zijl, P C M; Hock, C; Unschuld, P G

2018-03-13

The accumulation of β-amyloid plaques is a hallmark of Alzheimer's disease (AD), and recently published data suggest that increased brain iron burden may reflect pathologies that synergistically contribute to the development of cognitive dysfunction. While preclinical disease stages are considered most promising for therapeutic intervention, the link between emerging AD-pathology and earliest clinical symptoms remains largely unclear. In the current study we therefore investigated local correlations between iron and β-amyloid plaques, and their possible association with cognitive performance in healthy older adults. 116 older adults (mean age 75 ± 7.4 years) received neuropsychological testing to calculate a composite cognitive score of performance in episodic memory, executive functioning, attention, language and communication. All participants were scanned on a combined PET-MRI instrument and were administered T1-sequences for anatomical mapping, quantitative susceptibility mapping (QSM) for assessing iron, and 18F-Flutemetamol-PET for estimating β-amyloid plaque load. Biological parametric mapping (BPM) was used to generate masks indicating voxels with significant (p < 0.05) correlation between susceptibility and 18F-Flutemetamol-SUVR. We found a bilateral pattern of clusters characterized by a statistical relationship between magnetic susceptibility and 18F-Flutemetamol-SUVR, indicating local correlations between iron and β-amyloid plaque deposition. For two bilateral clusters, located in the frontal and temporal cortex, significant relationships (p<0.05) between local β-amyloid and the composite cognitive performance score could be observed. No relationship between whole-cortex β-amyloid plaque load and cognitive performance was observable. Our data suggest that the local correlation of β-amyloid plaque load and iron deposition may provide relevant information regarding cognitive performance of healthy older adults. Further studies are needed to clarify pathological correlates of the local interaction of β-amyloid, iron and other causes of altered magnetic susceptibility. Copyright © 2018 Elsevier Inc. All rights reserved.

Pathway deregulation and expression QTLs in response to Actinobacillus pleuropneumoniae infection in swine.

PubMed

Reiner, Gerald; Dreher, Felix; Drungowski, Mario; Hoeltig, Doris; Bertsch, Natalie; Selke, Martin; Willems, Hermann; Gerlach, Gerald Friedrich; Probst, Inga; Tuemmler, Burkhardt; Waldmann, Karl-Heinz; Herwig, Ralf

2014-12-01

Actinobacillus (A.) pleuropneumoniae is among the most important pathogens in pig. The agent causes severe economic losses due to decreased performance, the occurrence of acute or chronic pleuropneumonia, and an increase in death incidence. Since therapeutics cannot be used in a sustainable manner, and vaccination is not always available, new prophylactic measures are urgently needed. Recent research has provided evidence for a genetic predisposition in susceptibility to A. pleuropneumoniae in a Hampshire × German Landrace F2 family with 170 animals. The aim of the present study is to characterize the expression response in this family in order to unravel resistance and susceptibility mechanisms and to prioritize candidate genes for future fine mapping approaches. F2 pigs differed distinctly in clinical, pathological, and microbiological parameters after challenge with A. pleuropneumoniae. We monitored genome-wide gene expression from the 50 most and 50 least susceptible F2 pigs and identified 171 genes differentially expressed between these extreme phenotypes. We combined expression QTL analyses with network analyses and functional characterization using gene set enrichment analysis and identified a functional hotspot on SSC13, including 55 eQTL. The integration of the different results provides a resource for candidate prioritization for fine mapping strategies, such as TF, TFRC, RUNX1, TCN1, HP, CD14, among others.

A new discrete dipole kernel for quantitative susceptibility mapping.

PubMed

Milovic, Carlos; Acosta-Cabronero, Julio; Pinto, José Miguel; Mattern, Hendrik; Andia, Marcelo; Uribe, Sergio; Tejos, Cristian

2018-09-01

Most approaches for quantitative susceptibility mapping (QSM) are based on a forward model approximation that employs a continuous Fourier transform operator to solve a differential equation system. Such formulation, however, is prone to high-frequency aliasing. The aim of this study was to reduce such errors using an alternative dipole kernel formulation based on the discrete Fourier transform and discrete operators. The impact of such an approach on forward model calculation and susceptibility inversion was evaluated in contrast to the continuous formulation both with synthetic phantoms and in vivo MRI data. The discrete kernel demonstrated systematically better fits to analytic field solutions, and showed less over-oscillations and aliasing artifacts while preserving low- and medium-frequency responses relative to those obtained with the continuous kernel. In the context of QSM estimation, the use of the proposed discrete kernel resulted in error reduction and increased sharpness. This proof-of-concept study demonstrated that discretizing the dipole kernel is advantageous for QSM. The impact on small or narrow structures such as the venous vasculature might by particularly relevant to high-resolution QSM applications with ultra-high field MRI - a topic for future investigations. The proposed dipole kernel has a straightforward implementation to existing QSM routines. Copyright © 2018 Elsevier Inc. All rights reserved.

Soil geohazard mapping for improved asset management of UK local roads

NASA Astrophysics Data System (ADS)

Pritchard, O. G.; Hallett, S. H.; Farewell, T. S.

2015-09-01

Unclassified roads comprise 60 % of the road network in the United Kingdom (UK). The resilience of this locally important network is declining. It is considered by the Institution of Civil Engineers to be "at risk" and is ranked 26th in the world. Many factors contribute to the degradation and ultimate failure of particular road sections. However, several UK local authorities have identified that in drought conditions, road sections founded upon shrink-swell susceptible clay soils undergo significant deterioration compared with sections on non-susceptible soils. This arises from the local road network having little, if any, structural foundations. Consequently, droughts in East Anglia have resulted in millions of pounds of damage, leading authorities to seek emergency governmental funding. This paper assesses the use of soil-related geohazard assessments in providing soil-informed maintenance strategies for the asset management of the locally important road network of the UK. A case study draws upon the UK administrative county of Lincolnshire, where road assessment data have been analysed against mapped clay-subsidence risk. This reveals a statistically significant relationship between road condition and susceptible clay soils. Furthermore, incorporation of UKCP09 future climate projections within the geohazard models has highlighted roads likely to be at future risk of clay-related subsidence.

Soil geohazard mapping for improved asset management of UK local roads

NASA Astrophysics Data System (ADS)

Pritchard, O. G.; Hallett, S. H.; Farewell, T. S.

2015-05-01

Unclassified roads comprise 60% of the road network in the United Kingdom (UK). The resilience of this locally important network is declining. It is considered by the Institution of Civil Engineers to be "at risk" and is ranked 26th in the world. Many factors contribute to the degradation and ultimate failure of particular road sections. However, several UK local authorities have identified that in drought conditions, road sections founded upon shrink/swell susceptible clay soils undergo significant deterioration compared with sections on non-susceptible soils. This arises from the local road network having little, if any structural foundations. Consequently, droughts in East Anglia have resulted in millions of pounds of damage, leading authorities to seek emergency governmental funding. This paper assesses the use of soil-related geohazard assessments in providing soil-informed maintenance strategies for the asset management of the locally important road network of the UK. A case study draws upon the UK administrative county of Lincolnshire, where road assessment data have been analysed against mapped clay-subsidence risk. This reveals a statistically significant relationship between road condition and susceptible clay soils. Furthermore, incorporation of UKCP09 future climate projections within the geohazard models has highlighted roads likely to be at future risk of clay-related subsidence.

Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility

NASA Astrophysics Data System (ADS)

Wang, H. B.; Li, J. W.; Zhou, B.; Yuan, Z. Q.; Chen, Y. P.

2013-03-01

In the last few decades, the development of Geographical Information Systems (GIS) technology has provided a method for the evaluation of landslide susceptibility and hazard. Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation. Following the DEM construction in a loess area susceptible to landslides, the direct-reverse DEM technology was employed to generate 216 slope units in the studied area. After a detailed investigation, the landslide inventory was mapped in which 39 landslides, including paleo-landslides, old landslides and recent landslides, were present. Of the 216 slope units, 123 involved landslides. To analyze the mechanism of these landslides, six environmental factors were selected to evaluate landslide occurrence: slope angle, aspect, the height and shape of the slope, distance to river and human activities. These factors were extracted in terms of the slope unit within the ArcGIS software. The spatial analysis demonstrates that most of the landslides are located on convex slopes at an elevation of 100-150 m with slope angles from 135°-225° and 40°-60°. Landslide occurrence was then checked according to these environmental factors using an artificial neural network with back propagation, optimized by genetic algorithms. A dataset of 120 slope units was chosen for training the neural network model, i.e., 80 units with landslide presence and 40 units without landslide presence. The parameters of genetic algorithms and neural networks were then set: population size of 100, crossover probability of 0.65, mutation probability of 0.01, momentum factor of 0.60, learning rate of 0.7, max learning number of 10 000, and target error of 0.000001. After training on the datasets, the susceptibility of landslides was mapped for the land-use plan and hazard mitigation. Comparing the susceptibility map with landslide inventory, it was noted that the prediction accuracy of landslide occurrence is 93.02%, whereas units without landslide occurrence are predicted with an accuracy of 81.13%. To sum up, the verification shows satisfactory agreement with an accuracy of 86.46% between the susceptibility map and the landslide locations. In the landslide susceptibility assessment, ten new slopes were predicted to show potential for failure, which can be confirmed by the engineering geological conditions of these slopes. It was also observed that some disadvantages could be overcome in the application of the neural networks with back propagation, for example, the low convergence rate and local minimum, after the network was optimized using genetic algorithms. To conclude, neural networks with back propagation that are optimized by genetic algorithms are an effective method to predict landslide susceptibility with high accuracy.

Remote sensing as tool for development of landslide databases: The case of the Messina Province (Italy) geodatabase

NASA Astrophysics Data System (ADS)

Ciampalini, Andrea; Raspini, Federico; Bianchini, Silvia; Frodella, William; Bardi, Federica; Lagomarsino, Daniela; Di Traglia, Federico; Moretti, Sandro; Proietti, Chiara; Pagliara, Paola; Onori, Roberta; Corazza, Angelo; Duro, Andrea; Basile, Giuseppe; Casagli, Nicola

2015-11-01

Landslide geodatabases, including inventories and thematic data, today are fundamental tools for national and/or local authorities in susceptibility, hazard and risk management. A well organized landslide geo-database contains different kinds of data such as past information (landslide inventory maps), ancillary data and updated remote sensing (space-borne and ground based) data, which can be integrated in order to produce landslide susceptibility maps, updated landslide inventory maps and hazard and risk assessment maps. Italy is strongly affected by landslide phenomena which cause victims and significant economic damage to buildings and infrastructure, loss of productive soils and pasture lands. In particular, the Messina Province (southern Italy) represents an area where landslides are recurrent and characterized by high magnitude, due to several predisposing factors (e.g. morphology, land use, lithologies) and different triggering mechanisms (meteorological conditions, seismicity, active tectonics and volcanic activity). For this area, a geodatabase was created by using different monitoring techniques, including remote sensing (e.g. SAR satellite ERS1/2, ENVISAT, RADARSAT-1, TerraSAR-X, COSMO-SkyMed) data, and in situ measurements (e.g. GBInSAR, damage assessment). In this paper a complete landslide geodatabase of the Messina Province, designed following the requirements of the local and national Civil Protection authorities, is presented. This geo-database was used to produce maps (e.g. susceptibility, ground deformation velocities, damage assessment, risk zonation) which today are constantly used by the Civil Protection authorities to manage the landslide hazard of the Messina Province.

Mapping the nonlinear optical susceptibility by noncollinear second-harmonic generation.

PubMed

Larciprete, M C; Bovino, F A; Giardina, M; Belardini, A; Centini, M; Sibilia, C; Bertolotti, M; Passaseo, A; Tasco, V

2009-07-15

We present a method, based on noncollinear second-harmonic generation, to evaluate the nonzero elements of the nonlinear optical susceptibility. At a fixed incidence angle, the generated signal is investigated by varying the polarization state of both fundamental beams. The resulting polarization charts allows us to verify if Kleinman's symmetry rules can be applied to a given material or to retrieve the absolute value of the nonlinear optical tensor terms, from a reference measurement. Experimental measurements obtained from gallium nitride layers are reported. The proposed method does not require an angular scan and thus is useful when the generated signal is strongly affected by sample rotation.

Directions of the US Geological Survey Landslide Hazards Reduction Program

USGS Publications Warehouse

Wieczorek, G.F.

1993-01-01

The US Geological Survey (USGS) Landslide Hazards Reduction Program includes studies of landslide process and prediction, landslide susceptibility and risk mapping, landslide recurrence and slope evolution, and research application and technology transfer. Studies of landslide processes have been recently conducted in Virginia, Utah, California, Alaska, and Hawaii, Landslide susceptibility maps provide a very important tool for landslide hazard reduction. The effects of engineering-geologic characteristics of rocks, seismic activity, short and long-term climatic change on landslide recurrence are under study. Detailed measurement of movement and deformation has begun on some active landslides. -from Author

Exploring the origins of echo-time-dependent quantitative susceptibility mapping (QSM) measurements in healthy tissue and cerebral microbleeds.

PubMed

Cronin, Matthew J; Wang, Nian; Decker, Kyle S; Wei, Hongjiang; Zhu, Wen-Zhen; Liu, Chunlei

2017-04-01

Quantitative susceptibility mapping (QSM) is increasingly used to measure variation in tissue composition both in the brain and in other areas of the body in a range of disease pathologies. Although QSM measurements were originally believed to be independent of the echo time (TE) used in the gradient-recalled echo (GRE) acquisition from which they are derived; recent literature (Sood et al., 2016) has shown that these measurements can be highly TE-dependent in a number of brain regions. In this work we systematically investigate possible causes of this effect through analysis of apparent frequency and QSM measurements derived from data acquired at multiple TEs in vivo in healthy brain regions and in cerebral microbleeds (CMBs); QSM data acquired in a gadolinium-doped phantom; and in QSM data derived from idealized simulated phase data. Apparent frequency measurements in the optic radiations (OR) and central corpus callosum (CC) were compared to those predicted by a 3-pool white matter model, however the model failed to fully explain contrasting frequency profiles measured in the OR and CC. Our results show that TE-dependent QSM measurements can be caused by a failure of phase unwrapping algorithms in and around strong susceptibility sources such as CMBs; however, in healthy brain regions this behavior appears to result from intrinsic non-linear phase evolution in the MR signal. From these results we conclude that care must be taken when deriving frequency and QSM measurements in strong susceptibility sources due to the inherent limitations in phase unwrapping; and that while signal compartmentalization due to tissue microstructure and content is a plausible cause of TE-dependent frequency and QSM measurements in healthy brain regions, better sampling of the MR signal and more complex models of tissue are needed to fully exploit this relationship. Copyright © 2017 Elsevier Inc. All rights reserved.

Modelling Soil Erosion in the Densu River Basin Using RUSLE and GIS Tools.

PubMed

Ashiagbori, G; Forkuo, E K; Laari, P; Aabeyir, R

2014-07-01

Soil erosion involves detachment and transport of soil particles from top soil layers, degrading soil quality and reducing the productivity of affected lands. Soil eroded from the upland catchment causes depletion of fertile agricultural land and the resulting sediment deposited at the river networks creates river morphological change and reservoir sedimentation problems. However, land managers and policy makers are more interested in the spatial distribution of soil erosion risk than in absolute values of soil erosion loss. The aim of this paper is to model the spatial distribution of soil erosion in Densu River Basin of Ghana using RUSLE and GIS tools and to use the model to explore the relationship between erosion susceptibility, slope and land use/land cover (LULC) in the Basin. The rainfall map, digital elevation model, soil type map, and land cover map, were input data in the soil erosion model developed. This model was then categorized into four different erosion risk classes. The developed soil erosion map was then overlaid with the slope and LULC maps of the study area to explore their effects on erosion susceptibility of the soil in the Densu River Basin. The Model, predicted 88% of the basin as low erosion risk and 6% as moderate erosion risk, 3% as high erosion risk and 3% as severe risk. The high and severe erosion areas were distributed mainly within the areas of high slope gradient and also sections of the moderate forest LULC class. Also, the areas within the moderate forest LULC class found to have high erosion risk, had an intersecting high erodibility soil group.

Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model

NASA Astrophysics Data System (ADS)

García-Rodríguez, M. J.; Malpica, J. A.

2010-06-01

This paper presents an approach for assessing earthquake-triggered landslide susceptibility using artificial neural networks (ANNs). The computational method used for the training process is a back-propagation learning algorithm. It is applied to El Salvador, one of the most seismically active regions in Central America, where the last severe destructive earthquakes occurred on 13 January 2001 (Mw 7.7) and 13 February 2001 (Mw 6.6). The first one triggered more than 600 landslides (including the most tragic, Las Colinas landslide) and killed at least 844 people. The ANN is designed and programmed to develop landslide susceptibility analysis techniques at a regional scale. This approach uses an inventory of landslides and different parameters of slope instability: slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness. The information obtained from ANN is then used by a Geographic Information System (GIS) to map the landslide susceptibility. In a previous work, a Logistic Regression (LR) was analysed with the same parameters considered in the ANN as independent variables and the occurrence or non-occurrence of landslides as dependent variables. As a result, the logistic approach determined the importance of terrain roughness and soil type as key factors within the model. The results of the landslide susceptibility analysis with ANN are checked using landslide location data. These results show a high concordance between the landslide inventory and the high susceptibility estimated zone. Finally, a comparative analysis of the ANN and LR models are made. The advantages and disadvantages of both approaches are discussed using Receiver Operating Characteristic (ROC) curves.

Susceptibility of coastal plain aquifers to contamination, Fairfax County, Virginia; a computer composite map

USGS Publications Warehouse

Johnston, Richard H.; Van Driel, J. Nicholas

1978-01-01

A map is presented that classifies the Coastal Plain of Fairfax County, Virginia according to the susceptibility of the principal sand aquifers to contamination from surface sources. The following classification is used: (1) areas where leachate can readily enter the principal sand aquifers, (2) areas offering great natural protection against migration of leachate into the aquifers, and, (3) areas where the contamination risk is uncertain and onsite investigations are needed. Approximately 20 percent of the area is in the high-risk category. The map is computer generated and was made by combining four source maps depicting those hydrogeologic factors related to movement of contaminants into the aquifers. These factors are (1) lithologic character of the upper 25 feet of sediments, (2) clay thickness above uppermost sand aquifer, (3) hydraulic gradient direction and head difference between water table and artesian head in principal aquifer, and (4) areal occurrence of moderate to high transmissiviry aquifers. The map is designed to be used by planners with little or no earth-science background, however, a technical discussion for hydrologists and geologists is also provided. (Woodard-USGS)

Regional landslide hazard assesment for Kulon Progo Area, Central Java, Indonesia

NASA Astrophysics Data System (ADS)

Karnawati, D.

2009-12-01

Karanganyar region is situated in a dynamic volcanic region in Java Island, where rain-induced landslides are frequent and widespread. Shallow-rapid earth slides triggered by heavy rainfall are the most common landslide type occurring on the steep slope and had resulted in major casualties, whilst deep soil creeping is more prominant on the gentle slope which creat a lot of damages on the houses and infrastructure. A landslide hazard assessment had been conducted to support the landslide mitigation program in this region. Such assessment was carried out by applying a semi qualitative approach (Analytical Hierarchical Process) where a weighting system was applied to assess the level of importance of each controlling parameter as suggested by Saaty (1980). Existing conditions of each controlling parameters were also assessed based on relative hierarchical system by applying scoring. Geographical Information System was used as a tool in such analysis and mapping process. The isohyet map was also prepared from statistical and spatial analyses on rain fall data. Finally, two different scenarios of landslide hazard maps were established, i.e. the scenario without any rainfall (Scenario 1) and with the reainfall (Scenario 2). It was found that the most susceptible zone of landslide was localised on the steep slope (with the inclination beyond 45o ) of jointed andesitic breccia, which was covered by thinck silty clay and situated close to the stream zone (Scenario 1). However from the hazard map and analysis on scenario 2, it can be identified that the susceptible zone expanded larger due to the rainfall, covering most region of the west-slope area of Lawu Volcano. Therefore, it can be concluded that the rainfall intensity is very crucial to induce the landslide not only in the most susceptible zone, but also in the larger area which also include the less susceptbile zone. This findings is also crucial to support the development of landslide spatial-early-warning system in the region.

Hemisphere, gender and age-related effects on iron deposition in deep gray matter revealed by quantitative susceptibility mapping.

PubMed

Gong, Nan-Jie; Wong, Chun-Sing; Hui, Edward S; Chan, Chun-Chung; Leung, Lam-Ming

2015-10-01

The purpose of this work was to investigate the effects of hemispheric location, gender and age on susceptibility value, as well as the association between susceptibility value and diffusional metrics, in deep gray matter. Iron content was estimated in vivo using quantitative susceptibility mapping. Microstructure was probed using diffusional kurtosis imaging. Regional susceptibility and diffusional metrics were measured for the putamen, caudate nucleus, globus pallidus, thalamus, substantia nigra and red nucleus in 42 healthy adults (age range 25-78 years). Susceptibility value was significantly higher in the left than the right side of the caudate nucleus (P = 0.043) and substantia nigra (P < 0.001). Women exhibited lower susceptibility values than men in the thalamus (P < 0.001) and red nucleus (P = 0.032). Significant age-related increases of susceptibility were observed in the putamen (P < 0.001), red nucleus (P < 0.001), substantia nigra (P = 0.004), caudate nucleus (P < 0.001) and globus pallidus (P = 0.017). The putamen exhibited the highest rate of iron accumulation with aging (slope of linear regression = 0.73 × 10(-3) ppm/year), which was nearly twice those in substantia nigra (slope = 0.40 × 10(-3) ppm/year) and caudate nucleus (slope = 0.39 × 10(-3) ppm/year). Significant positive correlations between the susceptibility value and diffusion measurements were observed for fractional anisotropy (P = 0.045) and mean kurtosis (P = 0.048) in the putamen without controlling for age. Neither correlation was significant after controlling for age. Hemisphere, gender and age-related differences in iron measurements were observed in deep gray matter. Notably, the putamen exhibited the highest rate of increase in susceptibility with aging. Correlations between susceptibility value and microstructural measurements were inconclusive. These findings could provide new clues for unveiling mechanisms underlying iron-related neurodegenerative diseases. Copyright © 2015 John Wiley & Sons, Ltd.

Landslides triggered by Hurricane Mitch in Guatemala -- inventory and discussion

USGS Publications Warehouse

Bucknam, Robert C.; Coe, Jeffrey A.; Chavarria, Manuel Mota; Godt, Jonathan W.; Tarr, Arthur C.; Bradley, Lee-Ann; Rafferty, Sharon A.; Hancock, Dean; Dart, Richard L.; Johnson, Margo L.

2001-01-01

The torrential rains that accompanied Hurricane Mitch in October and November of 1998 triggered thousands of landslides in the moderate to steep terrain bordering the Motagua and Polochic Rivers in eastern Guatemala. Using aerial photographs taken between January and March 2000 we mapped all visible landslides larger than about 15 m in minimum dimension in a study area of 10,000 km2 encompassing twenty 1:50,000-scale topographic map quadrangles. Rainfall from Hurricane Mitch was exceptional because it was geographically widespread, prolonged over a period of about a week, moderate to heavy in intensity, and occurred at the end of the rainy season when the ground already had a high moisture content. As documented in this report, this type of rainfall, on saturated or nearly saturated ground, has the capability to trigger both shallow and deep-seated landslides over a large area. We mapped about 11,500 landslides in the study area. The mapped landslides were of two general types: relatively small, translational and rotational landslides that commonly mobilized into debris flows and covered less than several hectares in area (not including flow paths), and large, commonly translational, landslides that sometimes generated debris flows and covered between 15 ha and 25 ha (not including flow paths). The main concentrations of landslides are on moderate-to-steep hillslopes underlain by diverse geologic units. For the purpose of describing the mapped landslides, we divided the study area into five distinct regions based on differing geologic and geomorphic characteristics. These regions include the upper Polochic valley and surrounding highlands, the central Sierra de las Minas, the hills surrounding La Union and Zacapa, the eastern Sierra de las Minas, and the border region with Honduras. All of these areas received between 200 mm and 600 mm of rain over a 13-day period between October 25 and November 6. The highest rainfall amounts (400 mm to 600 mm) occurred in the Upper Polochic valley and surrounding highlands and in the central Sierra de las Minas. The lower rainfall amounts (200 mm to 400 mm) occurred in the hills surrounding La Union, the eastern Sierra de las Minas, and in the border region with Honduras. In general, the rainfall received in these areas is roughly equivalent to the average precipitation received in a 1-year period. We used 10-m digital elevation models (DEMs) generated from contours on two quadrangles in the central Sierra de las Minas to create a map showing areas that were susceptible to landslides during Hurricane Mitch. To create the Hurricane Mitch susceptibility map, we developed a susceptibility threshold equation based on elevation and gradient. The analysis indicates that, at least on two quadrangles, gradients less than 9? were not susceptible to landslides during Hurricane Mitch. The slope of the line defined by the threshold equation indicates that less rainfall was required to initiate landslides on steep gradients than on shallow gradients. Ninety percent of the mapped landslides that were triggered by Hurricane Mitch are within the susceptible zone shown on the map. Eightysix percent of landslides that were mapped as predating Hurricane Mitch, and all landslides mapped as postdating Hurricane Mitch, are within the susceptible zone. We used LAHARZ software to model the potential downstream area affected by debris if a large landslide dam on the Rio La Lima were to fail. The model shows that the area affected would be similar to the area that was affected by a debris flow that mobilized from a large landslide along the Rio La Lima during Hurricane Mitch. The characteristics of rainfall-triggered landslides described in this report can be used as a partial guide to future landslide activity triggered by rainstorms. On the basis of existing data, hazardous areas include: moderate to steep hillslopes and

Landslide Susceptibility Mapping of Tegucigalpa, Honduras Using Artificial Neural Network, Bayesian Network and Decision Trees

NASA Astrophysics Data System (ADS)

Garcia Urquia, E. L.; Braun, A.; Yamagishi, H.

2016-12-01

Tegucigalpa, the capital city of Honduras, experiences rainfall-induced landslides on a yearly basis. The high precipitation regime and the rugged topography the city has been built in couple with the lack of a proper urban expansion plan to contribute to the occurrence of landslides during the rainy season. Thousands of inhabitants live at risk of losing their belongings due to the construction of precarious shelters in landslide-prone areas on mountainous terrains and next to the riverbanks. Therefore, the city is in the need for landslide susceptibility and hazard maps to aid in the regulation of future development. Major challenges in the context of highly dynamic urbanizing areas are the overlap of natural and anthropogenic slope destabilizing factors, as well as the availability and accuracy of data. Data-driven multivariate techniques have proven to be powerful in discovering interrelations between factors, identifying important factors in large datasets, capturing non-linear problems and coping with noisy and incomplete data. This analysis focuses on the creation of a landslide susceptibility map using different methods from the field of data mining, Artificial Neural Networks (ANN), Bayesian Networks (BN) and Decision Trees (DT). The input dataset of the study contains geomorphological and hydrological factors derived from a digital elevation model with a 10 m resolution, lithological factors derived from a geological map, and anthropogenic factors, such as information on the development stage of the neighborhoods in Tegucigalpa and road density. Moreover, a landslide inventory map that was developed in 2014 through aerial photo interpretation was used as target variable in the analysis. The analysis covers an area of roughly 100 km2, while 8.95 km2 are occupied by landslides. In a first step, the dataset was explored by assessing and improving the data quality, identifying unimportant variables and finding interrelations. Then, based on a training partition of the dataset, the ANN, BN and DT were optimized for the prediction of landslides. The predictive power and ability to generalize of the resulting models were assessed in a test partition and evaluated using success rate curves, skill scores and by ensuring the spatial plausibility of the prediction.

Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling.

PubMed

Pourghasemi, Hamid Reza; Yousefi, Saleh; Kornejady, Aiding; Cerdà, Artemi

2017-12-31

Gully erosion is identified as an important sediment source in a range of environments and plays a conclusive role in redistribution of eroded soils on a slope. Hence, addressing spatial occurrence pattern of this phenomenon is very important. Different ensemble models and their single counterparts, mostly data mining methods, have been used for gully erosion susceptibility mapping; however, their calibration and validation procedures need to be thoroughly addressed. The current study presents a series of individual and ensemble data mining methods including artificial neural network (ANN), support vector machine (SVM), maximum entropy (ME), ANN-SVM, ANN-ME, and SVM-ME to map gully erosion susceptibility in Aghemam watershed, Iran. To this aim, a gully inventory map along with sixteen gully conditioning factors was used. A 70:30% randomly partitioned sets were used to assess goodness-of-fit and prediction power of the models. The robustness, as the stability of models' performance in response to changes in the dataset, was assessed through three training/test replicates. As a result, conducted preliminary statistical tests showed that ANN has the highest concordance and spatial differentiation with a chi-square value of 36,656 at 95% confidence level, while the ME appeared to have the lowest concordance (1772). The ME model showed an impractical result where 45% of the study area was introduced as highly susceptible to gullying, in contrast, ANN-SVM indicated a practical result with focusing only on 34% of the study area. Through all three replicates, the ANN-SVM ensemble showed the highest goodness-of-fit and predictive power with a respective values of 0.897 (area under the success rate curve) and 0.879 (area under the prediction rate curve), on average, and correspondingly the highest robustness. This attests the important role of ensemble modeling in congruently building accurate and generalized models which emphasizes the necessity to examine different models integrations. The result of this study can prepare an outline for further biophysical designs on gullies scattered in the study area. Copyright © 2017 Elsevier B.V. All rights reserved.

Utilizing of magnetic parameters for evaluation of soil erosion rates on two different agricultural sites

NASA Astrophysics Data System (ADS)

Kapicka, A.; Grison, H.; Petrovsky, E.; Jaksik, O.; Kodesova, R.

2015-12-01

Field measurements of magnetic susceptibility were carried out on regular grid, resulting in 101 data points at Brumovice and 65 at Vidim locality. Mass specific magnetic susceptibility χ and its frequency dependence χFD was used to estimate the significance of SP ferrimagnetic particles of pedogenic origin in topsoil horizons. The lowest magnetic susceptibility was obtained on the steep valley sides. Here the original topsoil was eroded and mixed by tillage with the soil substrate (loess). Soil profiles unaffected by erosion were investigated in detail. The vertical distribution of magnetic susceptibility along these "virgin" profiles was measured in laboratory on samples collected with 2-cm spacing. The differences between the distribution of susceptibility in the undisturbed soil profiles and the magnetic signal after uniform mixing of the soil material as a result of erosion and tillage are fundamental for the estimation of soil loss in the studied test fields. Maximum cumulative soil erosion depth in Brumovice and Vidim is around 100 cm and 50 cm respectively. The magnetic method is suitable for mapping at the chernozem localities and measurement of soil magnetic susceptibility is in this case useful and fast technique for quantitative estimation of soil loss caused by erosion. However, it is less suitable (due to lower magnetic differentiation with depth) in areas with luvisol as dominant soil unit. Acknowledgement: This study was supported by NAZV Agency of the Ministry of Agriculture of the Czech Republic through grant No QJ1230319.

Spatially explicit shallow landslide susceptibility mapping over large areas

Treesearch

Dino Bellugi; William E. Dietrich; Jonathan Stock; Jim McKean; Brian Kazian; Paul Hargrove

2011-01-01

Recent advances in downscaling climate model precipitation predictions now yield spatially explicit patterns of rainfall that could be used to estimate shallow landslide susceptibility over large areas. In California, the United States Geological Survey is exploring community emergency response to the possible effects of a very large simulated storm event and to do so...

Integrated geophysical characterisation of Sunyani municipal solid waste disposal site using magnetic gradiometry, magnetic susceptibility survey and electrical resistivity tomography

NASA Astrophysics Data System (ADS)

Appiah, Isaac; Wemegah, David Dotse; Asare, Van-Dycke Sarpong; Danuor, Sylvester K.; Forson, Eric Dominic

2018-06-01

Non-invasive geophysical investigation using magnetic gradiometry, magnetic susceptibility survey and electrical resistivity tomography (ERT) was carried out on the Sunyani Municipal Assembly (SMA) solid waste disposal (SWD) site. The study was aimed at delineating the physical boundaries and the area extent of the waste deposit, mapping the distribution of the waste at the site, detecting and delineating zones of leachate contamination and its preferential migration pathways beneath the waste deposit and its surroundings. The results of both magnetic susceptibility and gradiometric methods displayed in anomaly maps clearly delineated the physical boundaries of the waste deposit with an approximate area extent of 82,650 m2 that are characterised by high magnetic susceptibilities between 426 × 10-5 SI and 9890 × 10-5 SI. They also revealed high magnetic anomalies erratically distributed within the waste deposit attributable to its heterogeneous and uncontrolled nature. The high magnetic anomalies outside the designated waste boundaries were also attributed to indiscriminate deposition of the waste. Similarly, the ERT sections delineated and characterised zones of leachate contamination beneath the waste body and its close surroundings as well as pathways for leachate migration with low resistivity signatures up to 43.9 Ωm. In spite of the successes reported herein using the ERT, this research also revealed that the ERT is less effective in estimating the thickness of the waste deposit in unlined SWD sites due to leachate infiltration into the ground beneath it that masks the resistivities of the top level ground and makes it indistinguishable from the waste body.

Fine Mapping for Identification of Citrus Alternaria Brown Spot Candidate Resistance Genes and Development of New SNP Markers for Marker-Assisted Selection

PubMed Central

Cuenca, Jose; Aleza, Pablo; Garcia-Lor, Andres; Ollitrault, Patrick; Navarro, Luis

2016-01-01

Alternaria brown spot (ABS) is a serious disease affecting susceptible citrus genotypes, which is a strong concern regarding citrus breeding programs. Resistance is conferred by a recessive locus (ABSr) previously located by our group within a 3.3 Mb genome region near the centromere in chromosome III. This work addresses fine-linkage mapping of this region for identifying candidate resistance genes and develops new molecular markers for ABS-resistance effective marker-assisted selection (MAS). Markers closely linked to ABSr locus were used for fine mapping using a 268-segregating diploid progeny derived from a heterozygous susceptible × resistant cross. Fine mapping limited the genomic region containing the ABSr resistance gene to 366 kb, flanked by markers at 0.4 and 0.7 cM. This region contains nine genes related to pathogen resistance. Among them, eight are resistance (R) gene homologs, with two of them harboring a serine/threonine protein kinase domain. These two genes along with a gene encoding a S-adenosyl-L-methionine-dependent-methyltransferase protein, should be considered as strong candidates for ABS-resistance. Moreover, the closest SNP was genotyped in 40 citrus varieties, revealing very high association with the resistant/susceptible phenotype. This new marker is currently used in our citrus breeding program for ABS-resistant parent and cultivar selection, at diploid, triploid and tetraploid level. PMID:28066498

A simple statistical method for analyzing flood susceptibility with incorporating rainfall and impervious surface

NASA Astrophysics Data System (ADS)

Chiang, Shou-Hao; Chen, Chi-Farn

2016-04-01

Flood, as known as the most frequent natural hazard in Taiwan, has induced severe damages of residents and properties in urban areas. The flood risk is even more severe in Tainan since 1990s, with the significant urban development over recent decades. Previous studies have indicated that the characteristics and the vulnerability of flood are affected by the increase of impervious surface area (ISA) and the changing climate condition. Tainan City, in southern Taiwan is selected as the study area. This study uses logistic regression to functionalize the relationship between rainfall variables, ISA and historical flood events. Specifically, rainfall records from 2001 to 2014 were collected and mapped, and Landsat images of year 2001, 2004, 2007, 2010 and 2014 were used to generate the ISA with SVM (support vector machine) classifier. The result shows that rainfall variables and ISA are significantly correlated to the flood occurrence in Tainan City. With applying the logistic function, the likelihood of flood occurrence can be estimated and mapped over the study area. This study suggests the method is simple and feasible for rapid flood susceptibility mapping, when real-time rainfall observations can be available, and it has potential for future flood assessment, with incorporating climate change projections and urban growth prediction.

Identification, prediction, and mitigation of sinkhole hazards in evaporite karst areas

USGS Publications Warehouse

Gutierrez, F.; Cooper, A.H.; Johnson, K.S.

2008-01-01

Sinkholes usually have a higher probability of occurrence and a greater genetic diversity in evaporite terrains than in carbonate karst areas. This is because evaporites have a higher solubility and, commonly, a lower mechanical strength. Subsidence damage resulting from evaporite dissolution generates substantial losses throughout the world, but the causes are only well understood in a few areas. To deal with these hazards, a phased approach is needed for sinkhole identification, investigation, prediction, and mitigation. Identification techniques include field surveys and geomorphological mapping combined with accounts from local people and historical sources. Detailed sinkhole maps can be constructed from sequential historical maps, recent topographical maps, and digital elevation models (DEMs) complemented with building-damage surveying, remote sensing, and high-resolution geodetic surveys. On a more detailed level, information from exposed paleosubsidence features (paleokarst), speleological explorations, geophysical investigations, trenching, dating techniques, and boreholes may help in investigating dissolution and subsidence features. Information on the hydrogeological pathways including caves, springs, and swallow holes are particularly important especially when corroborated by tracer tests. These diverse data sources make a valuable database-the karst inventory. From this dataset, sinkhole susceptibility zonations (relative probability) may be produced based on the spatial distribution of the features and good knowledge of the local geology. Sinkhole distribution can be investigated by spatial distribution analysis techniques including studies of preferential elongation, alignment, and nearest neighbor analysis. More objective susceptibility models may be obtained by analyzing the statistical relationships between the known sinkholes and the conditioning factors. Chronological information on sinkhole formation is required to estimate the probability of occurrence of sinkholes (number of sinkholes/km2 year). Such spatial and temporal predictions, frequently derived from limited records and based on the assumption that past sinkhole activity may be extrapolated to the future, are non-corroborated hypotheses. Validation methods allow us to assess the predictive capability of the susceptibility maps and to transform them into probability maps. Avoiding the most hazardous areas by preventive planning is the safest strategy for development in sinkhole-prone areas. Corrective measures could be applied to reduce the dissolution activity and subsidence processes. A more practical solution for safe development is to reduce the vulnerability of the structures by using subsidence-proof designs. ?? 2007 Springer-Verlag.

Participatory three dimensional mapping for the preparation of landslide disaster risk reduction program

NASA Astrophysics Data System (ADS)

Kusratmoko, Eko; Wibowo, Adi; Cholid, Sofyan; Pin, Tjiong Giok

2017-07-01

This paper presents the results of applications of participatory three dimensional mapping (P3DM) method for fqcilitating the people of Cibanteng' village to compile a landslide disaster risk reduction program. Physical factors, as high rainfall, topography, geology and land use, and coupled with the condition of demographic and social-economic factors, make up the Cibanteng region highly susceptible to landslides. During the years 2013-2014 has happened 2 times landslides which caused economic losses, as a result of damage to homes and farmland. Participatory mapping is one part of the activities of community-based disaster risk reduction (CBDRR)), because of the involvement of local communities is a prerequisite for sustainable disaster risk reduction. In this activity, participatory mapping method are done in two ways, namely participatory two-dimensional mapping (P2DM) with a focus on mapping of disaster areas and participatory three-dimensional mapping (P3DM) with a focus on the entire territory of the village. Based on the results P3DM, the ability of the communities in understanding the village environment spatially well-tested and honed, so as to facilitate the preparation of the CBDRR programs. Furthermore, the P3DM method can be applied to another disaster areas, due to it becomes a medium of effective dialogue between all levels of involved communities.

Quaternary Geology and Liquefaction Susceptibility, San Francisco, California 1:100,000 Quadrangle: A Digital Database

USGS Publications Warehouse

Knudsen, Keith L.; Noller, Jay S.; Sowers, Janet M.; Lettis, William R.

1997-01-01

This Open-File report is a digital geologic map database. This pamphlet serves to introduce and describe the digital data. There are no paper maps included in the Open-File report. The report does include, however, PostScript plot files containing the images of the geologic map sheets with explanations, as well as the accompanying text describing the geology of the area. For those interested in a paper plot of information contained in the database or in obtaining the PostScript plot files, please see the section entitled 'For Those Who Aren't Familiar With Digital Geologic Map Databases' below. This digital map database, compiled from previously unpublished data, and new mapping by the authors, represents the general distribution of surficial deposits in the San Francisco bay region. Together with the accompanying text file (sf_geo.txt or sf_geo.pdf), it provides current information on Quaternary geology and liquefaction susceptibility of the San Francisco, California, 1:100,000 quadrangle. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:100,000 or smaller. The content and character of the database, as well as three methods of obtaining the database, are described below.

Quantitative Susceptibility Mapping Indicates a Disturbed Brain Iron Homeostasis in Neuromyelitis Optica - A Pilot Study.

PubMed

Doring, Thomas Martin; Granado, Vanessa; Rueda, Fernanda; Deistung, Andreas; Reichenbach, Juergen R; Tukamoto, Gustavo; Gasparetto, Emerson Leandro; Schweser, Ferdinand

2016-01-01

Dysregulation of brain iron homeostasis is a hallmark of many neurodegenerative diseases and can be associated with oxidative stress. The objective of this study was to investigate brain iron in patients with Neuromyelitis Optica (NMO) using quantitative susceptibility mapping (QSM), a quantitative iron-sensitive MRI technique. 12 clinically confirmed NMO patients (6 female and 6 male; age 35.4y±14.2y) and 12 age- and sex-matched healthy controls (7 female and 5 male; age 33.9±11.3y) underwent MRI of the brain at 3 Tesla. Quantitative maps of the effective transverse relaxation rate (R2*) and magnetic susceptibility were calculated and a blinded ROI-based group comparison analysis was performed. Normality of the data and differences between patients and controls were tested by Kolmogorov-Smirnov and t-test, respectively. Correlation with age was studied using Spearman's rank correlation and an ANCOVA-like analysis. Magnetic susceptibility values were decreased in the red nucleus (p<0.01; d>0.95; between -15 and -22 ppb depending on reference region) with a trend toward increasing differences with age. R2* revealed significantly decreased relaxation in the optic radiations of five of the 12 patients (p<0.0001; -3.136±0.567 s-1). Decreased relaxation in the optic radiation is indicative for demyelination, which is in line with previous findings. Decreased magnetic susceptibility in the red nucleus is indicative for a lower brain iron concentration, a chemical redistribution of iron into less magnetic forms, or both. Further investigations are necessary to elucidate the pathological cause or consequence of this finding.

A direct approach to estimating the number of potential fatalities from an eruption: Application to the Central Volcanic Complex of Tenerife Island

NASA Astrophysics Data System (ADS)

Marrero, J. M.; García, A.; Llinares, A.; Rodriguez-Losada, J. A.; Ortiz, R.

2012-03-01

One of the critical issues in managing volcanic crises is making the decision to evacuate a densely-populated region. In order to take a decision of such importance it is essential to estimate the cost in lives for each of the expected eruptive scenarios. One of the tools that assist in estimating the number of potential fatalities for such decision-making is the calculation of the FN-curves. In this case the FN-curve is a graphical representation that relates the frequency of the different hazards to be expected for a particular volcano or volcanic area, and the number of potential fatalities expected for each event if the zone of impact is not evacuated. In this study we propose a method for assessing the impact that a possible eruption from the Tenerife Central Volcanic Complex (CVC) would have on the population at risk. Factors taken into account include the spatial probability of the eruptive scenarios (susceptibility) and the temporal probability of the magnitudes of the eruptive scenarios. For each point or cell of the susceptibility map with greater probability, a series of probability-scaled hazard maps is constructed for the whole range of magnitudes expected. The number of potential fatalities is obtained from the intersection of the hazard maps with the spatial map of population distribution. The results show that the Emergency Plan for Tenerife must provide for the evacuation of more than 100,000 persons.

Variations in the susceptibility to landslides, as a consequence of land cover changes: A look to the past, and another towards the future.

PubMed

Pisano, L; Zumpano, V; Malek, Ž; Rosskopf, C M; Parise, M

2017-12-01

Land cover is one of the most important conditioning factors in landslide susceptibility analysis. Usually it is considered as a static factor, but it has proven to be dynamic, with changes occurring even in few decades. In this work the influence of land cover changes on landslide susceptibility are analyzed for the past and for future scenarios. For the application, an area representative of the hilly-low mountain sectors of the Italian Southern Apennines was chosen (Rivo basin, in Molise Region). With this purpose landslide inventories and land cover maps were produced for the years 1954, 1981 and 2007. Two alternative future scenarios were created for 2050, one which follows the past trend (2050-trend), and another one more extreme, foreseeing a decrease of forested and cultivated areas (2050-alternative). The landslide susceptibility analysis was performed using the Spatial Multi-Criteria Evaluation method for different time steps, investigating changes to susceptibility over time. The results show that environmental dynamics, such as land cover change, affect slope stability in time. In fact there is a decrease of susceptibility in the past and in the future 2050-trend scenario. This is due to the increase of forest or cultivated areas, that is probably determined by a better land management, water and soil control respect to other land cover types such as shrubland, pasture or bareland. Conversely the results revealed by the alternative scenario (2050-alternative), show how the decrease in forest and cultivated areas leads to an increase in landslide susceptibility. This can be related to the assumed worst climatic condition leading to a minor agricultural activity and lower extension of forested areas, possibly associated also to the effects of forest fires. The results suggest that conscious landscape management might contribute to determine a significant reduction in landslide susceptibility. Copyright © 2017 Elsevier B.V. All rights reserved.

Harmonisation of geological data to support geohazard mapping: the case of eENVplus project

NASA Astrophysics Data System (ADS)

Cipolloni, Carlo; Krivic, Matija; Novak, Matevž; Pantaloni, Marco; Šinigoj, Jasna

2014-05-01

In the eENVplus project, which aims is to unlock huge amounts of environmental datamanaged by the national and regional environmental agencies and other public and private organisations, we have developed a cross-border pilot on the geological data harmonisation through the integration and harmonisation of existing services. The pilot analyses the methodology and results of the OneGeology-Europe project, elaborated at the scale of 1:1M, to point out difficulties and unsolved problems highlighted during the project. This preliminary analysis is followed by a comparison of two geological maps provided by the neighbouring countries with the objective to compare and define the geometric and semantic anomalous contacts between geological polygons and lines in the maps. This phase will be followed by a detailed scale geological map analysis aimed to solve the anomalies identified in the previous phase. The two Geological Surveys involved into the pilot will discuss the problems highlighted during this phase. Subsequently the semantic description will be redefined and the geometry of the polygons in geological maps will be redrawn or adjusted according to a lithostratigraphic approach that takes in account the homogeneity of age, lithology, depositional environment and consolidation degree of geological units. The two Geological Surveys have decided to apply the harmonisation process on two different dataset: the first is represented by the Geological Map at the scale of 1:1,000,000, partially harmonised within the OneGeology-Europe project that will be re-aligned with GE INSPIRE data model to produce data and services compliant with INSPIRE target schema. The main target of Geological Surveys is to produce data and web services compliant with the wider international schema, where there are more options to provide data, with specific attributes that are important to obtain the geohazard map as in the case of this pilot project; therefore we have decided to apply GeoSciML 3.2 schema to the dataset that represents Geological Map at the scale of 1:100,000. Within the pilot will be realised two main geohazard examples with a semi-automatized procedure based on a specific tool component integrated in the client: a landslide susceptibility map and a potential flooding map. In this work we want to present the first results obtained with use case geo-processing procedure in the first test phase, where we have developed a dataset compliant with GE INSPIRE to perform the landslide and flooding susceptibility maps.

Magnetic Susceptibility Changes in the Basal Ganglia and Brain Stem of Patients with Wilson's Disease: Evaluation with Quantitative Susceptibility Mapping.

PubMed

Doganay, Selim; Gumus, Kazim; Koc, Gonca; Bayram, Ayse Kacar; Dogan, Mehmet Sait; Arslan, Duran; Gumus, Hakan; Gorkem, Sureyya Burcu; Ciraci, Saliha; Serin, Halil Ibrahim; Coskun, Abdulhakim

2018-01-10

Wilson's disease (WD) is characterized with the accumulation of copper in the liver and brain. The objective of this study is to quantitatively measure the susceptibility changes of basal ganglia and brain stem of pediatric patients with neurological WD using quantitative susceptibility mapping (QSM) in comparison to healthy controls. Eleven patients with neurological WD (mean age 15 ± 3.3 years, range 10-22 years) and 14 agematched controls were prospectively recruited. Both groups were scanned on a 1.5 Tesla clinical scanner. In addition to T 1 - and T 2 -weighted MR images, a 3D multi-echo spoiled gradient echo (GRE) sequence was acquired and QSM images were derived offline. The quantitative measurement of susceptibility of corpus striatum, thalamus of each hemisphere, midbrain, and pons were assessed with the region of interest analysis on the QSM images. The susceptibility values for the patient and control groups were compared using twosample t-test. One patient with WD had T 1 shortening in the bilateral globus pallidus. Another one had hyperintensity in the bilateral putamen, caudate nuclei, and substantia nigra on T 2 -weighted images. The rest of the patients with WD and all subjects of the control group had no signal abnormalities on conventional MR images. The susceptibility measures of right side of globus pallidus, putamen, thalamus, midbrain, and entire pons were significantly different in patients compared to controls (P < 0.05). QSM method exhibits increased susceptibility differences of basal ganglia and brain stem in patients with WD that have neurologic impairment even if no signal alteration is detected on T 1 - and T 2 -weighted MR images.

Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study

PubMed Central

Langkammer, Christian; Schweser, Ferdinand; Krebs, Nikolaus; Deistung, Andreas; Goessler, Walter; Scheurer, Eva; Sommer, Karsten; Reishofer, Gernot; Yen, Kathrin; Fazekas, Franz; Ropele, Stefan; Reichenbach, Jürgen R.

2012-01-01

Quantitative susceptibility mapping (QSM) is a novel technique which allows determining the bulk magnetic susceptibility distribution of tissue in vivo from gradient echo magnetic resonance phase images. It is commonly assumed that paramagnetic iron is the predominant source of susceptibility variations in gray matter as many studies have reported a reasonable correlation of magnetic susceptibility with brain iron concentrations in vivo. Instead of performing direct comparisons, however, all these studies used the putative iron concentrations reported in the hallmark study by Hallgren and Sourander (1958) for their analysis. Consequently, the extent to which QSM can serve to reliably assess brain iron levels is not yet fully clear. To provide such information we investigated the relation between bulk tissue magnetic susceptibility and brain iron concentration in unfixed (in situ) post mortem brains of 13 subjects using MRI and inductively coupled plasma mass spectrometry. A strong linear correlation between chemically determined iron concentration and bulk magnetic susceptibility was found in gray matter structures (r = 0.84, p < 0.001), whereas the correlation coefficient was much lower in white matter (r = 0.27, p < 0.001). The slope of the overall linear correlation was consistent with theoretical considerations of the magnetism of ferritin supporting that most of the iron in the brain is bound to ferritin proteins. In conclusion, iron is the dominant source of magnetic susceptibility in deep gray matter and can be assessed with QSM. In white matter regions the estimation of iron concentrations by QSM is less accurate and more complex because the counteracting contribution from diamagnetic myelinated neuronal fibers confounds the interpretation. PMID:22634862

Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker

PubMed Central

Wang, Yi; Liu, Tian

2015-01-01

In MRI, the main magnetic field polarizes the electron cloud of a molecule, generating a chemical shift for observer protons within the molecule and a magnetic susceptibility inhomogeneity field for observer protons outside the molecule. The number of water protons surrounding a molecule for detecting its magnetic susceptibility is vastly greater than the number of protons within the molecule for detecting its chemical shift. However, the study of tissue magnetic susceptibility has been hindered by poor molecular specificities of hitherto used methods based on MRI signal phase and T2* contrast, which depend convolutedly on surrounding susceptibility sources. Deconvolution of the MRI signal phase can determine tissue susceptibility but is challenged by the lack of MRI signal in the background and by the zeroes in the dipole kernel. Recently, physically meaningful regularizations, including the Bayesian approach, have been developed to enable accurate quantitative susceptibility mapping (QSM) for studying iron distribution, metabolic oxygen consumption, blood degradation, calcification, demyelination, and other pathophysiological susceptibility changes, as well as contrast agent biodistribution in MRI. This paper attempts to summarize the basic physical concepts and essential algorithmic steps in QSM, to describe clinical and technical issues under active development, and to provide references, codes, and testing data for readers interested in QSM. Magn Reson Med 73:82–101, 2015. © 2014 The Authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance. This is an open access article under the terms of the Creative commons Attribution License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited. PMID:25044035

Evaluation of Allele-Specific Somatic Changes of Genome-Wide Association Study Susceptibility Alleles in Human Colorectal Cancers

PubMed Central

Gerber, Madelyn M.; Hampel, Heather; Schulz, Nathan P.; Fernandez, Soledad; Wei, Lai; Zhou, Xiao-Ping; de la Chapelle, Albert; Toland, Amanda Ewart

2012-01-01

Background Tumors frequently exhibit loss of tumor suppressor genes or allelic gains of activated oncogenes. A significant proportion of cancer susceptibility loci in the mouse show somatic losses or gains consistent with the presence of a tumor susceptibility or resistance allele. Thus, allele-specific somatic gains or losses at loci may demarcate the presence of resistance or susceptibility alleles. The goal of this study was to determine if previously mapped susceptibility loci for colorectal cancer show evidence of allele-specific somatic events in colon tumors. Methods We performed quantitative genotyping of 16 single nucleotide polymorphisms (SNPs) showing statistically significant association with colorectal cancer in published genome-wide association studies (GWAS). We genotyped 194 paired normal and colorectal tumor DNA samples and 296 paired validation samples to investigate these SNPs for allele-specific somatic gains and losses. We combined analysis of our data with published data for seven of these SNPs. Results No statistically significant evidence for allele-specific somatic selection was observed for the tested polymorphisms in the discovery set. The rs6983267 variant, which has shown preferential loss of the non-risk T allele and relative gain of the risk G allele in previous studies, favored relative gain of the G allele in the combined discovery and validation samples (corrected p-value = 0.03). When we combined our data with published allele-specific imbalance data for this SNP, the G allele of rs6983267 showed statistically significant evidence of relative retention (p-value = 2.06×10−4). Conclusions Our results suggest that the majority of variants identified as colon cancer susceptibility alleles through GWAS do not exhibit somatic allele-specific imbalance in colon tumors. Our data confirm previously published results showing allele-specific imbalance for rs6983267. These results indicate that allele-specific imbalance of cancer susceptibility alleles may not be a common phenomenon in colon cancer. PMID:22629442

QVAST: a new Quantum GIS plugin for estimating volcanic susceptibility

NASA Astrophysics Data System (ADS)

Bartolini, S.; Cappello, A.; Martí, J.; Del Negro, C.

2013-08-01

One of the most important tasks of modern volcanology is the construction of hazard maps simulating different eruptive scenarios that can be used in risk-based decision-making in land-use planning and emergency management. The first step in the quantitative assessment of volcanic hazards is the development of susceptibility maps, i.e. the spatial probability of a future vent opening given the past eruptive activity of a volcano. This challenging issue is generally tackled using probabilistic methods that use the calculation of a kernel function at each data location to estimate probability density functions (PDFs). The smoothness and the modeling ability of the kernel function are controlled by the smoothing parameter, also known as the bandwidth. Here we present a new tool, QVAST, part of the open-source Geographic Information System Quantum GIS, that is designed to create user-friendly quantitative assessments of volcanic susceptibility. QVAST allows to select an appropriate method for evaluating the bandwidth for the kernel function on the basis of the input parameters and the shapefile geometry, and can also evaluate the PDF with the Gaussian kernel. When different input datasets are available for the area, the total susceptibility map is obtained by assigning different weights to each of the PDFs, which are then combined via a weighted summation and modeled in a non-homogeneous Poisson process. The potential of QVAST, developed in a free and user-friendly environment, is here shown through its application in the volcanic fields of Lanzarote (Canary Islands) and La Garrotxa (NE Spain).

Task-evoked brain functional magnetic susceptibility mapping by independent component analysis (χICA).

PubMed

Chen, Zikuan; Calhoun, Vince D

2016-03-01

Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (χ) functional states. Upon the reconstructed χ dataspace, we propose an ICA-based brain functional χ mapping method (χICA) to extract task-evoked brain functional map. A complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain χ response dataset. χICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D χ dataspace. With finger-tapping experiments on a 7T system, the χICA-extracted χ-depicted functional map is similar to the SPM-inferred functional χ map by a spatial correlation of 0.67 ± 0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81 ± 0.05. The understanding of the inferiority of χICA to AICA for task-evoked functional map is an ongoing research topic. For task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare χICA with AICA for extracting task-correlated timecourses and functional maps. χICA can extract a χ-depicted task-evoked brain functional map from a reconstructed χ dataspace without the knowledge about brain hemodynamic responses. The χICA-extracted brain functional χ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA. Copyright © 2016 Elsevier B.V. All rights reserved.

Modeling regional initiation of rainfall-induced shallow landslides in the eastern Umbria Region of central Italy

USGS Publications Warehouse

Salciarini, D.; Godt, J.W.; Savage, W.Z.; Conversini, P.; Baum, R.L.; Michael, J.A.

2006-01-01

We model the rainfall-induced initiation of shallow landslides over a broad region using a deterministic approach, the Transient Rainfall Infiltration and Grid-based Slope-stability (TRIGRS) model that couples an infinite-slope stability analysis with a one-dimensional analytical solution for transient pore pressure response to rainfall infiltration. This model permits the evaluation of regional shallow landslide susceptibility in a Geographic Information System framework, and we use it to analyze susceptibility to shallow landslides in an area in the eastern Umbria Region of central Italy. As shown on a landslide inventory map produced by the Italian National Research Council, the area has been affected in the past by shallow landslides, many of which have transformed into debris flows. Input data for the TRIGRS model include time-varying rainfall, topographic slope, colluvial thickness, initial water table depth, and material strength and hydraulic properties. Because of a paucity of input data, we focus on parametric analyses to calibrate and test the model and show the effect of variation in material properties and initial water table conditions on the distribution of simulated instability in the study area in response to realistic rainfall. Comparing the results with the shallow landslide inventory map, we find more than 80% agreement between predicted shallow landslide susceptibility and the inventory, despite the paucity of input data.

Natural variation in maize aphid resistance is associated with 2,4-Dihydroxy-7-Methoxy-1,4-Benzoxazin-3-One Glucoside Methyltransferase activity

USDA-ARS?s Scientific Manuscript database

Plants differ greatly in their susceptibility to insect herbivory, suggesting both local adaptation and resistance tradeoffs. We used maize (Zea mays) recombinant inbred lines to map a quantitative trait locus (QTL) for the maize leaf aphid (Rhopalosiphum maidis) susceptibility to maize Chromosome 1...

Rainfall-Triggered Landslides Bury Sri Lankan Villages

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Stanley, Thomas

2016-01-01

On the afternoon of May 17th, 2016, a major landslide event caused at least 92 deaths, with 109 still missing*. The site was rated highly susceptible to landslides in a new global landslide susceptibility map. GPM precipitation data suggest that both antecedent and current rainfall as well as complex topography played a role in the slope failures.

Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China.

PubMed

Hong, Haoyuan; Tsangaratos, Paraskevas; Ilia, Ioanna; Liu, Junzhi; Zhu, A-Xing; Xu, Chong

2018-07-15

The main objective of the present study was to utilize Genetic Algorithms (GA) in order to obtain the optimal combination of forest fire related variables and apply data mining methods for constructing a forest fire susceptibility map. In the proposed approach, a Random Forest (RF) and a Support Vector Machine (SVM) was used to produce a forest fire susceptibility map for the Dayu County which is located in southwest of Jiangxi Province, China. For this purpose, historic forest fires and thirteen forest fire related variables were analyzed, namely: elevation, slope angle, aspect, curvature, land use, soil cover, heat load index, normalized difference vegetation index, mean annual temperature, mean annual wind speed, mean annual rainfall, distance to river network and distance to road network. The Natural Break and the Certainty Factor method were used to classify and weight the thirteen variables, while a multicollinearity analysis was performed to determine the correlation among the variables and decide about their usability. The optimal set of variables, determined by the GA limited the number of variables into eight excluding from the analysis, aspect, land use, heat load index, distance to river network and mean annual rainfall. The performance of the forest fire models was evaluated by using the area under the Receiver Operating Characteristic curve (ROC-AUC) based on the validation dataset. Overall, the RF models gave higher AUC values. Also the results showed that the proposed optimized models outperform the original models. Specifically, the optimized RF model gave the best results (0.8495), followed by the original RF (0.8169), while the optimized SVM gave lower values (0.7456) than the RF, however higher than the original SVM (0.7148) model. The study highlights the significance of feature selection techniques in forest fire susceptibility, whereas data mining methods could be considered as a valid approach for forest fire susceptibility modeling. Copyright © 2018 Elsevier B.V. All rights reserved.

Magnetic soil mapping and modelling for sustainable land use management in Ukraine

NASA Astrophysics Data System (ADS)

Menshov, Oleksandr; Kruglov, Oleksandr; Pereira, Paulo; Sukhorada, Anatoliy

2015-04-01

The agricultural activities need to be monitored in order to observe if they respect the sustainability principles. During the last 15 years we have been using the magnetic susceptibility measurements for the identification of soil properties and degradation risks. This method can be used to measure soil fertility. We observed a decrease of soil magnetic susceptibility values in the areas with high erosion risk. Magnetic susceptibility can be used as an indicator in identifying rates and depths of soil erosion. Compared to other conventional methods, this one, have a low cost and is time saving. This opens new possibilities to have a better cover of the studied area, collect more samples, hence, a better spatial and temporal resolution. Another field of the soil magnetic properties study is the land use change a result of the urban sprawl and technogenic pollution. The increased risk of the soil degradation is connected to soil pollution and the high concentrations of heavy metals and other dangerous chemical elements and compounds to the environment. The main sources of the anthropogenic pollution are the vehicle circulation, power plants, cement and chemical industry. The components released by these sources contain magnetic properties, which can be identified in soils. In this way we can identify the negative impacts of these activities on the ecosystems sustainability and services and promote measures to recover it. We obtained new results on an example of the urban and industry developed sites of Ukraine. The interpretation of soil magnetic parameter measurements depends on knowledge of a reference value. It is influenced by the type of soils and landscape topography. Magnetic methods are an effective method for temporal and spatial soil mapping and modeling. The results of the soils magnetic studies are valuable to sustainable land use management.

Magnetic susceptibility induced echo time shifts: Is there a bias in age-related fMRI studies?

PubMed Central

Ngo, Giang-Chau; Wong, Chelsea N.; Guo, Steve; Paine, Thomas; Kramer, Arthur F.; Sutton, Bradley P.

2016-01-01

Purpose To evaluate the potential for bias in functional MRI (fMRI) aging studies resulting from age-related differences in magnetic field distributions which can impact echo time and functional contrast. Materials and Methods Magnetic field maps were taken on 31 younger adults (age: 22 ± 2.9 years) and 46 older adults (age: 66 ± 4.5 years) on a 3 T scanner. Using the spatial gradients of the magnetic field map for each participant, an echo planar imaging (EPI) trajectory was simulated. The effective echo time, time at which the k-space trajectory is the closest to the center of k-space, was calculated. This was used to examine both within-subject and across-age-group differences in the effective echo time maps. The Blood Oxygenation Level Dependent (BOLD) percent signal change resulting from those echo time shifts was also calculated to determine their impact on fMRI aging studies. Result For a single subject, the effective echo time varied as much as ± 5 ms across the brain. An unpaired t-test between the effective echo time across age group resulted in significant differences in several regions of the brain (p<0.01). The difference in echo time was only approximately 1 ms, however which is not expected to have an important impact on BOLD fMRI percent signal change (< 4%). Conclusion Susceptibility-induced magnetic field gradients induce local echo time shifts in gradient echo fMRI images, which can cause variable BOLD sensitivity across the brain. However, the age-related differences in BOLD signal are expected to be small for an fMRI study at 3 T. PMID:27299727

Evaluation of Sentinel-2A satellite imagery for mapping cotton root rot

USDA-ARS?s Scientific Manuscript database

Cotton (Gossypium hirsutum L.) is an economically important crop that is highly susceptible to cotton root rot. Remote sensing technology provides a useful and effective means for detecting and mapping cotton root rot infestations in cotton fields. This research assessed the potential of 10-m Sentin...

A Genetic Linkage Map of Mycosphaerella Fijiensis, using SSR and DArT Markers

USDA-ARS?s Scientific Manuscript database

Mycosphaerella fijiensis is the causal agent of black leaf streak or Black Sigatoka disease in bananas. This pathogen threatens global banana production as the main export Cavendish cultivars are highly susceptible. Previously a genetic linkage map was generated predominantly using anonymous AFLP ma...

Mapping the defoliation potential of gypsy moth

Treesearch

David A. Gansner; Stanford L. Arner; Rachel Riemann Hershey; Susan L. King

1993-01-01

A model that uses forest stand characteristics to estimate the likelihood of gypsy moth (Lymantria dispar) defoliation has been developed. It was applied to recent forest inventory plot data to produce susceptibility ratings and a map showing defoliation potential for counties in Pennsylvania and six adjacent states on new frontiers of infestation.

Evaluation and Quantitative trait loci mapping of resistance to powdery mildew in lettuce

USDA-ARS?s Scientific Manuscript database

Lettuce (Lactuca sativa L.) is the major leafy vegetable that is susceptible to powdery mildew disease under greenhouse and field conditions. We mapped quantitative trait loci (QTLs) for resistance to powdery mildew under greenhouse conditions in an interspecific population derived from a cross betw...

Dental artifacts in the head and neck region: implications for Dixon-based attenuation correction in PET/MR.

PubMed

Ladefoged, Claes N; Hansen, Adam E; Keller, Sune H; Fischer, Barbara M; Rasmussen, Jacob H; Law, Ian; Kjær, Andreas; Højgaard, Liselotte; Lauze, Francois; Beyer, Thomas; Andersen, Flemming L

2015-12-01

In the absence of CT or traditional transmission sources in combined clinical positron emission tomography/magnetic resonance (PET/MR) systems, MR images are used for MR-based attenuation correction (MR-AC). The susceptibility effects due to metal implants challenge MR-AC in the neck region of patients with dental implants. The purpose of this study was to assess the frequency and magnitude of subsequent PET image distortions following MR-AC. A total of 148 PET/MR patients with clear visual signal voids on the attenuation map in the dental region were included in this study. Patients were injected with [(18)F]-FDG, [(11)C]-PiB, [(18)F]-FET, or [(64)Cu]-DOTATATE. The PET/MR data were acquired over a single-bed position of 25.8 cm covering the head and neck. MR-AC was based on either standard MR-ACDIXON or MR-ACINPAINTED where the susceptibility-induced signal voids were substituted with soft tissue information. Our inpainting algorithm delineates the outer contour of signal voids breaching the anatomical volume using the non-attenuation-corrected PET image and classifies the inner air regions based on an aligned template of likely dental artifact areas. The reconstructed PET images were evaluated visually and quantitatively using regions of interests in reference regions. The volume of the artifacts and the computed relative differences in mean and max standardized uptake value (SUV) between the two PET images are reported. The MR-based volume of the susceptibility-induced signal voids on the MR-AC attenuation maps was between 1.6 and 520.8 mL. The corresponding/resulting bias of the reconstructed tracer distribution was localized mainly in the area of the signal void. The mean and maximum SUVs averaged across all patients increased after inpainting by 52% (± 11%) and 28% (± 11%), respectively, in the corrected region. SUV underestimation decreased with the distance to the signal void and correlated with the volume of the susceptibility artifact on the MR-AC attenuation map. Metallic dental work may cause severe MR signal voids. The resulting PET/MR artifacts may exceed the actual volume of the dental fillings. The subsequent bias in PET is severe in regions in and near the signal voids and may affect the conspicuity of lesions in the mandibular region.

The OncoArray Consortium: a Network for Understanding the Genetic Architecture of Common Cancers

PubMed Central

Amos, Christopher I.; Dennis, Joe; Wang, Zhaoming; Byun, Jinyoung; Schumacher, Fredrick R.; Gayther, Simon A.; Casey, Graham; Hunter, David J.; Sellers, Thomas A.; Gruber, Stephen B.; Dunning, Alison M.; Michailidou, Kyriaki; Fachal, Laura; Doheny, Kimberly; Spurdle, Amanda B.; Li, Yafang; Xiao, Xiangjun; Romm, Jane; Pugh, Elizabeth; Coetzee, Gerhard A.; Hazelett, Dennis J.; Bojesen, Stig E.; Caga-Anan, Charlisse; Haiman, Christopher A.; Kamal, Ahsan; Luccarini, Craig; Tessier, Daniel; Vincent, Daniel; Bacot, François; Van Den Berg, David J.; Nelson, Stefanie; Demetriades, Stephen; Goldgar, David E.; Couch, Fergus J.; Forman, Judith L.; Giles, Graham G.; Conti, David V.; Bickeböller, Heike; Risch, Angela; Waldenberger, Melanie; Brüske, Irene; Hicks, Belynda D.; Ling, Hua; McGuffog, Lesley; Lee, Andrew; Kuchenbaecker, Karoline B.; Soucy, Penny; Manz, Judith; Cunningham, Julie M.; Butterbach, Katja; Kote-Jarai, Zsofia; Kraft, Peter; FitzGerald, Liesel M.; Lindström, Sara; Adams, Marcia; McKay, James D.; Phelan, Catherine M.; Benlloch, Sara; Kelemen, Linda E.; Brennan, Paul; Riggan, Marjorie; O’Mara, Tracy A.; Shen, Hongbin; Shi, Yongyong; Thompson, Deborah J.; Goodman, Marc T.; Nielsen, Sune F.; Berchuck, Andrew; Laboissiere, Sylvie; Schmit, Stephanie L.; Shelford, Tameka; Edlund, Christopher K.; Taylor, Jack A.; Field, John K.; Park, Sue K.; Offit, Kenneth; Thomassen, Mads; Schmutzler, Rita; Ottini, Laura; Hung, Rayjean J.; Marchini, Jonathan; Al Olama, Ali Amin; Peters, Ulrike; Eeles, Rosalind A.; Seldin, Michael F.; Gillanders, Elizabeth; Seminara, Daniela; Antoniou, Antonis C.; Pharoah, Paul D.; Chenevix-Trench, Georgia; Chanock, Stephen J.; Simard, Jacques; Easton, Douglas F.

2016-01-01

Background Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers and cancer related traits. Methods The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. Results The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. Conclusions Results from these analyses will enable researchers to identify new susceptibility loci, perform fine mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental and lifestyle related exposures. Impact Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. PMID:27697780

Spatial prediction of ground subsidence susceptibility using an artificial neural network.

PubMed

Lee, Saro; Park, Inhye; Choi, Jong-Kuk

2012-02-01

Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor's relative importance were determined by the back-propagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, "distance from fault" had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.

Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information

NASA Astrophysics Data System (ADS)

Günther, Andreas; Van Den Eeckhaut, Miet; Malet, Jean-Philippe; Reichenbach, Paola; Hervás, Javier

2014-11-01

With the adoption of the EU Thematic Strategy for Soil Protection in 2006, small-scale (1:1 M) assessments of threats affecting soils over Europe received increasing attention. As landslides have been recognized as one of eight threats requiring a Pan-European evaluation, we present an approach for landslide susceptibility evaluation at the continental scale over Europe. Unlike previous continental and global scale landslide susceptibility studies not utilizing spatial information on the events, we collected more than 102,000 landslide locations in 22 European countries. These landslides are heterogeneously distributed over Europe, but are indispensable for the evaluation and classification of Pan-European datasets used as spatial predictors, and the validation of the resulting assessments. For the analysis we subdivided the European territory into seven different climate-physiographical zones by combining morphometric and climatic data for terrain differentiation, and adding a coastal zone defined as a 1 km strip inland from the coastline. Landslide susceptibility modeling was performed for each zone using heuristic spatial multicriteria evaluations supported by analytical hierarchy processes, and validated with the inventory data using the receiver operating characteristics. In contrast to purely data-driven statistical modeling techniques, our semi-quantitative approach is capable to introduce expert knowledge into the analysis, which is indispensable considering quality and resolution of the input data, and incompleteness and bias in the inventory information. The reliability of the resulting susceptibility map ELSUS 1000 Version 1 (1 km resolution) was examined on an administrative terrain unit level in areas with landslide information and through the comparison with available national susceptibility zonations. These evaluations suggest that although the ELSUS 1000 is capable for a correct synoptic prediction of landslide susceptibility in the majority of the area, it needs further improvement in terms of data used.

LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants.

PubMed

Machiela, Mitchell J; Chanock, Stephen J

2015-11-01

Assessing linkage disequilibrium (LD) across ancestral populations is a powerful approach for investigating population-specific genetic structure as well as functionally mapping regions of disease susceptibility. Here, we present LDlink, a web-based collection of bioinformatic modules that query single nucleotide polymorphisms (SNPs) in population groups of interest to generate haplotype tables and interactive plots. Modules are designed with an emphasis on ease of use, query flexibility, and interactive visualization of results. Phase 3 haplotype data from the 1000 Genomes Project are referenced for calculating pairwise metrics of LD, searching for proxies in high LD, and enumerating all observed haplotypes. LDlink is tailored for investigators interested in mapping common and uncommon disease susceptibility loci by focusing on output linking correlated alleles and highlighting putative functional variants. LDlink is a free and publically available web tool which can be accessed at http://analysistools.nci.nih.gov/LDlink/. mitchell.machiela@nih.gov. Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US.

An erythroid-specific ATP2B4 enhancer mediates red blood cell hydration and malaria susceptibility

PubMed Central

Lessard, Samuel; Gatof, Emily Stern; Schupp, Patrick G.; Sher, Falak; Ali, Adnan; Prehar, Sukhpal; Kurita, Ryo; Nakamura, Yukio; Baena, Esther; Oceandy, Delvac; Bauer, Daniel E.

2017-01-01

The lack of mechanistic explanations for many genotype-phenotype associations identified by GWAS precludes thorough assessment of their impact on human health. Here, we conducted an expression quantitative trait locus (eQTL) mapping analysis in erythroblasts and found erythroid-specific eQTLs for ATP2B4, the main calcium ATPase of red blood cells (rbc). The same SNPs were previously associated with mean corpuscular hemoglobin concentration (MCHC) and susceptibility to severe malaria infection. We showed that Atp2b4–/– mice demonstrate increased MCHC, confirming ATP2B4 as the causal gene at this GWAS locus. Using CRISPR-Cas9, we fine mapped the genetic signal to an erythroid-specific enhancer of ATP2B4. Erythroid cells with a deletion of the ATP2B4 enhancer had abnormally high intracellular calcium levels. These results illustrate the power of combined transcriptomic, epigenomic, and genome-editing approaches in characterizing noncoding regulatory elements in phenotype-relevant cells. Our study supports ATP2B4 as a potential target for modulating rbc hydration in erythroid disorders and malaria infection. PMID:28714864

An erythroid-specific ATP2B4 enhancer mediates red blood cell hydration and malaria susceptibility.

PubMed

Lessard, Samuel; Gatof, Emily Stern; Beaudoin, Mélissa; Schupp, Patrick G; Sher, Falak; Ali, Adnan; Prehar, Sukhpal; Kurita, Ryo; Nakamura, Yukio; Baena, Esther; Ledoux, Jonathan; Oceandy, Delvac; Bauer, Daniel E; Lettre, Guillaume

2017-08-01

The lack of mechanistic explanations for many genotype-phenotype associations identified by GWAS precludes thorough assessment of their impact on human health. Here, we conducted an expression quantitative trait locus (eQTL) mapping analysis in erythroblasts and found erythroid-specific eQTLs for ATP2B4, the main calcium ATPase of red blood cells (rbc). The same SNPs were previously associated with mean corpuscular hemoglobin concentration (MCHC) and susceptibility to severe malaria infection. We showed that Atp2b4-/- mice demonstrate increased MCHC, confirming ATP2B4 as the causal gene at this GWAS locus. Using CRISPR-Cas9, we fine mapped the genetic signal to an erythroid-specific enhancer of ATP2B4. Erythroid cells with a deletion of the ATP2B4 enhancer had abnormally high intracellular calcium levels. These results illustrate the power of combined transcriptomic, epigenomic, and genome-editing approaches in characterizing noncoding regulatory elements in phenotype-relevant cells. Our study supports ATP2B4 as a potential target for modulating rbc hydration in erythroid disorders and malaria infection.

Influence of tectonic folding on rockfall susceptibility, American Fork Canyon, Utah, USA

USGS Publications Warehouse

Coe, J.A.; Harp, E.L.

2007-01-01

We examine rockfall susceptibility of folded strata in the Sevier fold-thrust belt exposed in American Fork Canyon in north-central Utah. Large-scale geologic mapping, talus production data, rock-mass-quality measurements, and historical rockfall data indicate that rockfall susceptibility is correlated with limb dip and curvature of the folded, cliff-forming Mississippian limestones. On fold limbs, rockfall susceptibility increases as dip increases. This relation is controlled by several factors, including an increase in adverse dip conditions and apertures of discontinuities, and shearing by flexural slip during folding that has reduced the friction angles of discontinuities by smoothing surface asperities. Susceptibility is greater in fold hinge zones than on adjacent limbs primarily because there are greater numbers of discontinuities in hinge zones. We speculate that susceptibility increases in hinge zones as fold curvature becomes tighter.

Hydrogeologic Characteristics of the St. Croix River Basin, Minnesota and Wisconsin: Implications for the Susceptibility of Ground Water to Potential Contamination

USGS Publications Warehouse

Juckem, Paul F.

2007-01-01

Population growth in the St. Croix River Basin in Minnesota and Wisconsin has intensified concerns of county resource managers and the National Park Service, which is charged with protecting the St. Croix National Scenic Riverway, about the potential for ground-water contamination in the basin. This report describes a previously developed method that was adapted to illustrate potential ground-water-contamination susceptibility in the St. Croix River Basin. The report also gives an estimate of ground-water-residence time and surface-water/ground-water interaction as related to natural attenuation and movement of contaminants in five tributary basins. A ground-water-contamination-susceptibility map was adapted from a state-wide map of Wisconsin to the St. Croix River Basin by use of well-driller construction records and regional maps of aquifer properties in Minnesota and Wisconsin. Measures of various subsurface properties were combined to generate a spatial index of susceptibility. The subjective index method developed for the State of Wisconsin by Schmidt (1987) was not derived from analyses of water-quality data or physical processes. Nonetheless, it was adapted for this report to furnish a seamless map across state boundaries that would be familiar to many resource managers. Following this method, areas most susceptible to contamination appear to have coarse-grained sediments (sands or gravels) and shallow water tables or are underlain by carbonate-bedrock aquifers. The least susceptible areas appear to have fine-grained sediments and deep water tables. If an aquifer becomes contaminated, the ground-water-residence time can affect potential natural attenuation along the ground-water-flow path. Mean basin ground-water-residence times were computed for the Apple, Kettle, Kinnickinnic, Snake and Sunrise River Basins, which are tributary basins to the St. Croix Basin, by use of average aquifer properties of saturated thickness, porosity, and recharge rates. The Apple River Basin had the shortest mean ground-water-residence times (20-120 years), owing largely to the moderate saturated thickness and high recharge rate in the basin. The Kinnickinnic and Sunrise River Basins had the longest mean residence times (60-350 and 70-390 years, respectively) chiefly because of the relatively large saturated thickness of the basins. Owing to limitations of the residence-time calculations, actual ground-water-residence times will vary around the mean values within each basin and may range from days or weeks in karst carbonate aquifers to millennia in deep confined sandstone aquifers. Areas of relatively short residence time (less than the median residence time in each basin) were identified by use of ground-water-flow models for each of the five tributary basins. Results of simulations show that these areas, in which contaminants may have relatively less time for natural attenuation along the short flow paths, generally occur near streams and rivers where ground water discharges to the surface. Finally, the ground-water-flow models were used to simulate ground-water/surface-water interaction in the five tributary basins. Results of simulations show that some lakes and reservoirs leak surface water into the ground-water-flow system on their downgradient side, where the surface-water outflow has been restricted by a dam or a naturally constricted outlet. These locations are noteworthy because contaminated surface waters could potentially enter the ground-water-flow system at these locations.

Is it beneficial to approximate pre-failure topography to predict landslide susceptibility with empirical models?

NASA Astrophysics Data System (ADS)

Steger, Stefan; Schmaltz, Elmar; Glade, Thomas

2017-04-01

Empirical landslide susceptibility maps spatially depict the areas where future slope failures are likely due to specific environmental conditions. The underlying statistical models are based on the assumption that future landsliding is likely to occur under similar circumstances (e.g. topographic conditions, lithology, land cover) as past slope failures. This principle is operationalized by applying a supervised classification approach (e.g. a regression model with a binary response: landslide presence/absence) that enables discrimination between conditions that favored past landslide occurrences and the circumstances typical for landslide absences. The derived empirical relation is then transferred to each spatial unit of an area. Literature reveals that the specific topographic conditions representative for landslide presences are frequently extracted from derivatives of digital terrain models at locations were past landslides were mapped. The underlying morphology-based landslide identification becomes possible due to the fact that the topography at a specific locality usually changes after landslide occurrence (e.g. hummocky surface, concave and steep scarp). In a strict sense, this implies that topographic predictors used within conventional statistical landslide susceptibility models relate to post-failure topographic conditions - and not to the required pre-failure situation. This study examines the assumption that models calibrated on the basis of post-failure topographies may not be appropriate to predict future landslide locations, because (i) post-failure and pre-failure topographic conditions may differ and (ii) areas were future landslides will occur do not yet exhibit such a distinct post-failure morphology. The study was conducted for an area located in the Walgau region (Vorarlberg, western Austria), where a detailed inventory consisting of shallow landslides was available. The methodology comprised multiple systematic comparisons of models generated on the basis of post-failure conditions (i.e. the standard approach) with models based on an approximated pre-failure topography. Pre-failure topography was approximated by (i) erasing the area of mapped landslide polygons within a digital terrain model and (ii) filling these "empty" areas by interpolating elevation points located outside the mapped landslides. Landslide presence information was extracted from the respective landslide scarp locations while an equal number of randomly sampled points represented landslide absences. After an initial exploratory data analysis, mixed-effects logistic regression was applied to model landslide susceptibility on the basis of two predictor sets (post-failure versus pre-failure predictors). Furthermore, all analyses were separately conducted for five different modelling resolutions to elaborate the suspicion that the degree of generalization of topographic parameters may as well play a role on how the respective models may differ. Model evaluation was conducted by means of multiple procedures (i.e. odds ratios, k-fold cross validation, permutation-based variable importance, difference maps of predictions). The results revealed that models based on highest resolutions (e.g. 1 m, 2.5 m) and post-failure topography performed best from a purely quantitative perspective. A confrontation of models (post-failure versus pre-failure based models) based on an identical modelling resolution exposed that validation results, modelled relationships as well as the prediction pattern tended to converge with a decreasing raster resolution. Based on the results, we concluded that an approximation of pre-failure topography does not significantly contribute to improved landslide susceptibility models in the case (i) the underlying inventory consists of small landslide features and (ii) the models are based on coarse raster resolutions (e.g. 25 m). However, in the case modelling with high raster resolutions is envisaged (e.g. 1 m, 2.5 m) or the inventory mainly consists of larger events, a reconstruction of pre-failure conditions might be highly expedient, even though conventional validation results might indicate an opposite tendency. Finally, we recommend to consider that topographic predictors highly useful to detect past slope movements (e.g. roughness) are not necessarily valuable to predict future slope instabilities.

Comparative Transcriptome Analysis of Resistant and Susceptible Common Bean Genotypes in Response to Soybean Cyst Nematode Infection.

PubMed

Jain, Shalu; Chittem, Kishore; Brueggeman, Robert; Osorno, Juan M; Richards, Jonathan; Nelson, Berlin D

2016-01-01

Soybean cyst nematode (SCN; Heterodera glycines Ichinohe) reproduces on the roots of common bean (Phaseolus vulgaris L.) and can cause reductions in plant growth and seed yield. The molecular changes in common bean roots caused by SCN infection are unknown. Identification of genetic factors associated with SCN resistance could help in development of improved bean varieties with high SCN resistance. Gene expression profiling was conducted on common bean roots infected by SCN HG type 0 using next generation RNA sequencing technology. Two pinto bean genotypes, PI533561 and GTS-900, resistant and susceptible to SCN infection, respectively, were used as RNA sources eight days post inoculation. Total reads generated ranged between ~ 3.2 and 5.7 million per library and were mapped to the common bean reference genome. Approximately 70-90% of filtered RNA-seq reads uniquely mapped to the reference genome. In the inoculated roots of resistant genotype PI533561, a total of 353 genes were differentially expressed with 154 up-regulated genes and 199 down-regulated genes when compared to the transcriptome of non- inoculated roots. On the other hand, 990 genes were differentially expressed in SCN-inoculated roots of susceptible genotype GTS-900 with 406 up-regulated and 584 down-regulated genes when compared to non-inoculated roots. Genes encoding nucleotide-binding site leucine-rich repeat resistance (NLR) proteins, WRKY transcription factors, pathogenesis-related (PR) proteins and heat shock proteins involved in diverse biological processes were differentially expressed in both resistant and susceptible genotypes. Overall, suppression of the photosystem was observed in both the responses. Furthermore, RNA-seq results were validated through quantitative real time PCR. This is the first report describing genes/transcripts involved in SCN-common bean interaction and the results will have important implications for further characterization of SCN resistance genes in common bean.

Comparative Transcriptome Analysis of Resistant and Susceptible Common Bean Genotypes in Response to Soybean Cyst Nematode Infection

PubMed Central

Jain, Shalu; Chittem, Kishore; Brueggeman, Robert; Osorno, Juan M.; Richards, Jonathan; Nelson, Berlin D.

2016-01-01

Soybean cyst nematode (SCN; Heterodera glycines Ichinohe) reproduces on the roots of common bean (Phaseolus vulgaris L.) and can cause reductions in plant growth and seed yield. The molecular changes in common bean roots caused by SCN infection are unknown. Identification of genetic factors associated with SCN resistance could help in development of improved bean varieties with high SCN resistance. Gene expression profiling was conducted on common bean roots infected by SCN HG type 0 using next generation RNA sequencing technology. Two pinto bean genotypes, PI533561 and GTS-900, resistant and susceptible to SCN infection, respectively, were used as RNA sources eight days post inoculation. Total reads generated ranged between ~ 3.2 and 5.7 million per library and were mapped to the common bean reference genome. Approximately 70–90% of filtered RNA-seq reads uniquely mapped to the reference genome. In the inoculated roots of resistant genotype PI533561, a total of 353 genes were differentially expressed with 154 up-regulated genes and 199 down-regulated genes when compared to the transcriptome of non- inoculated roots. On the other hand, 990 genes were differentially expressed in SCN-inoculated roots of susceptible genotype GTS-900 with 406 up-regulated and 584 down-regulated genes when compared to non-inoculated roots. Genes encoding nucleotide-binding site leucine-rich repeat resistance (NLR) proteins, WRKY transcription factors, pathogenesis-related (PR) proteins and heat shock proteins involved in diverse biological processes were differentially expressed in both resistant and susceptible genotypes. Overall, suppression of the photosystem was observed in both the responses. Furthermore, RNA-seq results were validated through quantitative real time PCR. This is the first report describing genes/transcripts involved in SCN-common bean interaction and the results will have important implications for further characterization of SCN resistance genes in common bean. PMID:27441552

Soil magnetic susceptibility mapping as a pollution and provenance tool: an example from southern New Zealand

NASA Astrophysics Data System (ADS)

Martin, A. P.; Ohneiser, C.; Turnbull, R. E.; Strong, D. T.; Demler, S.

2018-02-01

The presence or absence, degree and variation of heavy metal contamination in New Zealand soils is a matter of ongoing debate as it affects soil quality, agriculture and human health. In many instances, however, the soil heavy metal concentration data do not exist to answer these questions and the debate is ongoing. To address this, magnetic susceptibility (a common proxy for heavy metal contamination) values were measured in topsoil (0-30 cm) and subsoil (50-70 cm) at grid sites spaced at 8 km intervals across ca. 20 000 km2 of southern New Zealand. Samples were measured for both mass- and volume-specific magnetic susceptibility, with results being strongly, positively correlated. Three different methods of determining anomalies were applied to the data including the topsoil-subsoil difference method, Tukey boxplot method and geoaccumulation index method, with each method filtering out progressively more anomalies. Additional soil magnetic (hysteresis, isothermal remanence and thermomagnetic) measurements were made on a select subset of samples from anomalous sites. Magnetite is the dominant remanence carrying mineral, and magnetic susceptibility is governed by that minerals concentration in soils, rather than mineral type. All except two anomalous sites have a dominant geogenic source (cf. anthropogenic). By proxy, heavy metal contamination in southern New Zealand soils is minimal, making them relatively pristine. The provenance of the magnetic minerals in the anomalous sites can be traced back to likely sources in outcrops of igneous rocks within the same catchment, terrane or rock type: a distance of <100 km but frequently <1 km. Soil provenance is a key step when mapping element or isotopic distribution, vectoring to mineralization or studying soil for agricultural suitability, water quality or environmental regulation. Measuring soil magnetic susceptibility is a useful, quick and inexpensive tool that usefully supplements soil geochemical data.

Changes and future trends in landslide risk mapping for mountain communities: application to the Vars catchment and Barcelonnette basin (French Alps)

NASA Astrophysics Data System (ADS)

Puissant, Anne; Wernert, Pauline; Débonnaire, Nicolas; Malet, Jean-Philippe; Bernardie, Séverine; Thomas, Loic

2017-04-01

Landslide risk assessment has become a major research subject within the last decades. In the context of the French-funded ANR Project SAMCO which aims at enhancing the overall resilience of societies on the impacts of mountain risks, we developed a procedure to quantify changes in landslide risk at catchment scales. First, we investigate landslide susceptibility, the spatial component of the hazard, through a weight of evidence probabilistic model. This latter is based on the knowledge of past and current landslides in order to simulate their spatial locations in relation to environmental controlling factors. Second, we studied potential consequences using a semi-quantitative region-scale indicator-based method, called method of the Potential Damage Index (PDI). It allows estimating the possible damages related to landslides by combining weighted indicators reflecting the exposure of the element at risk for structural, functional and socio-economic stakes. Finally, we provide landslide risk maps by combining both susceptibility and potential consequence maps resulting from the two previous steps. The risk maps are produced for the present time and for the future (e.g. period 2050 and 2100) taking into account four scenarios of future landcover and landuse development (based on the Prelude European Project) that are consistent with the likely evolution of mountain communities. Results allow identifying the geographical areas that are likely to be exposed to landslide risk in the future. The results are integrated on a web-based demonstrator, enabling the comparison between various scenarios, and could thus be used as decision-support tools for local stakeholders. The method and the demonstrator will be presented through the analysis of landslide risk in two catchments of the French Alps: the Vars catchment and the Barcelonnette basin, both characterized by a different exposure to landslide hazards.

Lithospheric magnetic field modelling of the African continent

NASA Astrophysics Data System (ADS)

Hemant, K.; Maus, S.

2003-04-01

New magnetic satellite missions in low-earth orbit are providing increasingly accurate maps of the lithospheric magnetic field. These maps can be used to infer the geological structure of regions hidden by Phanerozoic cover, taking into account our knowledge of crustal structure from surface geology and seismic methods. A GIS based modelling technique has been developed to model the various geological units of the continents using the UNESCO geological map of the world, supported by background geological information from various sources. Geological units of each region are assigned a susceptibility value based on laboratory values of the constituent rock types. Then, using the 3SMAC seismic crustal structure, a vertically integrated susceptibility (VIS) model is computed at each point of the region. Starting with this VIS model, the total field anomaly is computed at an altitude of 400 km and compared with the MF2 lithospheric magnetic field model derived from CHAMP data. The modelling results of the Precambrian units of the West African cratons agree well with MF2. The anomaly in the Central African cratonic region also correlates well, although part of it is unaccounted for as yet. Furthermore, the anomalies over the Tanzanian craton and surrounding region agree very well. Most of the regions around the South African cratons are hidden by Phanerozoic cover, yet the results above the Kaapvaal craton and the southern Zimbabwe craton around the Limpopo belt show good correspondence with the observed anomaly map. The results also suggest a probable extension of the Precambrian units below the sediments of younger age. In general, the lower crust is likely to be more mafic than presumed in our current understanding of Central Africa. Deviations in the magnitude of the anomalies in some regions are likely to be due to incomplete seismic information in those regions. Thus, the thickness of crustal layers derived from magnetic anomalies for these locations may help to constrain future geophysical models in the less explored regions of Africa.

3D linear inversion of magnetic susceptibility data acquired by frequency domain EMI

NASA Astrophysics Data System (ADS)

Thiesson, J.; Tabbagh, A.; Simon, F.-X.; Dabas, M.

2017-01-01

Low induction number EMI instruments are able to simultaneously measure a soil's apparent magnetic susceptibility and electrical conductivity. This family of dual measurement instruments is highly useful for the analysis of soils and archeological sites. However, the electromagnetic properties of soils are found to vary over considerably different ranges: whereas their electrical conductivity varies from ≤ 0.1 to ≥ 100 mS/m, their relative magnetic permeability remains within a very small range, between 1.0001 and 1.01 SI. Consequently, although apparent conductivity measurements need to be inverted using non-linear processes, the variations of the apparent magnetic susceptibility can be approximated through the use of linear processes, as in the case of the magnetic prospection technique. Our proposed 3D inversion algorithm starts from apparent susceptibility data sets, acquired using different instruments over a given area. A reference vertical profile is defined by considering the mode of the vertical distributions of both the electrical resistivity and of the magnetic susceptibility. At each point of the mapped area, the reference vertical profile response is subtracted to obtain the apparent susceptibility variation dataset. A 2D horizontal Fourier transform is applied to these variation datasets and to the dipole (impulse) response of each instrument, a (vertical) 1D inversion is performed at each point in the spectral domain, and finally the resulting dataset is inverse transformed to restore the apparent 3D susceptibility variations. It has been shown that when applied to synthetic results, this method is able to correct the apparent deformations of a buried object resulting from the geometry of the instrument, and to restore reliable quantitative susceptibility contrasts. It also allows the thin layer solution, similar to that used in magnetic prospection, to be implemented. When applied to field data it initially delivers a level of contrast comparable to that obtained with a non-linear 3D inversion. Over four different sites, this method is able to produce, following an acceptably short computation time, realistic values for the lateral and vertical variations in susceptibility, which are significantly different to those given by a point-by-point 1D inversion.

Quantitative susceptibility mapping of multiple sclerosis lesions at various ages.

PubMed

Chen, Weiwei; Gauthier, Susan A; Gupta, Ajay; Comunale, Joseph; Liu, Tian; Wang, Shuai; Pei, Mengchao; Pitt, David; Wang, Yi

2014-04-01

To assess multiple sclerosis (MS) lesions at various ages by using quantitative susceptibility mapping (QSM) and conventional magnetic resonance (MR) imaging. Retrospectively selected were 32 clinically confirmed MS patients (nine men and 23 women; 39.3 years ± 10.9) who underwent two MR examinations (interval, 0.43 years ± 0.16) with three-dimensional gradient-echo sequence from August 2011 to August 2012. To estimate the ages of MS lesions, MR examinations performed 0.3-10.6 years before study examinations were studied. Hyperintensity on T2-weighted images was used to define MS lesions. QSM images were reconstructed from gradient-echo data. Susceptibility of MS lesions and temporal rates of change were obtained from QSM images. Lesion susceptibilities were analyzed by t test with intracluster correlation adjustment and Bonferroni correction in multiple comparisons. MR imaging of 32 patients depicted 598 MS lesions, of which 162 lesions (27.1%) in 23 patients were age measurable and six (1.0%) were only visible at QSM. The susceptibilities relative to normal-appearing white matter (NAWM) were 0.53 ppb ± 3.34 for acute enhanced lesions, 38.43 ppb ± 13.0 (positive; P < .01) for early to intermediately aged nonenhanced lesions, and 4.67 ppb ± 3.18 for chronic nonenhanced lesions. Temporal rates of susceptibility changes relative to cerebrospinal fluid were 12.49 ppb/month ± 3.15 for acute enhanced lesions, 1.27 ppb/month ± 2.31 for early to intermediately aged nonenhanced lesions, and -0.004 ppb/month ± 0 for chronic nonenhanced lesions. Magnetic susceptibility of MS lesions increased rapidly as it changed from enhanced to nonenhanced, it attained a high susceptibility value relative to NAWM during its initial few years (approximately 4 years), and it gradually dissipated back to susceptibility similar to that of NAWM as it aged, which may provide new insight into pathophysiologic features of MS lesions. Online supplemental material is available for this article. RSNA, 2013

Susceptibility-based functional brain mapping by 3D deconvolution of an MR-phase activation map.

PubMed

Chen, Zikuan; Liu, Jingyu; Calhoun, Vince D

2013-05-30

The underlying source of T2*-weighted magnetic resonance imaging (T2*MRI) for brain imaging is magnetic susceptibility (denoted by χ). T2*MRI outputs a complex-valued MR image consisting of magnitude and phase information. Recent research has shown that both the magnitude and the phase images are morphologically different from the source χ, primarily due to 3D convolution, and that the source χ can be reconstructed from complex MR images by computed inverse MRI (CIMRI). Thus, we can obtain a 4D χ dataset from a complex 4D MR dataset acquired from a brain functional MRI study by repeating CIMRI to reconstruct 3D χ volumes at each timepoint. Because the reconstructed χ is a more direct representation of neuronal activity than the MR image, we propose a method for χ-based functional brain mapping, which is numerically characterised by a temporal correlation map of χ responses to a stimulant task. Under the linear imaging conditions used for T2*MRI, we show that the χ activation map can be calculated from the MR phase map by CIMRI. We validate our approach using numerical simulations and Gd-phantom experiments. We also analyse real data from a finger-tapping visuomotor experiment and show that the χ-based functional mapping provides additional activation details (in the form of positive and negative correlation patterns) beyond those generated by conventional MR-magnitude-based mapping. Copyright © 2013 Elsevier B.V. All rights reserved.

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

NASA Astrophysics Data System (ADS)

Duman, T. Y.; Can, T.; Gokceoglu, C.; Nefeslioglu, H. A.; Sonmez, H.

2006-11-01

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.

An integrated approach for the assessment of the Aedes aegypti and Aedes albopictus global spatial distribution, and determination of the zones susceptible to the development of Zika virus.

PubMed

Santos, José; Meneses, Bruno M

2017-04-01

The Zika virus, one of the new epidemic diseases, is reported to have affected millions of people in the past year. The suitable climate conditions of the areas where Zika virus has been reported, especially in areas with a high population density, are the main cause of the current outbreak and spread of the disease. Indeed, the suitable climatic conditions of certain territories constitute perfect breading nest for the propagation and outbreak of worldwide diseases. The main objective of this research is to analyze the global distribution and predicted areas of both mosquitoes Ae. aegypti and Ae. albopictus which are the main vectors of Zika virus. Physical (SRTM) and climatic variables (WorldClim) were used to obtain the susceptibility maps based on the optimum conditions for the development of these mosquitoes. The susceptibility model was developed using a Species Distribution Model - correlative model, namely the Maximum Entropy, that used as input the spatial references of both vectors (Dryad Digital Repository). The results show the most important classes of each independent variable used in assessing the presence of each species of mosquitoes and the areas susceptible to the presence of these vector species. It turns out that Ae. aegypti has greater global dispersion than the Ae. albopictus specie, although two common regions stand out as the most prone to the presence of both mosquito species (tropical and subtropical zones). The crossing of these areas of greater susceptibility with areas of greater population density (e.g. India, China, Se of USA and Brazil) shows some agreement, and these areas stand out due to the presence of several records of Zika virus (HealthMap Project). In this sense, through the intersection of susceptibility and human exposure the areas with increased risk of development and spread of Zika virus are pinpointed, suggesting that there may be a new outbreak of this virus in these places, if preventive measures are not adopted. Copyright © 2017 Elsevier B.V. All rights reserved.

Microarray-Based Mapping for the Detection of Molecular Markers in Response to Aspergillus flavus Infection in Susceptible and Resistant Maize Lines

USDA-ARS?s Scientific Manuscript database

The objectives of this study were (1) to evaluate differential gene expression levels for resistance to A. flavus kernel infection in susceptible (Va35) and resistant (Mp313E) maize lines using Oligonucleotide and cDNA microarray analysis, (2) to evaluate differences in A. flavus accumulation betwee...

Dentate nucleus iron deposition is a potential biomarker for tremor-dominant Parkinson’s disease

PubMed Central

He, Naying; Huang, Pei; Ling, Huawei; Langley, Jason; Liu, Chunlei; Ding, Bei; Huang, Juan; Xu, Hongmin; Zhang, Yong; Zhang, Zhongping; Hu, Xiaoping; Chen, Shengdi; Yan, Fuhua

2016-01-01

Parkinson disease (PD) is a heterogeneous neurodegenerative disorder with variable clinicopathologic phenotypes and underlying neuropathologic mechanisms. Each clinical phenotype has a unique set of motor symptoms. Tremor is the most frequent initial motor symptom of PD and is the most difficult symptom to treat. The dentate nucleus (DN) is a deep iron rich nucleus in the cerebellum and may be involved in PD tremor. In this study, we test the hypothesis that DN iron may be elevated in tremor dominant PD patients using quantitative susceptibility mapping. Forty-three patients with PD [19 tremor dominant (TD)/24 akinetic-rigid dominant (AR)] and 48 healthy gender- and age-matched controls were recruited. Multi-echo gradient echo data were collected for each subject on a 3.0 T MR system. Inter-group susceptibility differences in bilateral DN were investigated and correlations of clinical features with susceptibility were also examined. In contrast to the AR group, the TD group was found to have increased susceptibility in the bilateral DN, when compared to healthy controls. In addition, susceptibility was positively correlated with tremor score in drug naive PD patients. These findings indicate that iron load within DN may make an important contribution to motor phenotypes in PD. Moreover, our results suggest that TD and AR phenotypes of PD can be differentiated on the basis of the susceptibility of the DN at least on the group level. PMID:27192177

Mapping Forest Fire Susceptibility in Temperate Mountain Areas with Expert Knowledge. A Case Study from Iezer Mountains, Romanian Carpathians

NASA Astrophysics Data System (ADS)

Mihai, Bogdan; Savulescu, Ionut

2014-05-01

Forest fires in Romanian Carpathians became a frequent phenomenon during the last decade, although local climate and other environmental features did not create typical conditions. From 2004, forest fires affect in Romania more than 100 hectares/year of different forest types (deciduous and coniferous). Their magnitude and frequency are not known, since a historical forest fire inventory does not exist (only press papers and local witness for some selected events). Forest fires features the summer dry periods but there are dry autumns and early winter periods with events of different magnitudes. The application we propose is based on an empirical modeling of forest fire susceptibility in a typical mountain area from the Southern Carpathians, the Iezer Mountains (2462 m). The study area features almost all the altitudinal vegetation zones of the European temperate mountains, from the beech zone, to the coniferous zone, the subalpine and the alpine zones (Mihai et al., 2007). The analysis combines GIS and remote sensing models (Chuvieco et al., 2012), starting from the ideas that forest fires are featured by the ignition zones and then by the fire propagation zones. The first data layer (ignition zones) is the result of the crossing between the ignition factors: lightning - points of multitemporal occurence and anthropogenic activities (grazing, tourism and traffic) and the ignition zones (forest fuel zonation - forest stands, soil cover and topoclimatic factor zonation). This data is modelled from different sources: the MODIS imagery fire product (Hantson et al., 2012), detailed topographic maps, multitemporal orthophotos at 0.5 m resolution, Landsat multispectral imagery, forestry cadastre maps, detailed soil maps, meteorological data (the WorldClim digital database) as well as the field survey (mapping using GPS and local observation). The second data layer (fire propagation zones) is the result of the crossing between the forest fuel zonation, obtained with the help of forestry data, the wind regime data and the topographic features of the mountain area (elevation, slope declivity, slope aspect). The analysis also consider the insolation degree of mountain slopes, that creates favourable conditions for fire propagation between different canopies. These data layers are integrated within a simple GIS analysis in order to intersect the ignition zones with the fire propagation zones in order to obtain the potential areas to be affected by fire. The digital map show three levels of forest fire susceptibility, differenced on the basis of expert knowledge. The map can be validated from the statistical point of view with the polygons of the forest fire affected areas mapped from Landsat TM, ETM+ and OLI satellite imagery. The mapping results could be integrated within the forest management strategies and especially within the forest cadastre and development maps (updated every ten years). The result can confirm that the data gap in terms of forest fire events can be filled with expert knowledge. References Chuvieco, E, Aguado, I., Jurdao, S., Pettinari, M., Yebra, M., Salas, J., Hantson, S., de la Riva, J., Ibarra, P., Rodrigues, M., Echeverria, M., Azqueta, D., Roman, M., Bastarrika, A., Martinez, S., Recondo, C., Zapico, E., Martinez-Vega F.J. (2012) Integrating geospatial information into fire risk assessment, International Journal of Wildland Fire, 2,2, 69-86. Hantson, S., Padilla, M., Corti., D, Chuvieco, E. (2013) Strenghts and weaknesses of MODIS hotspots to characterize Global fire occurence, Remote Sensing of Environment, 131, 1, 152-159. Mihai, B., Savulescu, I.,Sandric, I. (2007) Change detection analysis (1986/2002) for the alpine, subalpine and forest landscape in Iezer Mountains (Southern Carpathians, Romania), Mountain Research and Development, 27, 250-258.

Hydrogeomorphic Classification of Wetlands on Mt. Desert Island, Maine, Including Hydrologic Susceptibility Factors for Wetlands in Acadia National Park

USGS Publications Warehouse

Nielsen, Martha G.

2006-01-01

The U.S. Geological Survey, in cooperation with the National Park Service, developed a hydrogeomorphic (HGM) classification system for wetlands greater than 0.4 hectares (ha) on Mt. Desert Island, Maine, and applied this classification using map-scale data to more than 1,200 mapped wetland units on the island. In addition, two hydrologic susceptibility factors were defined for a subset of these wetlands, using 11 variables derived from landscape-scale characteristics of the catchment areas of these wetlands. The hydrologic susceptibility factors, one related to the potential hydrologic pathways for contaminants and the other to the susceptibility of wetlands to disruptions in water supply from projected future changes in climate, were used to indicate which wetlands (greater than 1 ha) in Acadia National Park (ANP) may warrant further investigation or monitoring. The HGM classification system consists of 13 categories: Riverine-Upper Perennial, Riverine-Nonperennial, Riverine- Tidal, Depressional-Closed, Depressional-Semiclosed, Depressional-Open, Depressional-No Ground-Water Input, Mineral Soil Flat, Organic Soil Flat, Tidal Fringe, Lacustrine Fringe, Slope, and Hilltop/Upper Hillslope. A dichotomous key was developed to aid in the classification of wetlands. The National Wetland Inventory maps produced by the U.S. Fish and Wildlife Service provided the wetland mapping units used for this classification. On the basis of topographic map information and geographic information system (GIS) layers at a scale of 1:24,000 or larger, 1,202 wetland units were assigned a preliminary HGM classification. Two of the 13 HGM classes (Riverine-Tidal and Depressional-No Ground-Water Input) were not assigned to any wetlands because criteria for determining those classes are not available at that map scale, and must be determined by more site-specific information. Of the 1,202 wetland polygons classified, which cover 1,830 ha in ANP, 327 were classified as Slope, 258 were Depressional (Open, Semiclosed, and Closed), 231 were Riverine (Upper Perennial and Nonperennial), 210 were Soil Flat (Mineral and Organic), 68 were Lacustrine Fringe, 51 were Tidal Fringe, 22 were Hilltop/Upper Hillslope, and another 35 were small open water bodies. Most small, isolated wetlands classified on the island are Slope wetlands. The least common, Hilltop/Upper Hillslope wetlands, only occur on a few hilltops and shoulders of hills and mountains. Large wetland complexes generally consist of groups of Depressional wetlands and Mineral Soil Flat or Organic Soil Flat wetlands, often with fringing Slope wetlands at their edges and Riverine wetlands near streams flowing through them. The two analyses of wetland hydrologic susceptibility on Mt. Desert Island were applied to 186 wetlands located partially or entirely within ANP. These analyses were conducted using individually mapped catchments for each wetland. The 186 wetlands were aggregated from the original 1,202 mapped wetland polygons on the basis of their HGM classes. Landscape-level hydrologic, geomorphic, and soil variables were defined for the catchments of the wetlands, and transformed into scaled scores from 0 to 10 for each variable. The variables included area of the wetland, area of the catchment, area of the wetland divided by the area of the catchment, the average topographic slope of the catchment, the amount of the catchment where bedrock crops out with no soil cover or excessively thin soil cover, the amount of storage (in lakes and wetlands) in the catchment, the topographic relief of the catchment, the amount of clay-rich soil in the catchment, the amount of manmade impervious surface, whether the wetland had a stream inflow, and whether the wetland had a hydraulic connection to a lake or estuary. These data were determined using a GIS and data layers mapped at a scale of 1:24,000 or larger. These landscape variables were combined in different ways for the two hydrologic susceptibility fact

GIS-aided Statistical Landslide Susceptibility Modeling And Mapping Of Antipolo Rizal (Philippines)

NASA Astrophysics Data System (ADS)

Dumlao, A. J.; Victor, J. A.

2015-09-01

Slope instability associated with heavy rainfall or earthquake is a familiar geotechnical problem in the Philippines. The main objective of this study is to perform a detailed landslide susceptibility assessment of Antipolo City. The statistical method of assessment used was logistic regression. Landslide inventory was done through interpretation of aerial photographs and satellite images with corresponding field verification. In this study, morphologic and non-morphologic factors contributing to landslide occurrence and their corresponding spatial relationships were considered. The analysis of landslide susceptibility was implemented in a Geographic Information System (GIS). The 17320 randomly selected datasets were divided into training and test data sets. K- cross fold validation is done with k= 5. The subsamples are then fitted five times with k-1 training data set and the remaining fold as the validation data set. The AUROC of each model is validated using each corresponding data set. The AUROC of the five models are; 0.978, 0.977, 0.977, 0.974, and 0.979 respectively, implying that the models are effective in correctly predicting the occurrence and nonoccurrence of landslide activity. Field verification was also done. The landslide susceptibility map was then generated from the model. It is classified into four categories; low, moderate, high and very high susceptibility. The study also shows that almost 40% of Antipolo City has been assessed to be potentially dangerous areas in terms of landslide occurrence.

Nonlinear systems dynamics in cardiovascular physiology: The heart rate delay map and lower body negative pressure

NASA Technical Reports Server (NTRS)

Hooker, John C.

1990-01-01

A preliminary study of the applicability of nonlinear dynamic systems analysis techniques to low body negative pressure (LBNP) studies. In particular, the applicability of the heart rate delay map is investigated. It is suggested that the heart rate delay map has potential as a supplemental tool in the assessment of subject performance in LBNP tests and possibly in the determination of susceptibility to cardiovascular deconditioning with spaceflight.

Modelling mass movement susceptibility for Alpine infrastructure in the Karavank Mountains (Austria/Slovenia)

NASA Astrophysics Data System (ADS)

Bauer, C.; Kern, K.; Lieb, G. K.

2012-12-01

The aim of this study is the generation of indicative susceptibility maps on a regional scale that can be used as a decision support tool for land use management (i.e. risk potential on alpine infrastructure). The study in particular focuses on geomorphological processes (rockfall and debris flows in unconsolidated rock) that reshape the land surface by erosion, transport and deposition. When interacting with human activity (e.g. road, alpine trails) such naturally occurring processes can quickly become natural hazards. The study area is located in the Karavank Mountains, a border region between Austria and Slovenia, and covers approx. 200 sq km with maximum altitudes above 2.000 m a.s.l. (Hochstuhl: 2.237 m a.s.l.). The Karavanks form an east-west striking mountain chain (approx. 120 km total length) of the southeastern Alps that consists mainly of thick Triassic carbonate sequences and, with less extent, Paleozoic carbonate rocks crystalline rocks. The mountain chain is separated into the Northern Karavanks and the Southern Karavanks by a structural boundary (Periadriatic Line). In addition, the area is known for extreme weather events due to Adriatic cyclones with daily accumulated precipitation of more than 200 mm that regularly trigger hazardous and torrential processes like rockfall events and debris flows. To assess the triggering factors and trajectories, two different disposition and process models (one for rockfall and one for debris flow, respectively) were developed. The information about potential source areas was obtained by combining various types of information (e.g. DTM derivatives, geotechnical units, vegetation). Threshold slope values for potential rockfall source areas were attributed to different lithological units according to field observations. The defined threshold slope angles cover values from 42° in Triassic carbonates up to 46° in massive crystalline rocks. For debris flows areas with a slope inclination < 20° as well as areas with dense vegetation were excluded as potential source areas. In the next step, the rockfall runout zones were estimated empirically using the cone method. This model is based on the idea that an individual falling rock can reach any place in the area situated inside a cone of given aperture. In contrast, for modelling debris flows, a multiple flow directions method was used to calculate potential pathways and velocities. The method is implemented as a random walk in conjunction with a Monte Carlo approach (using 1000 iterations). Both models were calibrated with field observation data (e.g. GPS measurements) and in addition, model results were validated with high resolution aerial photographs. By overlaying the modelling results with road and trail network information, susceptibility maps were created. These maps clearly show that large parts of the existing Alpine infrastructure are potentially affected by the modelled processes. Therefore, the resulting susceptibility maps provide as a useful tool to indicate areas prone to rockfall and debris flow as well as for the maintenance of the road and trail networks.

Soil erosion assessment using the Universal Soil Loss Equation (USLE) in a GIS framework: A case study of Zacatecas, México

NASA Astrophysics Data System (ADS)

Betanzos Arroyo, L. I.; Prol Ledesma, R. M.; da Silva Pinto da Rocha, F. J. P.

2014-12-01

The Universal Soil Loss Equation (USLE), which is considered to be a contemporary approach in soil loss assessment, was used to assess soil erosion hazard in the Zacatecas mining district. The purpose of this study is to produce erosion susceptibility maps for an area that is polluted with mining tailings which are susceptible to erosion and can disperse the particles that contain heavy metals and other toxic elements. USLE method is based in the estimation of soil loss per unit area and takes into account specific parameters such as precipitation data, topography, soil erodibility, erosivity and runoff. The R-factor (rainfall erosivity) was calculated from monthly and annual precipitation data. The K-factor (soil erodibility) was estimated using soil maps available from the CONABIO at a scale of 1:250000. The LS-factor (slope length and steepness) was determined from a 30-m digital elevation model. A raster-based Geographic Information System (GIS) was used to interactively calculate soil loss and map erosion hazard. The results show that estimated erosion rates ranged from 0 to 4770.48 t/ha year. Maximum proportion of the total area of the Zacatecas mining district have nil to very extremely slight erosion severity. Small areas in the central and south part of the study area shows the critical condition requiring sustainable land management.

Rock-magnetic and geochemical characteristics of relict Vertisols—signs of past climate and recent pedogenic development

NASA Astrophysics Data System (ADS)

Jordanova, Neli; Jordanova, Diana

2016-06-01

Rock-magnetic and geochemical characteristics of three Vertisol profiles with different degree of textural differentiation have been studied. Thermomagnetic analyses, thermal demagnetization of laboratory remanences and acquisition of isothermal remanence curves are applied for identification of iron oxide mineralogy. The main magnetic minerals in Vertisols are ferrihydrite, single-domain magnetite, maghemite and hematite. Variations in magnetic susceptibility, anhysteretic remanent magnetization, isothermal remanent magnetization, as well as different ratios (Xarm/X, ARM/SIRM, S-ratio) along depth are studied. Concentration of magnetic minerals in Vertisols is low, influenced by the intense reductomorphic processes. The lowest magnetic susceptibility is found in the most texturally differentiated soil. However, rock-magnetic data suggest the presence of small, but well defined fraction of single domain-like magnetite with relatively wide grain-size distribution found in those parts of the profiles, which are subjected to most intense and frequent seasonal changes in oxidation-reduction conditions. It is suggested that this fraction is formed as a result of transformations of ferrihydrite under repeated cycles of anaerobic/aerobic conditions. Based on geochemical data, CALMAG weathering index was calculated for the three Vertisols. Using the established relation between CALMAG and mean annual precipitation (MAP), palaeo-MAP was evaluated for the studied profiles. The obtained MAP estimations fall in the range 1000-1200 mm and are much higher compared to contemporary precipitation in the area (MAP in the interval 540-770 mm). This finding confirms the relict character of Vertisols on Bulgarian territory and gives more information about the palaeoclimate during the initial stages of Vertisol formation.

Functional quantitative susceptibility mapping (fQSM).

PubMed

Balla, Dávid Z; Sanchez-Panchuelo, Rosa M; Wharton, Samuel J; Hagberg, Gisela E; Scheffler, Klaus; Francis, Susan T; Bowtell, Richard

2014-10-15

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is a powerful technique, typically based on the statistical analysis of the magnitude component of the complex time-series. Here, we additionally interrogated the phase data of the fMRI time-series and used quantitative susceptibility mapping (QSM) in order to investigate the potential of functional QSM (fQSM) relative to standard magnitude BOLD fMRI. High spatial resolution data (1mm isotropic) were acquired every 3 seconds using zoomed multi-slice gradient-echo EPI collected at 7 T in single orientation (SO) and multiple orientation (MO) experiments, the latter involving 4 repetitions with the subject's head rotated relative to B0. Statistical parametric maps (SPM) were reconstructed for magnitude, phase and QSM time-series and each was subjected to detailed analysis. Several fQSM pipelines were evaluated and compared based on the relative number of voxels that were coincidentally found to be significant in QSM and magnitude SPMs (common voxels). We found that sensitivity and spatial reliability of fQSM relative to the magnitude data depended strongly on the arbitrary significance threshold defining "activated" voxels in SPMs, and on the efficiency of spatio-temporal filtering of the phase time-series. Sensitivity and spatial reliability depended slightly on whether MO or SO fQSM was performed and on the QSM calculation approach used for SO data. Our results present the potential of fQSM as a quantitative method of mapping BOLD changes. We also critically discuss the technical challenges and issues linked to this intriguing new technique. Copyright © 2014 Elsevier Inc. All rights reserved.

QVAST: a new Quantum GIS plugin for estimating volcanic susceptibility

NASA Astrophysics Data System (ADS)

Bartolini, S.; Cappello, A.; Martí, J.; Del Negro, C.

2013-11-01

One of the most important tasks of modern volcanology is the construction of hazard maps simulating different eruptive scenarios that can be used in risk-based decision making in land-use planning and emergency management. The first step in the quantitative assessment of volcanic hazards is the development of susceptibility maps (i.e., the spatial probability of a future vent opening given the past eruptive activity of a volcano). This challenging issue is generally tackled using probabilistic methods that use the calculation of a kernel function at each data location to estimate probability density functions (PDFs). The smoothness and the modeling ability of the kernel function are controlled by the smoothing parameter, also known as the bandwidth. Here we present a new tool, QVAST, part of the open-source geographic information system Quantum GIS, which is designed to create user-friendly quantitative assessments of volcanic susceptibility. QVAST allows the selection of an appropriate method for evaluating the bandwidth for the kernel function on the basis of the input parameters and the shapefile geometry, and can also evaluate the PDF with the Gaussian kernel. When different input data sets are available for the area, the total susceptibility map is obtained by assigning different weights to each of the PDFs, which are then combined via a weighted summation and modeled in a non-homogeneous Poisson process. The potential of QVAST, developed in a free and user-friendly environment, is here shown through its application in the volcanic fields of Lanzarote (Canary Islands) and La Garrotxa (NE Spain).

An expert-based approach to forest road network planning by combining Delphi and spatial multi-criteria evaluation.

PubMed

Hayati, Elyas; Majnounian, Baris; Abdi, Ehsan; Sessions, John; Makhdoum, Majid

2013-02-01

Changes in forest landscapes resulting from road construction have increased remarkably in the last few years. On the other hand, the sustainable management of forest resources can only be achieved through a well-organized road network. In order to minimize the environmental impacts of forest roads, forest road managers must design the road network efficiently and environmentally as well. Efficient planning methodologies can assist forest road managers in considering the technical, economic, and environmental factors that affect forest road planning. This paper describes a three-stage methodology using the Delphi method for selecting the important criteria, the Analytic Hierarchy Process for obtaining the relative importance of the criteria, and finally, a spatial multi-criteria evaluation in a geographic information system (GIS) environment for identifying the lowest-impact road network alternative. Results of the Delphi method revealed that ground slope, lithology, distance from stream network, distance from faults, landslide susceptibility, erosion susceptibility, geology, and soil texture are the most important criteria for forest road planning in the study area. The suitability map for road planning was then obtained by combining the fuzzy map layers of these criteria with respect to their weights. Nine road network alternatives were designed using PEGGER, an ArcView GIS extension, and finally, their values were extracted from the suitability map. Results showed that the methodology was useful for identifying road that met environmental and cost considerations. Based on this work, we suggest future work in forest road planning using multi-criteria evaluation and decision making be considered in other regions and that the road planning criteria identified in this study may be useful.

A glycosylated recombinant subunit candidate vaccine consisting of Ehrlichia ruminantium major antigenic protein1 induces specific humoral and Th1 type cell responses in sheep.

PubMed

Faburay, Bonto; McGill, Jodi; Jongejan, Frans

2017-01-01

Heartwater, or cowdriosis, is a tick-borne disease of domestic and wild ruminants that is endemic in the Caribbean and sub-Saharan Africa. The disease is caused by an intracellular pathogen, Ehrlichia ruminantium and may be fatal within days of the onset of clinical signs with mortality rates of up to 90% in susceptible hosts. Due to the presence of competent tick vectors in North America, there is substantial risk of introduction of heartwater with potentially devastating consequences to the domestic livestock industry. There is currently no reliable or safe vaccine for use globally. To develop a protective DIVA (differentiate infected from vaccinated animals) subunit vaccine for heartwater, we targeted the E. ruminantium immunodominant major antigenic protein1 (MAP1) with the hypothesis that MAP1 is a glycosylated protein and glycans contained in the antigenic protein are important epitope determinants. Using a eukaryotic recombinant baculovirus expression system, we expressed and characterized, for the first time, a glycoform profile of MAP1 of two Caribbean E. ruminantium isolates, Antigua and Gardel. We have shown that the 37-38 kDa protein corresponded to a glycosylated form of the MAP1 protein, whereas the 31-32 kDa molecular weight band represented the non-glycosylated form of the protein frequently reported in scientific literature. Three groups of sheep (n = 3-6) were vaccinated with increasing doses of a bivalent (Antigua and Gardel MAP1) rMAP1 vaccine cocktail formulation with montanide ISA25 as an adjuvant. The glycosylated recombinant subunit vaccine induced E. ruminantium-specific humoral and Th1 type T cell responses, which are critical for controlling intracellular pathogens, including E. ruminantium, in infected hosts. These results provide an important basis for development of a subunit vaccine as a novel strategy to protect susceptible livestock against heartwater in non-endemic and endemic areas.

A glycosylated recombinant subunit candidate vaccine consisting of Ehrlichia ruminantium major antigenic protein1 induces specific humoral and Th1 type cell responses in sheep

PubMed Central

McGill, Jodi; Jongejan, Frans

2017-01-01

Heartwater, or cowdriosis, is a tick-borne disease of domestic and wild ruminants that is endemic in the Caribbean and sub-Saharan Africa. The disease is caused by an intracellular pathogen, Ehrlichia ruminantium and may be fatal within days of the onset of clinical signs with mortality rates of up to 90% in susceptible hosts. Due to the presence of competent tick vectors in North America, there is substantial risk of introduction of heartwater with potentially devastating consequences to the domestic livestock industry. There is currently no reliable or safe vaccine for use globally. To develop a protective DIVA (differentiate infected from vaccinated animals) subunit vaccine for heartwater, we targeted the E. ruminantium immunodominant major antigenic protein1 (MAP1) with the hypothesis that MAP1 is a glycosylated protein and glycans contained in the antigenic protein are important epitope determinants. Using a eukaryotic recombinant baculovirus expression system, we expressed and characterized, for the first time, a glycoform profile of MAP1 of two Caribbean E. ruminantium isolates, Antigua and Gardel. We have shown that the 37–38 kDa protein corresponded to a glycosylated form of the MAP1 protein, whereas the 31–32 kDa molecular weight band represented the non-glycosylated form of the protein frequently reported in scientific literature. Three groups of sheep (n = 3–6) were vaccinated with increasing doses of a bivalent (Antigua and Gardel MAP1) rMAP1 vaccine cocktail formulation with montanide ISA25 as an adjuvant. The glycosylated recombinant subunit vaccine induced E. ruminantium-specific humoral and Th1 type T cell responses, which are critical for controlling intracellular pathogens, including E. ruminantium, in infected hosts. These results provide an important basis for development of a subunit vaccine as a novel strategy to protect susceptible livestock against heartwater in non-endemic and endemic areas. PMID:28957443

Molecular mapping and candidate gene analysis for resistance to powdery mildew in Cucumis sativus stem.

PubMed

Liu, P N; Miao, H; Lu, H W; Cui, J Y; Tian, G L; Wehner, T C; Gu, X F; Zhang, S P

2017-08-31

Powdery mildew (PM) of cucumber (Cucumis sativus), caused by Podosphaera xanthii, is a major foliar disease worldwide and resistance is one of the main objectives in cucumber breeding programs. The resistance to PM in cucumber stem is important to the resistance for the whole plant. In this study, genetic analysis and gene mapping were implemented with cucumber inbred lines NCG-122 (with resistance to PM in the stem) and NCG-121 (with susceptibility in the stem). Genetic analysis showed that resistance to PM in the stem of NCG-122 was qualitative and controlled by a single-recessive nuclear gene (pm-s). Susceptibility was dominant to resistance. In the initial genetic mapping of the pm-s gene, 10 SSR markers were discovered to be linked to pm-s, which was mapped to chromosome 5 (Chr.5) of cucumber. The pm-s gene's closest flanking markers were SSR20486 and SSR06184/SSR13237 with genetic distances of 0.9 and 1.8 cM, respectively. One hundred and fifty-seven pairs of new SSR primers were exploited by the sequence information in the initial mapping region of pm-s. The analysis on the F 2 mapping population using the new molecular markers showed that 17 SSR markers were confirmed to be linked to the pm-s gene. The two closest flanking markers, pmSSR27and pmSSR17, were 0.1 and 0.7 cM from pm-s, respectively, confirming the location of this gene on Chr.5. The physical length of the genomic region containing pm-s was 135.7 kb harboring 21 predicted genes. Among these genes, the gene Csa5G623470 annotated as encoding Mlo-related protein was defined as the most probable candidate gene for the pm-s. The results of this study will provide a basis for marker-assisted selection, and make the benefit for the cloning of the resistance gene.

Preliminary maps of Quaternary deposits and liquefaction susceptibility, nine-county San Francisco Bay region, California: a digital database

USGS Publications Warehouse

Knudsen, Keith L.; Sowers, Janet M.; Witter, Robert C.; Wentworth, Carl M.; Helley, Edward J.; Nicholson, Robert S.; Wright, Heather M.; Brown, Katherine H.

2000-01-01

This report presents a preliminary map and database of Quaternary deposits and liquefaction susceptibility for the nine-county San Francisco Bay region, together with a digital compendium of ground effects associated with past earthquakes in the region. The report consists of (1) a spatial database of fivedata layers (Quaternary deposits, quadrangle index, and three ground effects layers) and two text layers (a labels and leaders layer for Quaternary deposits and for ground effects), (2) two small-scale colored maps (Quaternary deposits and liquefaction susceptibility), (3) a text describing the Quaternary map, liquefaction interpretation, and the ground effects compendium, and (4) the databse description pamphlet. The nine counties surrounding San Francisco Bay straddle the San Andreas fault system, which exposes the region to serious earthquake hazard (Working Group on California Earthquake Probabilities, 1999). Much of the land adjacent to the Bay and the major rivers and streams is underlain by unconsolidated deposits that are particularly vulnerable to earthquake shaking and liquefaction of water-saturated granular sediment. This new map provides a modern and regionally consistent treatment of Quaternary surficial deposits that builds on the pioneering mapping of Helley and Lajoie (Helley and others, 1979) and such intervening work as Atwater (1982), Helley and others (1994), and Helley and Graymer (1997a and b). Like these earlier studies, the current mapping uses geomorphic expression, pedogenic soils, and inferred depositional environments to define and distinguish the map units. In contrast to the twelve map units of Helley and Lajoie, however, this new map uses a complex stratigraphy of some forty units, which permits a more realistic portrayal of the Quaternary depositional system. The two colored maps provide a regional summary of the new mapping at a scale of 1:275,000, a scale that is sufficient to show the general distribution and relationships of the map units but cannot distinguish the more detailed elements that are present in the database. The report is the product of years of cooperative work by the USGS National Earthquake Hazards Reduction Program (NEHRP) and National Cooperative Geologic Mapping Program, William Lettis and & Associates, Inc. (WLA) and, more recently, by the California Division of Mines and Geology as well. An earlier version was submitted to the Geological Survey by WLA as a final report for a NEHRP grant (Knudsen and others, 2000). The mapping has been carried out by WLA geologists under contract to the NEHRP Earthquake Program (Grants #14-08-0001-G2129, 1434-94-G-2499, 1434-HQ-97-GR-03121, and 99-HQ-GR-0095) and with other limited support from the County of Napa, and recently also by the California Division of Mines and Geology. The current map consists of this new mapping and revisions of previous USGS mapping.

Utilizing NASA Earth Observations to Monitor Sinkhole Development and Identify Risk Areas in Dougherty County, Georgia

NASA Astrophysics Data System (ADS)

Cahalan, M. D.; Berry, K.; Amin, M.; Xu, W.; Hu, T.; Milewski, A.

2015-12-01

Located in southwest Georgia, Dougherty County has a growing populace in an agricultural region that relies heavily on groundwater resources. Partly due to escalated groundwater extraction, this area has experienced an increase in sinkhole development over the last decade. Sinkholes pose a threat to infrastructure development, groundwater pollution, and land use operations. The NASA DEVELOP Georgia Disasters and Water Resources team partnered with the City of Albany and Dougherty County Planning and Development Services (PDS) and the Southwest Georgia Water Resources Task Force (SGWRTF) to assess past sinkhole development and identify areas susceptible to future sinkhole formation. Sinkhole mapping was completed utilizing a time-series of elevation data (1999 - 2011) from NASA's SRTM and ASTER missions, as well as European Remote-Sensing (ERS-1 and 2) satellite-derived elevation data. The sinkhole inventory maps and spatial statistical techniques (i.e., geographically-weighted regression) were employed to quantify the factors most influential in sinkhole development. With those results, the susceptibility of every area within Dougherty County to future sinkhole formation was identified. The results of this applied science project will enable the PDS and SGWRTF to make informed decisions on current and future land use, safe infrastructure development, and sustainable water resource management.

Mapping Genetic Variants Associated with Beta-Adrenergic Responses in Inbred Mice

PubMed Central

Hersch, Micha; Peter, Bastian; Kang, Hyun Min; Schüpfer, Fanny; Abriel, Hugues; Pedrazzini, Thierry; Eskin, Eleazar; Beckmann, Jacques S.

2012-01-01

β-blockers and β-agonists are primarily used to treat cardiovascular diseases. Inter-individual variability in response to both drug classes is well recognized, yet the identity and relative contribution of the genetic players involved are poorly understood. This work is the first genome-wide association study (GWAS) addressing the values and susceptibility of cardiovascular-related traits to a selective β 1-blocker, Atenolol (ate), and a β-agonist, Isoproterenol (iso). The phenotypic dataset consisted of 27 highly heritable traits, each measured across 22 inbred mouse strains and four pharmacological conditions. The genotypic panel comprised 79922 informative SNPs of the mouse HapMap resource. Associations were mapped by Efficient Mixed Model Association (EMMA), a method that corrects for the population structure and genetic relatedness of the various strains. A total of 205 separate genome-wide scans were analyzed. The most significant hits include three candidate loci related to cardiac and body weight, three loci for electrocardiographic (ECG) values, two loci for the susceptibility of atrial weight index to iso, four loci for the susceptibility of systolic blood pressure (SBP) to perturbations of the β-adrenergic system, and one locus for the responsiveness of QTc (p<10−8). An additional 60 loci were suggestive for one or the other of the 27 traits, while 46 others were suggestive for one or the other drug effects (p<10−6). Most hits tagged unexpected regions, yet at least two loci for the susceptibility of SBP to β-adrenergic drugs pointed at members of the hypothalamic-pituitary-thyroid axis. Loci for cardiac-related traits were preferentially enriched in genes expressed in the heart, while 23% of the testable loci were replicated with datasets of the Mouse Phenome Database (MPD). Altogether these data and validation tests indicate that the mapped loci are relevant to the traits and responses studied. PMID:22859963

Landslide Susceptibility Mapping Using Geospatial Technology in South Eastern Part of Nilgiri District, Tamilnadu, India

NASA Astrophysics Data System (ADS)

Thangasamy, N.; Varathan, R.

2013-05-01

Landslides are often destructive and periodically affect the Nilgiris district. Two method viz., Frequency ratio (FR) and Weights of evidence (WofE) were used to reclassify the sub-variables and the landslide susceptibility index (LSI) was calculated by weighted sum overlay analysis. The final LS Zonation map was prepared from the LSI and the area was classified into two zones. Validation of the LSM was the next step and was accomplished by excluding some landslide points in the GIS analyses and overlying the unused landslides points over the LSM. The LSMs prepared using the FR and WofE methods are reliable as more than 75% of the excluded slides fall in high and very high landslide susceptibility zones and the error of mismatch in the two maps is negligible.During the course of this study landslides devastated the Kethi, Coonoor, Barliyar and Kothagiri areas due to an extreme event with 374 to 1,171 mm rainfall received in these stations in just three days on 8th to 10th November, 2009. The rainfall event is unprecedented and such extreme rainfall has not occurred in the region since meteorological records are maintained. Over 100 landslides took place in the area of which 75 are major slides and more 43 people died and 200 houses were damaged. The event was documented and a data base containing the location, details of death, slide characteristics and photographs was prepared. Further, the probability of landslide occurrence may change over time due to changes in land use, unscientific massive developmental activities and establishing settlements without adopting proper safety measures. The study also highlights the need for maintenance of landslide database and installation of more rain gauge stations to update and improve the LSM so as to reduce the risk of landslide hazard faced by the Community. NaveenRaj.T INDIA LANDSLIDE SUSCEPTIBILITY MAPPING USING GEOSPATIAL TECHNOLOGY IN SOUTH EASTERN PART OF NILGIRI DISTRICT, TAMILNADU, INDIA.

Presence-only approach to assess landslide triggering-thickness susceptibility. A test for the Mili catchment (North-Eastern Sicily, Italy)

NASA Astrophysics Data System (ADS)

Lombardo, Luigi; Fubelli, Giandomenico; Amato, Gabriele; Bonasera, Mauro; Hochschild, Volker; Rotigliano, Edoardo

2015-04-01

This study aims at comparing the performances of a presence only approach, namely Maximum Entropy, in assessing landslide triggering-thickness susceptibility within the Mili catchment, located in the north-eastern Sicily, Italy. This catchment has been recently exposed to three main meteorological extreme events, resulting in the activation of multiple fast landslides, which occurred on the 1st October 2009, 10th March 2010 and 1st March 2011. Differently from the 2009 event, which only marginally hit the catchment, the 2010 and 2011 storms fully involved the area of the Mili catchment. Detailed field data was collected to associate the thickness of mobilised materials at the triggering zone to each mass movement within the catchment. This information has been used to model the landslide susceptibility for two classes of processes clustered into shallow failures for maximum depths of 0.5m and deep ones in case of values equal or greater than 0.5m. As the authors believed that the peculiar geomorphometry of this narrow and steep catchment played a fundamental role in generating two distinct patterns of landslide thicknesses during the initiation phase, a HRDEM was used to extract topographic attributes to express near-triggering geomorphological conditions. On the other hand, medium resolution vegetation indexes derived from ASTER scenes were used as explanatory variables pertaining to a wider spatial neighbourhood, whilst a revised geological map, the land use from CORINE and a tectonic map were used to convey an even wider area connected to the slope instability. The choice of a presence-only approach allowed to effectively discriminate between the two types of landslide thicknesses at the triggering zone, producing outstanding prediction skills associated with relatively low variances across a set of 20 randomly generated replicates. The validation phase produced indeed average AUC values of 0.91 with a standard deviation of 0.03 for both the modelled landslide thicknesses. In addition, the role of each predictor within the whole modelling procedure was assessed by applying Jackknife tests. These analyses focussed on evaluating the variation of AUC values across replicates comparing single variable models with models based on the full set of predictors iteratively deprived of one covariate. As a result, relevant differences among main contributors between the two considered classes were also quantitatively derived and geomorphologically interpreted. This work can be considered as an example for creating specific landslide susceptibility maps to be used in master planning in order to establish proportional countermeasures to different activation mechanisms. Keywords: statistical analysis, shallow landslide, landslide susceptibility, triggering factors, presence-only approach

The Role of Abcb5 Alleles in Susceptibility to Haloperidol-Induced Toxicity in Mice and Humans

PubMed Central

Zheng, Ming; Zhang, Haili; Dill, David L.; Clark, J. David; Tu, Susan; Yablonovitch, Arielle L.; Tan, Meng How; Zhang, Rui; Rujescu, Dan; Wu, Manhong; Tessarollo, Lino; Vieira, Wilfred; Gottesman, Michael M.; Deng, Suhua; Eberlin, Livia S.; Zare, Richard N.; Billard, Jean-Martin; Gillet, Jean-Pierre; Li, Jin Billy; Peltz, Gary

2015-01-01

Background We know very little about the genetic factors affecting susceptibility to drug-induced central nervous system (CNS) toxicities, and this has limited our ability to optimally utilize existing drugs or to develop new drugs for CNS disorders. For example, haloperidol is a potent dopamine antagonist that is used to treat psychotic disorders, but 50% of treated patients develop characteristic extrapyramidal symptoms caused by haloperidol-induced toxicity (HIT), which limits its clinical utility. We do not have any information about the genetic factors affecting this drug-induced toxicity. HIT in humans is directly mirrored in a murine genetic model, where inbred mouse strains are differentially susceptible to HIT. Therefore, we genetically analyzed this murine model and performed a translational human genetic association study. Methods and Findings A whole genome SNP database and computational genetic mapping were used to analyze the murine genetic model of HIT. Guided by the mouse genetic analysis, we demonstrate that genetic variation within an ABC-drug efflux transporter (Abcb5) affected susceptibility to HIT. In situ hybridization results reveal that Abcb5 is expressed in brain capillaries, and by cerebellar Purkinje cells. We also analyzed chromosome substitution strains, imaged haloperidol abundance in brain tissue sections and directly measured haloperidol (and its metabolite) levels in brain, and characterized Abcb5 knockout mice. Our results demonstrate that Abcb5 is part of the blood-brain barrier; it affects susceptibility to HIT by altering the brain concentration of haloperidol. Moreover, a genetic association study in a haloperidol-treated human cohort indicates that human ABCB5 alleles had a time-dependent effect on susceptibility to individual and combined measures of HIT. Abcb5 alleles are pharmacogenetic factors that affect susceptibility to HIT, but it is likely that additional pharmacogenetic susceptibility factors will be discovered. Conclusions ABCB5 alleles alter susceptibility to HIT in mouse and humans. This discovery leads to a new model that (at least in part) explains inter-individual differences in susceptibility to a drug-induced CNS toxicity. PMID:25647612

Fast T2*-weighted MRI of the prostate at 3 Tesla.

PubMed

Hardman, Rulon L; El-Merhi, Fadi; Jung, Adam J; Ware, Steve; Thompson, Ian M; Friel, Harry T; Peng, Qi

2011-04-01

To describe a rapid T2*-weighted (T2*W), three-dimensional (3D) echo planar imaging (EPI) sequence and its application in mapping local magnetic susceptibility variations in 3 Tesla (T) prostate MRI. To compare the sensitivity of T2*W EPI with routinely used T1-weighted turbo-spin echo sequence (T1W TSE) in detecting hemorrhage and the implications on sequences sensitive to field inhomogeneities such as MR spectroscopy (MRS). B(0) susceptibility weighted mapping was performed using a 3D EPI sequence featuring a 2D spatial excitation pulse with gradients of spiral k-space trajectory. A series of 11 subjects were imaged using 3T MRI and combination endorectal (ER) and six-channel phased array cardiac coils. T1W TSE and T2*W EPI sequences were analyzed quantitatively for hemorrhage contrast. Point resolved spectroscopy (PRESS MRS) was performed and data quality was analyzed. Two types of susceptibility variation were identified: hemorrhagic and nonhemorrhagic T2*W-positive areas. Post-biopsy hemorrhage lesions showed on average five times greater contrast on the T2*W images than T1W TSE images. Six nonhemorrhage regions of severe susceptibility artifact were apparent on the T2*W images that were not seen on standard T1W or T2W images. All nonhemorrhagic susceptibility artifact regions demonstrated compromised spectral quality on 3D MRS. The fast T2*W EPI sequence identifies hemorrhagic and nonhemorrhagic areas of susceptibility variation that may be helpful in prostate MRI planning at 3.0T. Copyright © 2011 Wiley-Liss, Inc.

Genomic regions underlying susceptibility to bovine tuberculosis in Holstein-Friesian cattle.

PubMed

Raphaka, Kethusegile; Matika, Oswald; Sánchez-Molano, Enrique; Mrode, Raphael; Coffey, Mike Peter; Riggio, Valentina; Glass, Elizabeth Janet; Woolliams, John Arthur; Bishop, Stephen Christopher; Banos, Georgios

2017-03-23

The significant social and economic loss as a result of bovine tuberculosis (bTB) presents a continuous challenge to cattle industries in the UK and worldwide. However, host genetic variation in cattle susceptibility to bTB provides an opportunity to select for resistant animals and further understand the genetic mechanisms underlying disease dynamics. The present study identified genomic regions associated with susceptibility to bTB using genome-wide association (GWA), regional heritability mapping (RHM) and chromosome association approaches. Phenotypes comprised de-regressed estimated breeding values of 804 Holstein-Friesian sires and pertained to three bTB indicator traits: i) positive reactors to the skin test with positive post-mortem examination results (phenotype 1); ii) positive reactors to the skin test regardless of post-mortem examination results (phenotype 2) and iii) as in (ii) plus non-reactors and inconclusive reactors to the skin tests with positive post-mortem examination results (phenotype 3). Genotypes based on the 50 K SNP DNA array were available and a total of 34,874 SNPs remained per animal after quality control. The estimated polygenic heritability for susceptibility to bTB was 0.26, 0.37 and 0.34 for phenotypes 1, 2 and 3, respectively. GWA analysis identified a putative SNP on Bos taurus autosomes (BTA) 2 associated with phenotype 1, and another on BTA 23 associated with phenotype 2. Genomic regions encompassing these SNPs were found to harbour potentially relevant annotated genes. RHM confirmed the effect of these genomic regions and identified new regions on BTA 18 for phenotype 1 and BTA 3 for phenotypes 2 and 3. Heritabilities of the genomic regions ranged between 0.05 and 0.08 across the three phenotypes. Chromosome association analysis indicated a major role of BTA 23 on susceptibility to bTB. Genomic regions and candidate genes identified in the present study provide an opportunity to further understand pathways critical to cattle susceptibility to bTB and enhance genetic improvement programmes aiming at controlling and eradicating the disease.

Permeability of soils in Mississippi

USGS Publications Warehouse

O'Hara, Charles G.

1994-01-01

The permeability of soils in Mississippi was determined and mapped using a geographic information system (GIS). Soil permeabilities in Mississippi were determined to range in value from nearly 0.0 to values exceeding 5.0 inches per hour. The U.S. Soil Conservation Service's State Soil Geographic Data Base (STATSGO) was used as the primary source of data for the determination of area-weighted soil permeability. STATSGO provides soil layer properties that are spatially referenced to mapped areas. These mapped areas are referred to as polygons in the GIS. The polygons arc boundaries of soils mapped as a group and are given unique Map Unit Identifiers (MUIDs). The data describing the physical characteristics of the soils within each polygon are stored in a tabular data base format and are referred to as attributes. The U.S. Soil Conservation Service developed STATSGO to be primarily used as a guide for regional resource planning, management, and monitoring. STATSGO was designed so that soil information could be extracted from properties tables at the layer level, combined by component, and statistically expanded to cover the entire map unit. The results of this study provide a mapped value for permeability which is representative of the vertical permeability of soils in that area. The resultant permeability map provides a representative vertical soil permeability for a given area sufficient for county, multi- county, and area planning, and will be used as the soil permeability data component in the evaluation of the susceptibility of major aquifers to contami- nation in Mississippi.

Map making in the 21st century: charting breast cancer susceptibility pathways in rodent models.

PubMed

Blackburn, Anneke C; Jerry, D Joseph

2011-04-01

Genetic factors play an important role in determining risk and resistance to increased breast cancer. Recent technological advances have made it possible to analyze hundreds of thousands of single nucleotide polymorphisms in large-scale association studies in humans and have resulted in identification of alleles in over 20 genes that influence breast cancer risk. Despite these advances, the challenge remains in identifying what the functional polymorphisms are that confer the increased risk, and how these genetic variants interact with each other and with environmental factors. In rodents, the incidence of mammary tumors varies among strains, such that they can provide alternate ideas for candidate pathways involved in humans. Mapping studies in animals have unearthed numerous loci for breast cancer susceptibility that have been validated in human populations. In a reciprocal manner, knockin and knockout mice have been used to validate the tumorigenicity of risk alleles found in population studies. Rodent studies also underscore the complexity of interactions among alleles. The fact that genes affecting risk and resistance to mammary tumors in rodents depend greatly upon the carcinogenic challenge emphasizes the importance of gene x environment interactions. The challenge to rodent geneticists now is to capitalize on the ability to control the genetics and environment in rodent models of tumorigenesis to better understand the biology of breast cancer development, to identify those polymorphisms most relevant to human susceptibility and to identify compensatory pathways that can be targeted for improved prevention in women at highest risk of developing breast cancer.

Landslide Susceptibility Statistical Methods: A Critical and Systematic Literature Review

NASA Astrophysics Data System (ADS)

Mihir, Monika; Malamud, Bruce; Rossi, Mauro; Reichenbach, Paola; Ardizzone, Francesca

2014-05-01

Landslide susceptibility assessment, the subject of this systematic review, is aimed at understanding the spatial probability of slope failures under a set of geomorphological and environmental conditions. It is estimated that about 375 landslides that occur globally each year are fatal, with around 4600 people killed per year. Past studies have brought out the increasing cost of landslide damages which primarily can be attributed to human occupation and increased human activities in the vulnerable environments. Many scientists, to evaluate and reduce landslide risk, have made an effort to efficiently map landslide susceptibility using different statistical methods. In this paper, we do a critical and systematic landslide susceptibility literature review, in terms of the different statistical methods used. For each of a broad set of studies reviewed we note: (i) study geography region and areal extent, (ii) landslide types, (iii) inventory type and temporal period covered, (iv) mapping technique (v) thematic variables used (vi) statistical models, (vii) assessment of model skill, (viii) uncertainty assessment methods, (ix) validation methods. We then pulled out broad trends within our review of landslide susceptibility, particularly regarding the statistical methods. We found that the most common statistical methods used in the study of landslide susceptibility include logistic regression, artificial neural network, discriminant analysis and weight of evidence. Although most of the studies we reviewed assessed the model skill, very few assessed model uncertainty. In terms of geographic extent, the largest number of landslide susceptibility zonations were in Turkey, Korea, Spain, Italy and Malaysia. However, there are also many landslides and fatalities in other localities, particularly India, China, Philippines, Nepal and Indonesia, Guatemala, and Pakistan, where there are much fewer landslide susceptibility studies available in the peer-review literature. This raises some concern that existing studies do not always cover all the regions globally that currently experience landslides and landslide fatalities.

Wingless-type MMTV integration site family member 2 (WNT2) gene is associated with resistance to MAP in faecal culture and antibody response in Holstein cattle.

PubMed

Pauciullo, A; Küpper, J; Brandt, H; Donat, K; Iannuzzi, L; Erhardt, G

2015-04-01

Mycobacterium avium subspecies paratuberculosis (MAP) is a pathogenic bacterium responsible for the lethal Johne's disease in cattle. So far, several genome-wide association studies (GWAS) have been carried out to identify chromosomal regions highly associated with Johne's disease. The aim of this study was to investigate the genetic variability within a pool of seven genes (LAMB1, DLD, WNT2, PRDM1, SOCS5, PTGER4 and IL10) indicated by former GWAS/RNA-Seq studies as putatively associated with MAP infections and to achieve a confirmation study of association with paratuberculosis susceptibility in a population of 324 German Holstein cattle (162 cases MAP positive and 162 controls MAP negative) using ELISA and fecal cultural tests. SNP validation and genotyping information are provided, quick methods for allelic discrimination were set up and transcription factor binding analyses were performed. The rs43390642:G>TSNP in the WNT2 promoter region is associated with paratuberculosis susceptibility (P = 0.013), suggesting a protective role of the T allele (P = 0.043; odds ratio 0.50 [0.25-0.97]). The linkage disequilibrium with the DLD rs134692583:A>T might suggest a combined mechanism of action of these neighboring genes in resistance to MAP infection, which is also supported by a significant effect shown by the haplotype DLD(T) /WNT2(T) (P = 0.047). In silico analysis predicted rs43390642:G>T and rs134692583:A>T as essential parts of binding sites for the transcription factors GR, C/EBPβ and GATA-1, hence suggesting a potential influence on WNT2 and DLD gene expression. This study confirmed the region on BTA 4 (UMD 3.1: 50639460-51397892) as involved in tolerance/resistance to Johne's disease. In addition, this study clarifies the involvement of the investigated genes in MAP infection and contributes to the understanding of genetic variability involved in Johne's disease susceptibility. © 2015 Stichting International Foundation for Animal Genetics.

Mapping Vulnerability to Disasters in Latin America and the Caribbean, 1900-2007

USGS Publications Warehouse

Maynard-Ford, Miriam C.; Phillips, Emily C.; Chirico, Peter G.

2008-01-01

The vulnerability of a population and its infrastructure to disastrous events is a factor of both the probability of a hazardous event occurring and the community's ability to cope with the resulting impacts. Therefore, the ability to accurately identify vulnerable populations and places in order to prepare for future hazards is of critical importance for disaster mitigation programs. This project created maps of higher spatial resolution of vulnerability to disaster in Latin America and the Caribbean from 1900 to 2007 by mapping disaster data by first-level administrative boundaries with the objective of identifying geographic trends in regional occurrences of disasters and vulnerable populations. The method of mapping by administrative level is an improvement on displaying and analyzing disasters at the country level and shows the relative intensity of vulnerability within and between countries in the region. Disaster mapping at the country level produces only a basic view of which countries experience various types of natural disasters. Through disaggregation, the data show which geographic areas of these countries, including populated areas, are historically most susceptible to different hazard types.

Bone quantitative susceptibility mapping using a chemical species-specific R2* signal model with ultrashort and conventional echo data.

PubMed

Dimov, Alexey V; Liu, Zhe; Spincemaille, Pascal; Prince, Martin R; Du, Jiang; Wang, Yi

2018-01-01

To develop quantitative susceptibility mapping (QSM) of bone using an ultrashort echo time (UTE) gradient echo (GRE) sequence for signal acquisition and a bone-specific effective transverse relaxation rate ( R2*) to model water-fat MR signals for field mapping. Three-dimensional radial UTE data (echo times ≥ 40 μs) was acquired on a 3 Tesla scanner and fitted with a bone-specific signal model to map the chemical species and susceptibility field. Experiments were performed ex vivo on a porcine hoof and in vivo on healthy human subjects (n = 7). For water-fat separation, a bone-specific model assigning R2* decay mostly to water was compared with the standard models that assigned the same decay for both fat and water. In the ex vivo experiment, bone QSM was correlated with CT. Compared with standard models, the bone-specific R2* method significantly reduced errors in the fat fraction within the cortical bone in all tested data sets, leading to reduced artifacts in QSM. Good correlation was found between bone CT and QSM values in the porcine hoof (R 2  = 0.77). Bone QSM was successfully generated in all subjects. The QSM of bone is feasible using UTE with a conventional echo time GRE acquisition and a bone-specific R2* signal model. Magn Reson Med 79:121-128, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Quantification of susceptibility change at high-concentrated SPIO-labeled target by characteristic phase gradient recognition.

PubMed

Zhu, Haitao; Nie, Binbin; Liu, Hua; Guo, Hua; Demachi, Kazuyuki; Sekino, Masaki; Shan, Baoci

2016-05-01

Phase map cross-correlation detection and quantification may produce highlighted signal at superparamagnetic iron oxide nanoparticles, and distinguish them from other hypointensities. The method may quantify susceptibility change by performing least squares analysis between a theoretically generated magnetic field template and an experimentally scanned phase image. Because characteristic phase recognition requires the removal of phase wrap and phase background, additional steps of phase unwrapping and filtering may increase the chance of computing error and enlarge the inconsistence among algorithms. To solve problem, phase gradient cross-correlation and quantification method is developed by recognizing characteristic phase gradient pattern instead of phase image because phase gradient operation inherently includes unwrapping and filtering functions. However, few studies have mentioned the detectable limit of currently used phase gradient calculation algorithms. The limit may lead to an underestimation of large magnetic susceptibility change caused by high-concentrated iron accumulation. In this study, mathematical derivation points out the value of maximum detectable phase gradient calculated by differential chain algorithm in both spatial and Fourier domain. To break through the limit, a modified quantification method is proposed by using unwrapped forward differentiation for phase gradient generation. The method enlarges the detectable range of phase gradient measurement and avoids the underestimation of magnetic susceptibility. Simulation and phantom experiments were used to quantitatively compare different methods. In vivo application performs MRI scanning on nude mice implanted by iron-labeled human cancer cells. Results validate the limit of detectable phase gradient and the consequent susceptibility underestimation. Results also demonstrate the advantage of unwrapped forward differentiation compared with differential chain algorithms for susceptibility quantification at high-concentrated iron accumulation. Copyright © 2015 Elsevier Inc. All rights reserved.

Lgn1, a gene that determines susceptibility to Legionella pneumophila, maps to mouse chromosome 13

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dietrich, W.F.; Damron, D.M.; Lander, E.S.

1995-04-10

The intracellular pathogen Legionella pneumophila is unable to replicate in macrophages derived from most inbred mouse strains. Here, we report the mapping of a gene, called Lgn1, that determines whether mouse macrophages are permissive for the intracellular replication of L. pneumophila. Although Lgn1 has been previously reported to map to mouse chromosome 15, we show here that it actually maps to chromosome 13, between D13Mit128 and D13Mit70. In the absence of any regional candidates for Lgn1, this map position will facilitate positional cloning attempts directed at this gene. 22 refs., 2 figs., 2 tabs.

Comparative studies of groundwater vulnerability assessment

NASA Astrophysics Data System (ADS)

Maria, Rizka

2018-02-01

Pollution of groundwater is a primary issue because aquifers are susceptible to contamination from land use and anthropogenic impacts. Groundwater susceptibility is intrinsic and specific. Intrinsic vulnerability refers to an aquifer that is susceptible to pollution and to the geological and hydrogeological features. Vulnerability assessment is an essential step in assessing groundwater contamination. This approach provides a visual analysis for helping planners and decision makers to achieve the sustainable management of water resources. Comparative studies are applying different methodologies to result in the basic evaluation of the groundwater vulnerability. Based on the comparison of methods, there are several advantages and disadvantages. SI can be overlaid on DRASTIC and Pesticide DRASTIC to extract the divergence in sensitivity. DRASTIC identifies low susceptibility and underestimates the pollution risk while Pesticide DRASTIC and SI represents better risk and is recommended for the future. SINTACS method generates very high vulnerability zones with surface waters and aquifer interactions. GOD method could be adequate for vulnerability mapping in karstified carbonate aquifers at small-moderate scales, and EPIK method can be used for large scale. GOD method is suitable for designing large area such as land management while DRASTIC has good accuracy and more real use in geoenvironmental detailed studies.

Spatial Resolution Effects of Digital Terrain Models on Landslide Susceptibility Analysis

NASA Astrophysics Data System (ADS)

Chang, K. T.; Dou, J.; Chang, Y.; Kuo, C. P.; Xu, K. M.; Liu, J. K.

2016-06-01

The purposes of this study are to identify the maximum number of correlated factors for landslide susceptibility mapping and to evaluate landslide susceptibility at Sihjhong river catchment in the southern Taiwan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN). The landslide inventory data of the Central Geological Survey (CGS, MOEA) in 2004-2014 and two digital elevation model (DEM) datasets including a 5-meter LiDAR DEM and a 30-meter Aster DEM were prepared. We collected thirteen possible landslide-conditioning factors. Considering the multi-collinearity and factor redundancy, we applied the CF approach to optimize these thirteen conditioning factors. We hypothesize that if the CF values of the thematic factor layers are positive, it implies that these conditioning factors have a positive relationship with the landslide occurrence. Therefore, based on this assumption and positive CF values, seven conditioning factors including slope angle, slope aspect, elevation, terrain roughness index (TRI), terrain position index (TPI), total curvature, and lithology have been selected for further analysis. The results showed that the optimized-factors model provides a better accuracy for predicting landslide susceptibility in the study area. In conclusion, the optimized-factors model is suggested for selecting relative factors of landslide occurrence.

Landslide Susceptibility Index Determination Using Aritificial Neural Network

NASA Astrophysics Data System (ADS)

Kawabata, D.; Bandibas, J.; Urai, M.

2004-12-01

The occurrence of landslide is the result of the interaction of complex and diverse environmental factors. The geomorphic features, rock types and geologic structure are especially important base factors of the landslide occurrence. Generating landslide susceptibility index by defining the relationship between landslide occurrence and that base factors using conventional mathematical and statistical methods is very difficult and inaccurate. This study focuses on generating landslide susceptibility index using artificial neural networks in Southern Japanese Alps. The training data are geomorphic (e.g. altitude, slope and aspect) and geologic parameters (e.g. rock type, distance from geologic boundary and geologic dip-strike angle) and landslides. Artificial neural network structure and training scheme are formulated to generate the index. Data from areas with and without landslide occurrences are used to train the network. The network is trained to output 1 when the input data are from areas with landslides and 0 when no landslide occurred. The trained network generates an output ranging from 0 to 1 reflecting the possibility of landslide occurrence based on the inputted data. Output values nearer to 1 means higher possibility of landslide occurrence. The artificial neural network model is incorporated into the GIS software to generate a landslide susceptibility map.

Potentiality of SENTINEL-1 for landslide detection: first results in the Molise Region (Italy)

NASA Astrophysics Data System (ADS)

Barra, Anna; Monserrat, Oriol; Mazzanti, Paolo; Esposito, Carlo; Crosetto, Michele; Scarascia Mugnozza, Gabriele

2016-04-01

A detailed inventory map, including information on landslide activity, is one of the most important input to landslide susceptibility and hazard analyses. The contribution of satellite SAR Interferometry in landslide risk mitigation is well-known within the scientific community. In fact, many encouraging results have been obtained, principally, in areas characterized by high coherence of the images (e.g. due to rock lithology or urban environment setting). In terms of coherence, the expected increased capabilities of Sentinel-1 for landslide mapping and monitoring are connected to both wavelength (55.5 mm) and short temporal baseline (12 days). The latter one is expected to be a key feature for increasing coherence and for defining monitoring and updating plans. With the aim of assessing these potentialities, we processed a set of 14 Sentinel-1 SLC images, acquired during a temporal span of 7 months, over the Molise region (Southern Italy), a critical area geologically susceptible to landslides. Even though Molise is mostly covered by crops and forested areas (63% and 35% respectively), that means a non-optimal coherence condition for SAR interferometry, promising results have been obtained. This has been achieved by integrating differential interferometric SAR techniques (12-days interferograms and time series) with GIS multilayer analysis (optical, geological, geomorphological, etc.). Specifically, analyzing a single burst of a Sentinel-1 frame (approximately 1875 km2), 62 landslides have been detected, thus allowing to improve the pre-existing inventory maps both in terms of landslide boundaries and state of activity. The results of our ongoing research show that Sentinel-1 can give a significant improvement in terms of exploitation of SAR data for landslide mapping and monitoring. As a matter of fact, by analyzing longer periods, it is expected to achieve a better understanding of landslide behavior and its relationship with triggering factors. This will be key to perform hazard analyses. Further research will be focused in finding algorithms to automatically detect and extract patterns and in developing a more reliable methodology. This will be done by integrating the Sentinel-1 data with other types of data and, in particular, with Sentinel-2 imagery.

Genes tagging and molecular diversity of red rot susceptible/tolerant sugarcane hybrids using c-DNA and unigene derived markers.

PubMed

Singh, R K; Singh, R B; Singh, S P; Sharma, M L

2012-04-01

Sugarcane is an important international commodity as a valuable agricultural crop especially in tropical and subtropical countries. Two bulked DNA used to screen polymorphic primers from commercial hybrids (varieties) with moderately resistant and highly susceptible to red rot disease. Among 145 simple sequence repeat and unigene primers screened, 37 (25%) were found to be highly robust and polymorphic with Polymorphism Information Content values ranging from 0.50 to 1.00 with the mean value of 0.82. Among these microsatellites, twenty one were used in the study of genetic relationships and marker identification in sugarcane varieties for red rot resistance. A total of 105 polymorphic DNA bands were identified, with their fragment size ranging from 54 to 1,280 bp. Jaccard's similarity coefficient value recorded between closely related hybrids was 0.986 while lowest coefficient value of 0.341 was detected with distantly related hybrids. The average similarity coefficient among these hybrids was 0.663. Cluster analysis resulted in a dendrogram with two major clusters separating the moderately resistant varieties from highly susceptible varieties. Three group specific fragments amplified by unigene Saccharum microsatellite primers viz; two markers UGSM316(850) and UGSM316(60) were closely associated with moderately resistant varieties by appearing bands in this region but the bands were absent in highly susceptible varieties. Similarly UGSM316(400) marker was tightly linked with highly susceptible varieties by amplifying uniformly in sugarcane varieties showing highly susceptible reaction to red rot but it was absent in moderately resistant varietal groups. Validation of red rot resistance/susceptibility associated markers on a group of different mapping populations for red rot resistant/susceptible traits is in progress.

Identification of prostate cancer modifier pathways using parental strain expression mapping

PubMed Central

Xu, Qing; Majumder, Pradip K.; Ross, Kenneth; Shim, Yeonju; Golub, Todd R.; Loda, Massimo; Sellers, William R.

2007-01-01

Inherited genetic risk factors play an important role in cancer. However, other than the Mendelian fashion cancer susceptibility genes found in familial cancer syndromes, little is known about risk modifiers that control individual susceptibility. Here we developed a strategy, parental strain expression mapping, that utilizes the homogeneity of inbred mice and genome-wide mRNA expression analyses to directly identify candidate germ-line modifier genes and pathways underlying phenotypic differences among murine strains exposed to transgenic activation of AKT1. We identified multiple candidate modifier pathways and, specifically, the glycolysis pathway as a candidate negative modulator of AKT1-induced proliferation. In keeping with the findings in the murine models, in multiple human prostate expression data set, we found that enrichment of glycolysis pathways in normal tissues was associated with decreased rates of cancer recurrence after prostatectomy. Together, these data suggest that parental strain expression mapping can directly identify germ-line modifier pathways of relevance to human disease. PMID:17978178

Significant locations in auxiliary data as seeds for typical use cases of point clustering

NASA Astrophysics Data System (ADS)

Kröger, Johannes

2018-05-01

Random greedy clustering and grid-based clustering are highly susceptible by their initial parameters. When used for point data clustering in maps they often change the apparent distribution of the underlying data. We propose a process that uses precomputed weighted seed points for the initialization of clusters, for example from local maxima in population density data. Exemplary results from the clustering of a dataset of petrol stations are presented.

Assessing Seismic Hazards - Algorithms, Maps, and Emergency Scenarios

NASA Astrophysics Data System (ADS)

Ferriz, H.

2007-05-01

Public officials in charge of building codes, land use planning, and emergency response need sound estimates of seismic hazards. Sources may be well defined (e.g., active faults that have a surface trace) or diffuse (e.g., a subduction zone or a blind-thrust belt), but in both cases one can use a deterministic or worst-case scenario approach. For each scenario, a design earthquake is selected based on historic data or the known length of Holocene ruptures (as determined by geologic mapping). Horizontal ground accelerations (HGAs) can then be estimated at different distances from the earthquake epicenter using published attenuation relations (e.g., Seismological Res. Letters, v. 68, 1997) and estimates of the elastic properties of the substrate materials. No good algorithms are available to take into account reflection of elastic waves across other fault planes (e.g., a common effect in California, where there are many strands of the San Andreas fault), or amplification of waves in water-saturated alluvial and lacustrine basins (e.g., the Mexico City basin), but empirical relations can be developed by correlating historic damage patterns with predicted HGAs. The ultimate result is a map of HGAs. With this map, and with additional data on depth to groundwater and geotechnical properties of local soils, a liquefaction susceptibility map can be prepared, using published algorithms (e.g., J. of Geotech. Geoenv. Eng., v. 127, p. 817-833, 2001; Eng. Geology Practice in N. California, p. 579-594, 2001). Finally, the HGA estimates, digital elevation models, geologic structural data, and geotechnical properties of local geologic units can be used to prepare a slope failure susceptibility map (e.g., Eng. Geology Practice in N. California, p. 77-94, 2001). Seismic hazard maps are used by: (1) Building officials to determine areas of the city where special construction codes have to be implemented, and where existing buildings may need to be retrofitted. (2) Planning officials to evaluate plans for new growth (though in most cities land use patterns are historically established). (3) Emergency response officials to plan emergency operations. (4) Insurance commissioners to estimate losses and insurance claims (e.g., with FEMA's software HAZUS).

Magnetic mapping of distribution of wood ash used for fertilization of forest soil.

PubMed

Petrovský, Eduard; Remeš, Jiří; Kapička, Aleš; Podrázský, Vilém; Grison, Hana; Borůvka, Luboš

2018-06-01

The effect of wood-ash fertilization on forest soils has been assessed mainly through geochemical methods (e.g., content of soil organic matter or nutrients). However, a simple and fast method of determining the distribution of the ash and the extent of affected soil is missing. In this study we present the use of magnetic susceptibility, which is controlled by Fe-oxides, in comparing the fertilized soil in the forest plantation of pine and oak with intact forest soil. Spatial and vertical distribution of magnetic susceptibility was measured in an oak and pine plantation next to stems of young plants, where wood ash was applied as fertilizer. Pattern of the susceptibility distribution was compared with that in non-fertilized part of the plantation as well as with a spot of intact natural forest soil nearby. Our results show that the wood-ash samples contain significant amount of ferrimagnetic magnetite with susceptibility higher than that of typical forest soil. Clear differences were observed between magnetic susceptibility of furrows and ridges. Moreover, the dispersed ash remains practically on the surface, does not penetrate to deeper layers. Finally, our data suggest significant differences in surface values between the pine and oak plants. Based on this study we may conclude that magnetic susceptibility may represent a simple and approximate method of assessing the extent of soil affected by wood-ash. Copyright © 2018 Elsevier B.V. All rights reserved.

Dentate nucleus iron deposition is a potential biomarker for tremor-dominant Parkinson's disease.

PubMed

He, Naying; Huang, Pei; Ling, Huawei; Langley, Jason; Liu, Chunlei; Ding, Bei; Huang, Juan; Xu, Hongmin; Zhang, Yong; Zhang, Zhongping; Hu, Xiaoping; Chen, Shengdi; Yan, Fuhua

2017-04-01

Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder with variable clinicopathologic phenotypes and underlying neuropathologic mechanisms. Each clinical phenotype has a unique set of motor symptoms. Tremor is the most frequent initial motor symptom of PD and is the most difficult symptom to treat. The dentate nucleus (DN) is a deep iron-rich nucleus in the cerebellum and may be involved in PD tremor. In this study, we test the hypothesis that DN iron may be elevated in tremor-dominant PD patients using quantitative susceptibility mapping. Forty-three patients with PD [19 tremor dominant (TD)/24 akinetic rigidity (AR) dominant] and 48 healthy gender- and age-matched controls were recruited. Multi-echo gradient echo data were collected for each subject on a 3.0-T MR system. Inter-group susceptibility differences in the bilateral DN were investigated and correlations of clinical features with susceptibility were also examined. In contrast with the AR-dominant group, the TD group was found to have increased susceptibility in the bilateral DN when compared with healthy controls. In addition, susceptibility was positively correlated with tremor score in drug-naive PD patients. These findings indicate that iron load within the DN may make an important contribution to motor phenotypes in PD. Moreover, our results suggest that TD and AR-dominant phenotypes of PD can be differentiated on the basis of the susceptibility of the DN, at least at the group level. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

NASA Astrophysics Data System (ADS)

Pham, Binh Thai; Tien Bui, Dieu; Pourghasemi, Hamid Reza; Indra, Prakash; Dholakia, M. B.

2017-04-01

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.

Genome-wide linkage meta-analysis identifies susceptibility loci at 2q34 and 13q31.3 for genetic generalized epilepsies.

PubMed

Leu, Costin; de Kovel, Carolien G F; Zara, Federico; Striano, Pasquale; Pezzella, Marianna; Robbiano, Angela; Bianchi, Amedeo; Bisulli, Francesca; Coppola, Antonietta; Giallonardo, Anna Teresa; Beccaria, Francesca; Trenité, Dorothée Kasteleijn-Nolst; Lindhout, Dick; Gaus, Verena; Schmitz, Bettina; Janz, Dieter; Weber, Yvonne G; Becker, Felicitas; Lerche, Holger; Kleefuss-Lie, Ailing A; Hallman, Kerstin; Kunz, Wolfram S; Elger, Christian E; Muhle, Hiltrud; Stephani, Ulrich; Møller, Rikke S; Hjalgrim, Helle; Mullen, Saul; Scheffer, Ingrid E; Berkovic, Samuel F; Everett, Kate V; Gardiner, Mark R; Marini, Carla; Guerrini, Renzo; Lehesjoki, Anna-Elina; Siren, Auli; Nabbout, Rima; Baulac, Stephanie; Leguern, Eric; Serratosa, Jose M; Rosenow, Felix; Feucht, Martha; Unterberger, Iris; Covanis, Athanasios; Suls, Arvid; Weckhuysen, Sarah; Kaneva, Radka; Caglayan, Hande; Turkdogan, Dilsad; Baykan, Betul; Bebek, Nerses; Ozbek, Ugur; Hempelmann, Anne; Schulz, Herbert; Rüschendorf, Franz; Trucks, Holger; Nürnberg, Peter; Avanzini, Giuliano; Koeleman, Bobby P C; Sander, Thomas

2012-02-01

Genetic generalized epilepsies (GGEs) have a lifetime prevalence of 0.3% with heritability estimates of 80%. A considerable proportion of families with siblings affected by GGEs presumably display an oligogenic inheritance. The present genome-wide linkage meta-analysis aimed to map: (1) susceptibility loci shared by a broad spectrum of GGEs, and (2) seizure type-related genetic factors preferentially predisposing to either typical absence or myoclonic seizures, respectively. Meta-analysis of three genome-wide linkage datasets was carried out in 379 GGE-multiplex families of European ancestry including 982 relatives with GGEs. To dissect out seizure type-related susceptibility genes, two family subgroups were stratified comprising 235 families with predominantly genetic absence epilepsies (GAEs) and 118 families with an aggregation of juvenile myoclonic epilepsy (JME). To map shared and seizure type-related susceptibility loci, both nonparametric loci (NPL) and parametric linkage analyses were performed for a broad trait model (GGEs) in the entire set of GGE-multiplex families and a narrow trait model (typical absence or myoclonic seizures) in the subgroups of JME and GAE families. For the entire set of 379 GGE-multiplex families, linkage analysis revealed six loci achieving suggestive evidence for linkage at 1p36.22, 3p14.2, 5q34, 13q12.12, 13q31.3, and 19q13.42. The linkage finding at 5q34 was consistently supported by both NPL and parametric linkage results across all three family groups. A genome-wide significant nonparametric logarithm of odds score of 3.43 was obtained at 2q34 in 118 JME families. Significant parametric linkage to 13q31.3 was found in 235 GAE families assuming recessive inheritance (heterogeneity logarithm of odds = 5.02). Our linkage results support an oligogenic predisposition of familial GGE syndromes. The genetic risk factor at 5q34 confers risk to a broad spectrum of familial GGE syndromes, whereas susceptibility loci at 2q34 and 13q31.3 preferentially predispose to myoclonic seizures or absence seizures, respectively. Phenotype- genotype strategies applying narrow trait definitions in phenotypic homogeneous subgroups of families improve the prospects of disentangling the genetic basis of common familial GGE syndromes. Wiley Periodicals, Inc. © 2012 International League Against Epilepsy.

Flood susceptibility analysis through remote sensing, GIS and frequency ratio model

NASA Astrophysics Data System (ADS)

Samanta, Sailesh; Pal, Dilip Kumar; Palsamanta, Babita

2018-05-01

Papua New Guinea (PNG) is saddled with frequent natural disasters like earthquake, volcanic eruption, landslide, drought, flood etc. Flood, as a hydrological disaster to humankind's niche brings about a powerful and often sudden, pernicious change in the surface distribution of water on land, while the benevolence of flood manifests in restoring the health of the thalweg from excessive siltation by redistributing the fertile sediments on the riverine floodplains. In respect to social, economic and environmental perspective, flood is one of the most devastating disasters in PNG. This research was conducted to investigate the usefulness of remote sensing, geographic information system and the frequency ratio (FR) for flood susceptibility mapping. FR model was used to handle different independent variables via weighted-based bivariate probability values to generate a plausible flood susceptibility map. This study was conducted in the Markham riverine precinct under Morobe province in PNG. A historical flood inventory database of PNG resource information system (PNGRIS) was used to generate 143 flood locations based on "create fishnet" analysis. 100 (70%) flood sample locations were selected randomly for model building. Ten independent variables, namely land use/land cover, elevation, slope, topographic wetness index, surface runoff, landform, lithology, distance from the main river, soil texture and soil drainage were used into the FR model for flood vulnerability analysis. Finally, the database was developed for areas vulnerable to flood. The result demonstrated a span of FR values ranging from 2.66 (least flood prone) to 19.02 (most flood prone) for the study area. The developed database was reclassified into five (5) flood vulnerability zones segmenting on the FR values, namely very low (less that 5.0), low (5.0-7.5), moderate (7.5-10.0), high (10.0-12.5) and very high susceptibility (more than 12.5). The result indicated that about 19.4% land area as `very high' and 35.8% as `high' flood vulnerable class. The FR model output was validated with remaining 43 (30%) flood points, where 42 points were marked as correct predictions which evinced an accuracy of 97.7% in prediction. A total of 137292 people are living in those vulnerable zones. The flood susceptibility analysis using this model will be very useful and also an efficient tool to the local government administrators, researchers and planners for devising flood mitigation plans.

Population differences in platinum toxicity as a means to identify novel genetic susceptibility variants

PubMed Central

O'Donnell, Peter H.; Gamazon, Eric; Zhang, Wei; Stark, Amy L.; Kistner-Griffin, Emily O.; Huang, R. Stephanie; Dolan, M. Eileen

2010-01-01

Objectives Clinical studies show that Asians (ASN) are more susceptible to toxicities associated with platinum-containing regimens. We hypothesized that studying ASN as an `enriched phenotype' population could enable the discovery of novel genetic determinants of platinum susceptibility. Methods Using well-genotyped lymphoblastoid cell lines from the HapMap, we determined cisplatin and carboplatin cytotoxicity phenotypes (IC50s) for ASN, Caucasians (CEU), and Africans (YRI). IC50s were used in genome-wide association studies. Results ASN were most sensitive to platinums, corroborating clinical findings. ASN genome-wide association studies produced 479 single-nucleotide polymorphisms (SNPs) associating with cisplatin susceptibility and 199 with carboplatin susceptibility (P<10−4). Considering only the most significant variants (P< 9.99 × 10−6), backwards elimination was then used to identify reduced-model SNPs, which robustly described the drug phenotypes within ASN. These SNPs comprised highly descriptive genetic signatures of susceptibility, with 12 SNPs explaining more than 95% of the susceptibility phenotype variation for cisplatin, and eight SNPs approximately 75% for carboplatin. To determine the possible function of these variants in ASN, the SNPs were tested for association with differential expression of target genes. SNPs were highly associated with the expression of multiple target genes, and notably, the histone H3 family was implicated for both drugs, suggesting a platinum-class mechanism. Histone H3 has repeatedly been described as regulating the formation of platinum-DNA adducts, but this is the first evidence that specific genetic variants might mediate these interactions in a pharmacogenetic manner. Finally, to determine whether any ASN-identified SNPs might also be important in other human populations, we interrogated all 479/199 SNPs for association with platinum susceptibility in an independent combined CEU/YRI population. Three unique SNPs for cisplatin and 10 for carboplatin replicated in CEU/YRI. Conclusion Enriched `platinum susceptible' populations can be used to discover novel genetic determinants governing interindividual platinum chemotherapy susceptibility. PMID:20393316

QTL mapping of downy and powdery mildew resistances in PI 197088 cucumber with genotyping-by-sequencing in RIL population.

PubMed

Wang, Yuhui; VandenLangenberg, Kyle; Wen, Changlong; Wehner, Todd C; Weng, Yiqun

2018-03-01

Host resistances in PI 197088 cucumber to downy and powdery mildew pathogens are conferred by 11 (3 with major effect) and 4 (1 major effect) QTL, respectively, and three of which are co-localized. The downy mildew (DM) and powdery mildew (PM) are the two most important foliar diseases of cucurbit crops worldwide. The cucumber accession PI 197088 exhibits high-level resistances to both pathogens. Here, we reported QTL mapping results for DM and PM resistances with 148 recombinant inbred lines from a cross between PI 197088 and the susceptible line 'Coolgreen'. Phenotypic data on responses to natural DM and PM infection were collected in multi-year and multi-location replicated field trials. A high-density genetic map with 2780 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing and 55 microsatellite markers was developed, which revealed genomic regions with segregation distortion and mis-assemblies in the '9930' cucumber draft genome. QTL analysis identified 11 and 4 QTL for DM and PM resistances accounting for more than 73.5 and 63.0% total phenotypic variance, respectively. Among the 11 DM resistance QTL, dm5.1, dm5.2, and dm5.3 were major-effect contributing QTL, whereas dm1.1, dm2.1, and dm6.2 conferred susceptibility. Of the 4 QTL for PM resistance, pm5.1 was the major-effect QTL explaining 32.4% phenotypic variance and the minor-effect QTL pm6.1 contributed to disease susceptibility. Three PM QTL, pm2.1, pm5.1, and pm6.1, were co-localized with DM QTL dm2.1, dm5.2, and dm6.1, respectively, which was consistent with the observed linkage of PM and DM resistances in PI 197088. The genetic architecture of DM resistance in PI 197088 and another resistant line WI7120 (PI 330628) was compared, and the potential of using PI 197088 in cucumber breeding for downy and powdery mildew resistances is discussed.

A comparison of phase imaging and quantitative susceptibility mapping in the imaging of multiple sclerosis lesions at ultrahigh field.

PubMed

Cronin, Matthew John; Wharton, Samuel; Al-Radaideh, Ali; Constantinescu, Cris; Evangelou, Nikos; Bowtell, Richard; Gowland, Penny Anne

2016-06-01

The aim of this study was to compare the use of high-resolution phase and QSM images acquired at ultra-high field in the investigation of multiple sclerosis (MS) lesions with peripheral rings, and to discuss their usefulness for drawing inferences about underlying tissue composition. Thirty-nine Subjects were scanned at 7 T, using 3D T 2*-weighted and T 1-weighted sequences. Phase images were then unwrapped and filtered, and quantitative susceptibility maps were generated using a thresholded k-space division method. Lesions were compared visually and using a 1D profiling algorithm. Lesions displaying peripheral rings in the phase images were identified in 10 of the 39 subjects. Dipolar projections were apparent in the phase images outside of the extent of several of these lesions; however, QSM images showed peripheral rings without such projections. These projections appeared ring-like in a small number of phase images where no ring was observed in QSM. 1D profiles of six well-isolated example lesions showed that QSM contrast corresponds more closely to the magnitude images than phase contrast. Phase images contain dipolar projections, which confounds their use in the investigation of tissue composition in MS lesions. Quantitative susceptibility maps correct these projections, providing insight into the composition of MS lesions showing peripheral rings.

Aphid Resistance in Medicago truncatula Involves Antixenosis and Phloem-Specific, Inducible Antibiosis, and Maps to a Single Locus Flanked by NBS-LRR Resistance Gene Analogs1

PubMed Central

Klingler, John; Creasy, Robert; Gao, Lingling; Nair, Ramakrishnan M.; Calix, Alonso Suazo; Jacob, Helen Spafford; Edwards, Owain R.; Singh, Karam B.

2005-01-01

Aphids and related insects feed from a single cell type in plants: the phloem sieve element. Genetic resistance to Acyrthosiphon kondoi Shinji (bluegreen aphid or blue alfalfa aphid) has been identified in Medicago truncatula Gaert. (barrel medic) and backcrossed into susceptible cultivars. The status of M. truncatula as a model legume allows an in-depth study of defense against this aphid at physiological, biochemical, and molecular levels. In this study, two closely related resistant and susceptible genotypes were used to characterize the aphid-resistance phenotype. Resistance conditions antixenosis since migratory aphids were deterred from settling on resistant plants within 6 h of release, preferring to settle on susceptible plants. Analysis of feeding behavior revealed the trait affects A. kondoi at the level of the phloem sieve element. Aphid reproduction on excised shoots demonstrated that resistance requires an intact plant. Antibiosis against A. kondoi is enhanced by prior infestation, indicating induction of this phloem-specific defense. Resistance segregates as a single dominant gene, AKR (Acyrthosiphon kondoi resistance), in two mapping populations, which have been used to map the locus to a region flanked by resistance gene analogs predicted to encode the CC-NBS-LRR subfamily of resistance proteins. This work provides the basis for future molecular analysis of defense against phloem parasitism in a plant model system. PMID:15778464

Identification of loci associated with susceptibility to mycobacterium avium subspecies paratuberculosis (Map) tissue infection in cattle

USDA-ARS?s Scientific Manuscript database

Johne’s disease is a contagious bacterial infection of cattle caused by Mycobacterium avium ssp. paratuberculosis (Map). A previous genome-wide association analysis (GWAA) in Holstein cattle identified QTL on BTA3 and BTA9 that were highly associated (P < 5 × 10-7) and on BTA1, BTA16, and BTA21 that...

Marker development, saturation mapping, and high-resolution mapping of the Septoria nodorum blotch susceptibility gene Snn3-B1 in wheat

USDA-ARS?s Scientific Manuscript database

Septoria nodorum blotch (SNB), caused by Parastagonospora nodorum, is a severe foliar and glume disease on durum and common wheat. Pathogen-produced necrotrophic effectors (NEs) are the major determinants for SNB on leaves. One such NE is SnTox3, which evokes programmed cell death and leads to dis...

Disease mapping based on stochastic SIR-SI model for Dengue and Chikungunya in Malaysia

NASA Astrophysics Data System (ADS)

Samat, N. A.; Ma'arof, S. H. Mohd Imam

2014-12-01

This paper describes and demonstrates a method for relative risk estimation which is based on the stochastic SIR-SI vector-borne infectious disease transmission model specifically for Dengue and Chikungunya diseases in Malaysia. Firstly, the common compartmental model for vector-borne infectious disease transmission called the SIR-SI model (susceptible-infective-recovered for human populations; susceptible-infective for vector populations) is presented. This is followed by the explanations on the stochastic SIR-SI model which involve the Bayesian description. This stochastic model then is used in the relative risk formulation in order to obtain the posterior relative risk estimation. Then, this relative estimation model is demonstrated using Dengue and Chikungunya data of Malaysia. The viruses of these diseases are transmitted by the same type of female vector mosquito named Aedes Aegypti and Aedes Albopictus. Finally, the findings of the analysis of relative risk estimation for both Dengue and Chikungunya diseases are presented, compared and displayed in graphs and maps. The distribution from risk maps show the high and low risk area of Dengue and Chikungunya diseases occurrence. This map can be used as a tool for the prevention and control strategies for both diseases.

Disease mapping based on stochastic SIR-SI model for Dengue and Chikungunya in Malaysia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Samat, N. A.; Ma'arof, S. H. Mohd Imam

This paper describes and demonstrates a method for relative risk estimation which is based on the stochastic SIR-SI vector-borne infectious disease transmission model specifically for Dengue and Chikungunya diseases in Malaysia. Firstly, the common compartmental model for vector-borne infectious disease transmission called the SIR-SI model (susceptible-infective-recovered for human populations; susceptible-infective for vector populations) is presented. This is followed by the explanations on the stochastic SIR-SI model which involve the Bayesian description. This stochastic model then is used in the relative risk formulation in order to obtain the posterior relative risk estimation. Then, this relative estimation model is demonstrated using Denguemore » and Chikungunya data of Malaysia. The viruses of these diseases are transmitted by the same type of female vector mosquito named Aedes Aegypti and Aedes Albopictus. Finally, the findings of the analysis of relative risk estimation for both Dengue and Chikungunya diseases are presented, compared and displayed in graphs and maps. The distribution from risk maps show the high and low risk area of Dengue and Chikungunya diseases occurrence. This map can be used as a tool for the prevention and control strategies for both diseases.« less

Farmers' awareness on landslide susceptibility on their plots: a first step towards household resilience in the Rwenzori region, Western Uganda

NASA Astrophysics Data System (ADS)

Mertens, Kewan; Jacobs, Lies; Maes, Jan; Kervyn, Matthieu; Vranken, Liesbet

2016-04-01

In the mountainous area of the Rwenzori region, western Uganda, landslides frequently destroy houses and plots of farmers living and cultivating on unstable slopes. The impact of these landslides on the local livelihoods depends on the exposure and the resilience of the households. Both the exposure and the resilience can be modified to a certain extent with specific measures, e.g. planting slope stabilizing trees of paying for (informal) insurance. The adoption of such measures and the willingness to accept measures imposed by local governments crucially depends on the local awareness of landslide risk. The aim of this research is to estimate awareness on landslide susceptibility, as a proxy for landslide risk, among household heads in a landslide prone area in the Rwenzori region, Western Uganda. The objective is to compare household and plot characteristics between aware and unaware households. This will allow us to identify those households which are less aware of landslide susceptibility and therefore most likely to be less resilient when exposed to landslide risk. We use data from a susceptibility map constructed in 2016 and a structured household survey conducted in the Rwenzori region in 2015. The susceptibility map is based on a SRTM 30m DEM and validated with field observations, while the household survey includes the answers of more than 450 households that have been asked to evaluate the landslide susceptibility on their plots. Simple probit models at plot level are used to compare the estimated landslide susceptibility with the modelled susceptibility. We use this comparison to identify the household characteristics of those households that do not correctly estimate the landslide susceptibility on their plots. We will exploit the fact that landslide susceptibility is very space specific and that households can therefore have plots in both susceptible and unsusceptible areas. The research is currently ongoing, but we hypothesize that younger farmers with a lower education level, lower trust, social capital and networks and with a recent migration history are less able to estimate landslide susceptibility on their plots. Literature on other disasters has demonstrated that human capital, social networks and past experience are crucial factors in determining risk perception. To our knowledge this is the first study to specifically investigate landslide risk awareness in a developing country, integrating both detailed socio-economic and geographical data. While estimating the awareness of landslide susceptibility is not sufficient to come to an estimation of a household's coping capacity, we consider it to be a first and necessary step towards a full estimation of household resilience.

Genetic-based prediction of disease traits: prediction is very difficult, especially about the future†

PubMed Central

Schrodi, Steven J.; Mukherjee, Shubhabrata; Shan, Ying; Tromp, Gerard; Sninsky, John J.; Callear, Amy P.; Carter, Tonia C.; Ye, Zhan; Haines, Jonathan L.; Brilliant, Murray H.; Crane, Paul K.; Smelser, Diane T.; Elston, Robert C.; Weeks, Daniel E.

2014-01-01

Translation of results from genetic findings to inform medical practice is a highly anticipated goal of human genetics. The aim of this paper is to review and discuss the role of genetics in medically-relevant prediction. Germline genetics presages disease onset and therefore can contribute prognostic signals that augment laboratory tests and clinical features. As such, the impact of genetic-based predictive models on clinical decisions and therapy choice could be profound. However, given that (i) medical traits result from a complex interplay between genetic and environmental factors, (ii) the underlying genetic architectures for susceptibility to common diseases are not well-understood, and (iii) replicable susceptibility alleles, in combination, account for only a moderate amount of disease heritability, there are substantial challenges to constructing and implementing genetic risk prediction models with high utility. In spite of these challenges, concerted progress has continued in this area with an ongoing accumulation of studies that identify disease predisposing genotypes. Several statistical approaches with the aim of predicting disease have been published. Here we summarize the current state of disease susceptibility mapping and pharmacogenetics efforts for risk prediction, describe methods used to construct and evaluate genetic-based predictive models, and discuss applications. PMID:24917882

Rainfall-induced Landslide Susceptibility assessment at the Longnan county

NASA Astrophysics Data System (ADS)

Hong, Haoyuan; Zhang, Ying

2017-04-01

Landslides are a serious disaster in Longnan county, China. Therefore landslide susceptibility assessment is useful tool for government or decision making. The main objective of this study is to investigate and compare the frequency ratio, support vector machines, and logistic regression. The Longnan county (Jiangxi province, China) was selected as the case study. First, the landslide inventory map with 354 landslide locations was constructed. Then landslide locations were then randomly divided into a ratio of 70/30 for the training and validating the models. Second, fourteen landslide conditioning factors were prepared such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, lithology, distance to faults, distance to rivers, distance to roads, land use, normalized difference vegetation index (NDVI), and rainfall. Using the frequency ratio, support vector machines, and logistic regression, a total of three landslide susceptibility models were constructed. Finally, the overall performance of the resulting models was assessed and compared using the Receiver operating characteristic (ROC) curve technique. The result showed that the support vector machines model is the best model in the study area. The success rate is 88.39 %; and prediction rate is 84.06 %.

Mapping Chaparral in the Santa Monica Mountains Using Multiple Spectral Mixture Models

NASA Technical Reports Server (NTRS)

Green Robert O.; Roberts, D. A.; Gardner, M.; Church, R.; Ustin, S.; Scheer, G.

1996-01-01

California chaparral is one of the most important natural vegetation communities in Southern California, representing a significant source of species diversity and, through a high susceptibility to fire, playing a major role in ecosystem dynamics. Due to steep topographic gradients, harsh edaphic conditions and variable fire histories, chaparral typically forms a complex mosaic of different species dominants and age classes, each with unique successional responses to fire and canopy characteristics (e.g. moisture content, biomass, fuel load) that modify fire susceptibility. The high human cost of fire and intimate mixing along the urban interface combine to modify the natural fire regime as well as provide additional impetus for a better understanding of how to predict fire and its management. Management problems have been further magnified by nearly seventy years of fire suppression and drought related die-back over the last few years resulting in a large accumulation of highly combustible fuels. Chaparral communities in the Santa Monica Mountains exemplify many of the management challenges associated with fire and biodiversity. A study was initiated in the Santa Monica Mountains to investigate the use of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for providing improved maps of chaparral coupled with direct estimates of canopy attributes (e.g. biomass, leaf area, fuel load). The Santa Monica Mountains are an east-west trending range located approximately 75 kilometers north of Los Angeles extending westward into Ventura County. Within the Santa Monica Mountains a diverse number of ecosystems are located, including four distinct types of chaparral, wetlands, riparian habitats, woodlands, and coastal sage scrub. In this study we focus on mapping three types of chaparral, oak woodlands and grasslands. Chaparral mapped included coastal sage scrub, chamise chaparral and mixed chaparral that consisted predominantly of two species of Ceanothus.

Debris flow susceptibility mapping using a qualitative heuristic method and Flow-R along the Yukon Alaska Highway Corridor, Canada

NASA Astrophysics Data System (ADS)

Blais-Stevens, A.; Behnia, P.

2016-02-01

This research activity aimed at reducing risk to infrastructure, such as a proposed pipeline route roughly parallel to the Yukon Alaska Highway Corridor (YAHC), by filling geoscience knowledge gaps in geohazards. Hence, the Geological Survey of Canada compiled an inventory of landslides including debris flow deposits, which were subsequently used to validate two different debris flow susceptibility models. A qualitative heuristic debris flow susceptibility model was produced for the northern region of the YAHC, from Kluane Lake to the Alaska border, by integrating data layers with assigned weights and class ratings. These were slope angle, slope aspect, surficial geology, plan curvature, and proximity to drainage system. Validation of the model was carried out by calculating a success rate curve which revealed a good correlation with the susceptibility model and the debris flow deposit inventory compiled from air photos, high-resolution satellite imagery, and field verification. In addition, the quantitative Flow-R method was tested in order to define the potential source and debris flow susceptibility for the southern region of Kluane Lake, an area where documented debris flow events have blocked the highway in the past (e.g. 1988). Trial and error calculations were required for this method because there was not detailed information on the debris flows for the YAHC to allow us to define threshold values for some parameters when calculating source areas, spreading, and runout distance. Nevertheless, correlation with known documented events helped define these parameters and produce a map that captures most of the known events and displays debris flow susceptibility in other, usually smaller, steep channels that had not been previously documented.

Debris flow susceptibility mapping using a qualitative heuristic method and Flow-R along the Yukon Alaska Highway Corridor, Canada

NASA Astrophysics Data System (ADS)

Blais-Stevens, A.; Behnia, P.

2015-05-01

This research activity aimed at reducing risk to infrastructure, such as a proposed pipeline route roughly parallel to the Yukon Alaska Highway Corridor (YAHC) by filling geoscience knowledge gaps in geohazards. Hence, the Geological Survey of Canada compiled an inventory of landslides including debris flow deposits, which were subsequently used to validate two different debris flow susceptibility models. A qualitative heuristic debris flow susceptibility model was produced for the northern region of the YAHC, from Kluane Lake to the Alaska border, by integrating data layers with assigned weights and class ratings. These were slope angle, slope aspect (derived from a 5 m × 5 m DEM), surficial geology, permafrost distribution, and proximity to drainage system. Validation of the model was carried out by calculating a success rate curve which revealed a good correlation with the susceptibility model and the debris flow deposit inventory compiled from air photos, high resolution satellite imagery, and field verification. In addition, the quantitative Flow-R method was tested in order to define the potential source and debris flow susceptibility for the southern region of Kluane Lake, an area where documented debris flow events have blocked the highway in the past (e.g., 1988). Trial and error calculations were required for this method because there was not detailed information on the debris flows for the YAHC to allow us to define threshold values for some parameters when calculating source areas, spreading, and runout distance. Nevertheless, correlation with known documented events helped define these parameters and produce a map that captures most of the known events and displays debris flow susceptibility in other, usually smaller, steep channels that had not been previously documented.

Common subtypes of idiopathic generalized epilepsies: Lack of linkage to D20S19 close to candidate loci (EBN1, EEGV1) on chromosome 20

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sander, T.; Schmitz, B.; Janz, D.

1996-02-16

Hereditary factors play a major role in the etiology of idiopathic generalized epilepsies (IGEs). A trait locus (EBN1) for a rare subtype of IGEs, the benign neonatal familial convulsions, and a susceptibility gene (EEGV1) for the common human low-voltage electroencephalogram have been mapped close together with D20S19 to the chromosomal region 20q13.2. Both loci are potential candidates for the susceptibility to IGE spectra with age-related onset beyond the neonatal period. The present study tested the hypothesis that a putative susceptibility locus linked to D20S19 predisposes to spectra of IGEs with age-related onset from childhood to adolescence. Linkage analyses were conductedmore » in 60 families ascertained through IGE patients with juvenile myoclonic epilepsy, juvenile absence epilepsy or childhood absence epilepsy. Our results provide evidence against linkage of a putative susceptibility gene for four hierarchically broadened IGE spectra with D20S19 assuming tentative single-locus genetic models. The extent of an {open_quotes}exclusion region{close_quotes} (lod scores below -2) varied from 0.5 cM up to 22 cM on either side of D2OSl9 depending on the trait assumed. These results are contrary to the expectation that a susceptibility gene in vicinity to D20S19 confers a common major gene effect to the expression of IGE spectra with age-related onset from childhood to adolescence. 50 refs., 1 fig., 1 tab.« less

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

PubMed

Khosravi, Khabat; Pham, Binh Thai; Chapi, Kamran; Shirzadi, Ataollah; Shahabi, Himan; Revhaug, Inge; Prakash, Indra; Tien Bui, Dieu

2018-06-15

Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas. Copyright © 2018 Elsevier B.V. All rights reserved.

Landslide-susceptibility analysis using light detection and ranging-derived digital elevation models and logistic regression models: a case study in Mizunami City, Japan

NASA Astrophysics Data System (ADS)

Wang, Liang-Jie; Sawada, Kazuhide; Moriguchi, Shuji

2013-01-01

To mitigate the damage caused by landslide disasters, different mathematical models have been applied to predict landslide spatial distribution characteristics. Although some researchers have achieved excellent results around the world, few studies take the spatial resolution of the database into account. Four types of digital elevation model (DEM) ranging from 2 to 20 m derived from light detection and ranging technology to analyze landslide susceptibility in Mizunami City, Gifu Prefecture, Japan, are presented. Fifteen landslide-causative factors are considered using a logistic-regression approach to create models for landslide potential analysis. Pre-existing landslide bodies are used to evaluate the performance of the four models. The results revealed that the 20-m model had the highest classification accuracy (71.9%), whereas the 2-m model had the lowest value (68.7%). In the 2-m model, 89.4% of the landslide bodies fit in the medium to very high categories. For the 20-m model, only 83.3% of the landslide bodies were concentrated in the medium to very high classes. When the cell size decreases from 20 to 2 m, the area under the relative operative characteristic increases from 0.68 to 0.77. Therefore, higher-resolution DEMs would provide better results for landslide-susceptibility mapping.

3D and 4D magnetic susceptibility tomography based on complex MR images

DOEpatents

Chen, Zikuan; Calhoun, Vince D

2014-11-11

Magnetic susceptibility is the physical property for T2*-weighted magnetic resonance imaging (T2*MRI). The invention relates to methods for reconstructing an internal distribution (3D map) of magnetic susceptibility values, .chi. (x,y,z), of an object, from 3D T2*MRI phase images, by using Computed Inverse Magnetic Resonance Imaging (CIMRI) tomography. The CIMRI technique solves the inverse problem of the 3D convolution by executing a 3D Total Variation (TV) regularized iterative convolution scheme, using a split Bregman iteration algorithm. The reconstruction of .chi. (x,y,z) can be designed for low-pass, band-pass, and high-pass features by using a convolution kernel that is modified from the standard dipole kernel. Multiple reconstructions can be implemented in parallel, and averaging the reconstructions can suppress noise. 4D dynamic magnetic susceptibility tomography can be implemented by reconstructing a 3D susceptibility volume from a 3D phase volume by performing 3D CIMRI magnetic susceptibility tomography at each snapshot time.

Iron in Multiple Sclerosis and Its Noninvasive Imaging with Quantitative Susceptibility Mapping

PubMed Central

Stüber, Carsten; Pitt, David; Wang, Yi

2016-01-01

Iron is considered to play a key role in the development and progression of Multiple Sclerosis (MS). In particular, iron that accumulates in myeloid cells after the blood-brain barrier (BBB) seals may contribute to chronic inflammation, oxidative stress and eventually neurodegeneration. Magnetic resonance imaging (MRI) is a well-established tool for the non-invasive study of MS. In recent years, an advanced MRI method, quantitative susceptibility mapping (QSM), has made it possible to study brain iron through in vivo imaging. Moreover, immunohistochemical investigations have helped defining the lesional and cellular distribution of iron in MS brain tissue. Imaging studies in MS patients and of brain tissue combined with histological studies have provided important insights into the role of iron in inflammation and neurodegeneration in MS. PMID:26784172

Landslide inventory maps: New tools for an old problem

NASA Astrophysics Data System (ADS)

Guzzetti, Fausto; Mondini, Alessandro Cesare; Cardinali, Mauro; Fiorucci, Federica; Santangelo, Michele; Chang, Kang-Tsung

2012-04-01

Landslides are present in all continents, and play an important role in the evolution of landscapes. They also represent a serious hazard in many areas of the world. Despite their importance, we estimate that landslide maps cover less than 1% of the slopes in the landmasses, and systematic information on the type, abundance, and distribution of landslides is lacking. Preparing landslide maps is important to document the extent of landslide phenomena in a region, to investigate the distribution, types, pattern, recurrence and statistics of slope failures, to determine landslide susceptibility, hazard, vulnerability and risk, and to study the evolution of landscapes dominated by mass-wasting processes. Conventional methods for the production of landslide maps rely chiefly on the visual interpretation of stereoscopic aerial photography, aided by field surveys. These methods are time consuming and resource intensive. New and emerging techniques based on satellite, airborne, and terrestrial remote sensing technologies, promise to facilitate the production of landslide maps, reducing the time and resources required for their compilation and systematic update. In this work, we first outline the principles for landslide mapping, and we review the conventional methods for the preparation of landslide maps, including geomorphological, event, seasonal, and multi-temporal inventories. Next, we examine recent and new technologies for landslide mapping, considering (i) the exploitation of very-high resolution digital elevation models to analyze surface morphology, (ii) the visual interpretation and semi-automatic analysis of different types of satellite images, including panchromatic, multispectral, and synthetic aperture radar images, and (iii) tools that facilitate landslide field mapping. Next, we discuss the advantages and the limitations of the new remote sensing data and technology for the production of geomorphological, event, seasonal, and multi-temporal inventory maps. We conclude by arguing that the new tools will help to improve the quality of landslide maps, with positive effects on all derivative products and analyses, including erosion studies and landscape modeling, susceptibility and hazard assessments, and risk evaluations.

Holocene evolution of Apalachicola Bay, Florida

USGS Publications Warehouse

Osterman, Lisa E.; Twichell, David C.

2011-01-01

A program of geophysical mapping and vibracoring was conducted in 2007 to better understand the geologic evolution of Apalachicola Bay and its response to sea-level rise. A detailed geologic history could help better understand how this bay may respond to both short-term (for example, storm surge) and long-term sea-level rise. The results of this study were published (Osterman and others, 2009) as part of a special issue of Geo-Marine Letters that documents early results from the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility Project.

Assessing Landslide Mobility Using GIS: Application to Kosrae, Micronesia

NASA Astrophysics Data System (ADS)

Reid, M. E.; Brien, D. L.; Godt, J.; Schmitt, R. G.; Harp, E. L.

2015-12-01

Deadly landslides are often mobile landslides, as exemplified by the disastrous landslide that occurred near Oso, Washington in 2014 killing 43. Despite this association, many landslide susceptibility maps do not identify runout areas. We developed a simple, GIS-based method for identifying areas potentially overrun by mobile slides and debris flows. Our method links three processes within a DEM landscape: landslide initiation, transport, and debris-flow inundation (from very mobile slides). Given spatially distributed shear strengths, we first identify initiation areas using an infinite-slope stability analysis. We then delineate transport zones, or regions of potential entrainment and/or deposition, using a height/length runout envelope. Finally, where these transport zones intersect the channel network, we start debris-flow inundation zones. The extent of inundation is computed using the USGS model Laharz, modified to include many debris-flow locations throughout a DEM. Potential debris-flow volumes are computed from upslope initiation areas and typical slide thicknesses. We applied this approach to the main island of Kosrae State, Federated States of Micronesia (FSM). In 2002, typhoon Chata'an triggered numerous landslides on the neighboring islands of Chuuk State, FSM, resulting in 43 fatalities. Using an infinite-slope stability model calibrated to the Chuuk event, we identified potential landslide initiation areas on Kosrae. We then delineated potential transport zones using a 20º runout envelope, based on runout observations from Chuuk. Potential debris-flow inundation zones were then determined using Laharz. Field inspections on Kosrae revealed that our resulting susceptibility map correctly classified areas covered by previous debris-flow deposits and did not include areas covered by fluvial deposits. Our map has the advantage of providing a visual tool to portray initiation, transport, and runout zones from mobile landslides.

Flash-flood potential assessment and mapping by integrating the weights-of-evidence and frequency ratio statistical methods in GIS environment - case study: Bâsca Chiojdului River catchment (Romania)

NASA Astrophysics Data System (ADS)

Costache, Romulus; Zaharia, Liliana

2017-06-01

Given the significant worldwide human and economic losses caused due to floods annually, reducing the negative consequences of these hazards is a major concern in development strategies at different spatial scales. A basic step in flood risk management is identifying areas susceptible to flood occurrences. This paper proposes a methodology allowing the identification of areas with high potential of accelerated surface run-off and consequently, of flash-flood occurrences. The methodology involves assessment and mapping in GIS environment of flash flood potential index (FFPI), by integrating two statistical methods: frequency ratio and weights-of-evidence. The methodology was applied for Bâsca Chiojdului River catchment (340 km2), located in the Carpathians Curvature region (Romania). Firstly, the areas with torrential phenomena were identified and the main factors controlling the surface run-off were selected (in this study nine geographical factors were considered). Based on the features of the considered factors, many classes were set for each of them. In the next step, the weights of each class/category of the considered factors were determined, by identifying their spatial relationships with the presence or absence of torrential phenomena. Finally, the weights for each class/category of geographical factors were summarized in GIS, resulting the FFPI values for each of the two statistical methods. These values were divided into five classes of intensity and were mapped. The final results were used to estimate the flash-flood potential and also to identify the most susceptible areas to this phenomenon. Thus, the high and very high values of FFPI characterize more than one-third of the study catchment. The result validation was performed by (i) quantifying the rate of the number of pixels corresponding to the torrential phenomena considered for the study (training area) and for the results' testing (validating area) and (ii) plotting the ROC (receiver operating characteristics) curve.

Cerebral Metabolic Rate of Oxygen (CMRO2 ) Mapping by Combining Quantitative Susceptibility Mapping (QSM) and Quantitative Blood Oxygenation Level-Dependent Imaging (qBOLD).

PubMed

Cho, Junghun; Kee, Youngwook; Spincemaille, Pascal; Nguyen, Thanh D; Zhang, Jingwei; Gupta, Ajay; Zhang, Shun; Wang, Yi

2018-03-07

To map the cerebral metabolic rate of oxygen (CMRO 2 ) by estimating the oxygen extraction fraction (OEF) from gradient echo imaging (GRE) using phase and magnitude of the GRE data. 3D multi-echo gradient echo imaging and perfusion imaging with arterial spin labeling were performed in 11 healthy subjects. CMRO 2 and OEF maps were reconstructed by joint quantitative susceptibility mapping (QSM) to process GRE phases and quantitative blood oxygen level-dependent (qBOLD) modeling to process GRE magnitudes. Comparisons with QSM and qBOLD alone were performed using ROI analysis, paired t-tests, and Bland-Altman plot. The average CMRO 2 value in cortical gray matter across subjects were 140.4 ± 14.9, 134.1 ± 12.5, and 184.6 ± 17.9 μmol/100 g/min, with corresponding OEFs of 30.9 ± 3.4%, 30.0 ± 1.8%, and 40.9 ± 2.4% for methods based on QSM, qBOLD, and QSM+qBOLD, respectively. QSM+qBOLD provided the highest CMRO 2 contrast between gray and white matter, more uniform OEF than QSM, and less noisy OEF than qBOLD. Quantitative CMRO 2 mapping that fits the entire complex GRE data is feasible by combining QSM analysis of phase and qBOLD analysis of magnitude. © 2018 International Society for Magnetic Resonance in Medicine.

Structural fire risk of Portugal

NASA Astrophysics Data System (ADS)

Parente, Joana; Pereira, Mário

2017-04-01

Portugal is on the top of the European countries most affected by vegetation fires which underlines the importance of the existence of an updated and coherent fire risk map. This map represent a valuable supporting tool for forest and fire management decisions, focus prevention activities, improve the efficiency of fire detection systems, manage resources and actions of fire fighting with greater effectiveness. Therefore this study proposed a structural fire risk map of the vegetated area of Portugal using a deterministic approach based on the concept of fire risk currently accepted by the scientific community which consists in the combination of the fire hazard and the potential economic damage. The existing fire susceptibility map for Portugal based on the slope, land cover and fire probability, was adopted and updated by the use of a higher resolution digital terrain model, longer burnt area perimeter dataset (1975 - 2013) and the entire set of Corine land cover inventories. Five susceptibility classes were mapped to be in accordance with the Portuguese law and the results confirms the good performance of this model not only in terms of the favourability scores but also in the predictive values. Considering three different scenarios of (maximum, mean, and minimum annual) burnt area, fire hazard were estimate. The vulnerability scores and monetary values of species defined in the literature and by law were used to calculate the potential economic damage. The result was a fire risk map that identifies the areas more prone to be affected by fires in the future and provides an estimate of the economic damage of the fire which will be a valuable tool for forest and fire managers and to minimize the economic and environmental consequences of vegetation fires in Portugal. Acknowledgements: This work was supported by: (i) the project Interact - Integrative Research in Environment,Agro-Chain and Technology, NORTE-01-0145-FEDER-000017, research line BEST, cofinanced by FEDER/NORTE 2020; (ii) the FIREXTR project, PTDC/ATP¬GEO/0462/2014; and, (iii) European Investment Funds by FEDER/COMPETE/POCI-Operacional Competitiveness and Internacionalization Programme, under Project POCI-01-0145-FEDER-006958 and National Funds by FCT - Portuguese Foundation for Science and Technology, under the project UID/AGR/04033. We are especially grateful to ICNF and ISA for providing the fire data.

Clinical high-resolution mapping of the proteoglycan-bound water fraction in articular cartilage of the human knee joint.

PubMed

Bouhrara, Mustapha; Reiter, David A; Sexton, Kyle W; Bergeron, Christopher M; Zukley, Linda M; Spencer, Richard G

2017-11-01

We applied our recently introduced Bayesian analytic method to achieve clinically-feasible in-vivo mapping of the proteoglycan water fraction (PgWF) of human knee cartilage with improved spatial resolution and stability as compared to existing methods. Multicomponent driven equilibrium single-pulse observation of T 1 and T 2 (mcDESPOT) datasets were acquired from the knees of two healthy young subjects and one older subject with previous knee injury. Each dataset was processed using Bayesian Monte Carlo (BMC) analysis incorporating a two-component tissue model. We assessed the performance and reproducibility of BMC and of the conventional analysis of stochastic region contraction (SRC) in the estimation of PgWF. Stability of the BMC analysis of PgWF was tested by comparing independent high-resolution (HR) datasets from each of the two young subjects. Unlike SRC, the BMC-derived maps from the two HR datasets were essentially identical. Furthermore, SRC maps showed substantial random variation in estimated PgWF, and mean values that differed from those obtained using BMC. In addition, PgWF maps derived from conventional low-resolution (LR) datasets exhibited partial volume and magnetic susceptibility effects. These artifacts were absent in HR PgWF images. Finally, our analysis showed regional variation in PgWF estimates, and substantially higher values in the younger subjects as compared to the older subject. BMC-mcDESPOT permits HR in-vivo mapping of PgWF in human knee cartilage in a clinically-feasible acquisition time. HR mapping reduces the impact of partial volume and magnetic susceptibility artifacts compared to LR mapping. Finally, BMC-mcDESPOT demonstrated excellent reproducibility in the determination of PgWF. Published by Elsevier Inc.

Cerebral metabolic rate of oxygen (CMRO2 ) mapping with hyperventilation challenge using quantitative susceptibility mapping (QSM).

PubMed

Zhang, Jingwei; Zhou, Dong; Nguyen, Thanh D; Spincemaille, Pascal; Gupta, Ajay; Wang, Yi

2017-05-01

Our objective was to demonstrate the feasibility of using hyperventilation as an efficient vasoconstrictive challenge and prior knowledge as denoising constraints for cerebral metabolic rate of oxygen (CMRO 2 ) mapping based upon quantitative susceptibility mapping (QSM). Three-dimensional (3D) multi-echo gradient echo and arterial spin labeling imaging were performed to calculate QSM and perfusion maps before and after a hyperventilation challenge in 11 healthy subjects. For comparison, this was repeated using a caffeine challenge. Whole-brain CMRO 2 and oxygen extraction fraction (OEF) maps were computed using constrained optimization. Hyperventilation scans were repeated to measure reproducibility. Regional agreement of CMRO 2 and OEF maps was analyzed within the cortical gray matter (CGM) using t-test and Bland-Altman plots. Hyperventilation challenge eliminates the 30-min waiting time needed for caffeine to exert its vasoconstrictive effects. Mean CMRO 2 (in µmol/100g/min) obtained in CGM using the caffeine and repeated hyperventilation scans were 149 ± 16, 153 ± 19, and 150 ± 20, respectively. This corresponded to an OEF of 33.6 ± 3.4%, 32.3 ± 3.2%, and 34.1 ± 3.8% at baseline state and 39.8 ± 4.8%, 43.6 ± 6.2%, and 42.8 ± 6.8% at challenged state, respectively. Hyperventilation scans produced a good agreement of CMRO 2 and OEF values. Hyperventilation is a feasible, reproducible, and efficient vasoconstrictive challenge for QSM-based quantitative CMRO 2 mapping. Magn Reson Med 77:1762-1773, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Integration of Murine and Human Studies for Mapping Periodontitis Susceptibility.

PubMed

Nashef, A; Qabaja, R; Salaymeh, Y; Botzman, M; Munz, M; Dommisch, H; Krone, B; Hoffmann, P; Wellmann, J; Laudes, M; Berger, K; Kocher, T; Loos, B; van der Velde, N; Uitterlinden, A G; de Groot, L C P G M; Franke, A; Offenbacher, S; Lieb, W; Divaris, K; Mott, R; Gat-Viks, I; Wiess, E; Schaefer, A; Iraqi, F A; Haddad, Y H

2018-05-01

Periodontitis is one of the most common inflammatory human diseases with a strong genetic component. Due to the limited sample size of available periodontitis cohorts and the underlying trait heterogeneity, genome-wide association studies (GWASs) of chronic periodontitis (CP) have largely been unsuccessful in identifying common susceptibility factors. A combination of quantitative trait loci (QTL) mapping in mice with association studies in humans has the potential to discover novel risk loci. To this end, we assessed alveolar bone loss in response to experimental periodontal infection in 25 lines (286 mice) from the Collaborative Cross (CC) mouse population using micro-computed tomography (µCT) analysis. The orthologous human chromosomal regions of the significant QTL were analyzed for association using imputed genotype data (OmniExpress BeadChip arrays) derived from case-control samples of aggressive periodontitis (AgP; 896 cases, 7,104 controls) and chronic periodontitis (CP; 2,746 cases, 1,864 controls) of northwest European and European American descent, respectively. In the mouse genome, QTL mapping revealed 2 significant loci (-log P = 5.3; false discovery rate = 0.06) on chromosomes 1 ( Perio3) and 14 ( Perio4). The mapping resolution ranged from ~1.5 to 3 Mb. Perio3 overlaps with a previously reported QTL associated with residual bone volume in F2 cross and includes the murine gene Ccdc121. Its human orthologue showed previously a nominal significant association with CP in humans. Use of variation data from the genomes of the CC founder strains further refined the QTL and suggested 7 candidate genes ( CAPN8, DUSP23, PCDH17, SNORA17, PCDH9, LECT1, and LECT2). We found no evidence of association of these candidates with the human orthologues. In conclusion, the CC populations enabled mapping of confined QTL that confer susceptibility to alveolar bone loss in mice and larger human phenotype-genotype samples and additional expression data from gingival tissues are likely required to identify true positive signals.

Noninvasive Assessment of Oxygen Extraction Fraction in Chronic Ischemia Using Quantitative Susceptibility Mapping at 7 Tesla.

PubMed

Uwano, Ikuko; Kudo, Kohsuke; Sato, Ryota; Ogasawara, Kuniaki; Kameda, Hiroyuki; Nomura, Jun-Ichi; Mori, Futoshi; Yamashita, Fumio; Ito, Kenji; Yoshioka, Kunihiro; Sasaki, Makoto

2017-08-01

The oxygen extraction fraction (OEF) is an effective metric to evaluate metabolic reserve in chronic ischemia. However, OEF is considered to be accurately measured only when using positron emission tomography (PET). Thus, we investigated whether OEF maps generated by magnetic resonance quantitative susceptibility mapping (QSM) at 7 Tesla enabled detection of OEF changes when compared with those obtained with PET. Forty-one patients with chronic stenosis/occlusion of the unilateral internal carotid artery or middle cerebral artery were examined using 7 Tesla-MRI and PET scanners. QSM images were obtained from 3-dimensional T2*-weighted images, using a multiple dipole-inversion algorithm. OEF maps were generated based on susceptibility differences between venous structures and brain tissues on QSM images. OEF ratios of the ipsilateral middle cerebral artery territory against the contralateral side were calculated on the QSM-OEF and PET-OEF images, using an anatomic template. The OEF ratio in the middle cerebral artery territory showed significant correlations between QSM-OEF and PET-OEF maps ( r =0.69; P <0.001), especially in patients with a substantial increase in the PET-OEF ratio of 1.09 ( r =0.79; P =0.004), although showing significant systematic biases for the agreements. An increased QSM-OEF ratio of >1.09, as determined by receiver operating characteristic analysis, showed a sensitivity and specificity of 0.82 and 0.86, respectively, for the substantial increase in the PET-OEF ratio. Absolute QSM-OEF values were significantly correlated with PET-OEF values in the patients with increased PET-OEF. OEF ratios on QSM-OEF images at 7 Tesla showed a good correlation with those on PET-OEF images in patients with unilateral steno-occlusive internal carotid artery/middle cerebral artery lesions, suggesting that noninvasive OEF measurement by MRI can be a substitute for PET. © 2017 American Heart Association, Inc.

Mapping wildfire susceptibility in Southern California using live and dead fractions of vegetation derived from Multiple Endmember Spectral Mixture Analysis of MODIS imagery

NASA Astrophysics Data System (ADS)

Schneider, P.; Roberts, D. A.

2008-12-01

Wildfire is a significant natural disturbance mechanism in Southern California. Assessing spatial patterns of wildfire susceptibility requires estimates of the live and dead fractions of vegetation. The Fire Potential Index (FPI), which is currently the only operationally computed fire susceptibility index incorporating remote sensing data, estimates such fractions using a relative greenness measure based on time series of vegetation index images. This contribution assesses the potential of Multiple Endmember Spectral Mixture Analysis (MESMA) for deriving such fractions from single MODIS images without the need for a long remote sensing time series, and investigates the applicability of such MESMA-derived fractions for mapping dynamic fire susceptibility in Southern California. Endmembers for MESMA were selected from a library of reference endmembers using Constrained Reference Endmember Selection (CRES), which uses field estimates of fractions to guide the selection process. Fraction images of green vegetation, non-photosynthetic vegetation, soil, and shade were then computed for all available 16-day MODIS composites between 2000 and 2006 using MESMA. Initial results indicate that MESMA of MODIS imagery is capable of providing reliable estimates of live and dead vegetation fraction. Validation against in situ observations in the Santa Ynez Mountains near Santa Barbara, California, shows that the average fraction error for two tested species was around 10%. Further validation of MODIS-derived fractions was performed against fractions from high-resolution hyperspectral data. It was shown that the fractions derived from data of both sensors correlate with R2 values greater than 0.95. MESMA-derived live and dead vegetation fractions were subsequently tested as a substitute to relative greenness in the FPI algorithm. FPI was computed for every day between 2000 and 2006 using the derived fractions. Model performance was then tested by extracting FPI values for historical fire events and random no-fire events in Southern California for the same period and developing a logistic regression model. Preliminary results show that an FPI based on MESMA-derived fractions has the potential to deliver similar performance as the traditional FPI but requiring a greatly reduced data volume and using an approach based on physical rather than empirical relationships.

Assessment of seismic hazards along the northern Gulf of Aqaba

NASA Astrophysics Data System (ADS)

Abueladas, Abdel-Rahman Aqel

Aqaba and Elat are very important port and recreation cities for the Hashemite Kingdom of Jordan and Israel, respectively. The two cities are the most susceptible to damage from a destructive future earthquake because they are located over the tectonically active Dead Sea transform fault (DST) that is the source of most of the major historical earthquakes in the region. The largest twentieth century earthquake on the DST, the magnitude Mw 7.2 Nuweiba earthquake of November 22, 1995, caused damage to structures in both cities. The integration of geological, geophysical, and earthquake engineering studies will help to assess the seismic hazards by determining the location and slip potential of active faults and by mapping areas of high liquefaction susceptibility. Ground Penetrating Radar (GPR) as a high resolution shallow geophysical tool was used to map the shallow active faults in Aqaba, Taba Sabkha area, and Elat. The GPR data revealed the onshore continuation of the Evrona, West Aqaba, Aqaba fault zones, and several transverse faults. The integration of offshore and onshore data confirm the extension of these faults along both sides of the Gulf of Aqaba. A 3D model of GPR data at one site in Aqaba indicates that the NW-trending transverse faults right laterally offset older than NE-trending faults. The most hazardous fault is the Evrona fault which extends north to the Tabs Sabkha. A geographic information system (GIS) database of the seismic hazard was created in order to facilitate the analyzing, manipulation, and updating of the input parameters. Liquefaction potential maps were created for the region based on analysis of borehole data. The liquefaction map shows high and moderate liquefaction susceptibility zones along the northern coast of the Gulf of Aqaba. In Aqaba several hotels are located within a high and moderate liquefaction zones. The Yacht Club, Aqaba, Ayla archaeological site, and a part of commercial area are also situated in a risk area. A part of residential site of the Saraya Development and the southern part of Ayla Oasis Development project area are located within a high susceptibility zone In Elat, the seaport and most hotels are located within a high susceptibility zone. Fortunately most residence areas, schools, and hospitals in both cities are located within zones not susceptible to liquefaction. A setback, or no build zone, is delineated around active faults to allow a suitable level of conservatism or factor of safety, residential, hotels, commercial buildings, schools, and other facilities are located inside this buffer in Aqaba area. These data will help planners, engineer instructions within the rapidly developing the northern Gulf of Aqaba.

Mapping Fusarium solani and Aphanomyces euteiches root rot resistance and root architecture quantitative trait loci in common bean (Phaseolus vulgaris)

USDA-ARS?s Scientific Manuscript database

Root rot diseases of bean (Phaseolus vulgaris L.) are a constraint to dry and snap bean production. We developed the RR138 RIL mapping population from the cross of OSU5446, a susceptible line that meets current snap bean processing industry standards, and RR6950, a root rot resistant dry bean in th...

Radiation hybrid mapping of genes in the lithium-sensitive wnt signaling pathway.

PubMed

Rhoads, A R; Karkera, J D; Detera-Wadleigh, S D

1999-09-01

Lithium, an effective drug in the treatment of bipolar disorder, has been proposed to disrupt the Wnt signaling pathway. To facilitate analysis of the possible involvement of elements of the Wnt pathway in human bipolar disorder, a high resolution radiation hybrid mapping (RHM) of these genes was performed. A fine physical location has been obtained for Wnt 7A, frizzled 3, 4 and 5, dishevelled 1, 2 and 3, GSK3beta, axin, alpha-catenin, the Armadillo repeat-containing genes (delta-catenin and ARVCF), and a frizzled-like protein (frpHE) using the Stanford Human Genome Center (SHGC) G3 panel. Most of these genes were previously mapped by fluorescence in situ hybridization (FISH). Frizzled 4, axin and frpHE did not have a previous chromosomal assignment and were linked by RHM to chromosome markers, SHGC-35131 at 11q22.1, NIB1488 at 16p13.3 and D7S2919 at 7p15.2, respectively. Interestingly, some of these genes were found to map within potential regions underlying susceptibility to bipolar disorder and schizophrenia as well as disorders of neurodevelopmental origin. This alternative approach of establishing the precise location of selected genetic components of a candidate pathway and determining if they map within previously defined susceptibility loci should help to identify plausible candidate genes that warrant further analysis through association and mutational scanning.

SU-E-J-233: Effect of Brachytherapy Seed Artifacts in T2 and Proton Density Maps in MR Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mashouf, S; University of Toronto, Dept of Radiation Oncology, Toronto, Ontario; Fatemi-Ardekani, A

Purpose: This study aims at investigating the influence of brachytherapy seeds on T2 and proton density (PD) maps generated from MR images. Proton density maps can be used to extract water content. Since dose absorbed in tissue surrounding low energy brachytherapy seeds are highly influenced by tissue composition, knowing the water content is a first step towards implementing a heterogeneity correction algorithm using MR images. Methods: An LDR brachytherapy (IsoAid Advantage Pd-103) seed was placed in the middle of an agar-based gel phantom and imaged using a 3T Philips MR scanner with a 168-channel head coil. A multiple echo sequencemore » with TE=20, 40, 60, 80, 100 (ms) with large repetition time (TR=6259ms) was used to extract T2 and PD maps. Results: Seed artifacts were considerably reduced on T2 maps compared to PD maps. The variation of PD around the mean was obtained as −97% to 125% (±1%) while for T2 it was recorded as −71% to 24% (±1%). Conclusion: PD maps which are required for heterogeneity corrections are susceptible to artifacts from seeds. Seed artifacts on T2 maps, however, are significantly reduced due to not being sensitive to B0 field variation.« less

The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers.

PubMed

Amos, Christopher I; Dennis, Joe; Wang, Zhaoming; Byun, Jinyoung; Schumacher, Fredrick R; Gayther, Simon A; Casey, Graham; Hunter, David J; Sellers, Thomas A; Gruber, Stephen B; Dunning, Alison M; Michailidou, Kyriaki; Fachal, Laura; Doheny, Kimberly; Spurdle, Amanda B; Li, Yafang; Xiao, Xiangjun; Romm, Jane; Pugh, Elizabeth; Coetzee, Gerhard A; Hazelett, Dennis J; Bojesen, Stig E; Caga-Anan, Charlisse; Haiman, Christopher A; Kamal, Ahsan; Luccarini, Craig; Tessier, Daniel; Vincent, Daniel; Bacot, François; Van Den Berg, David J; Nelson, Stefanie; Demetriades, Stephen; Goldgar, David E; Couch, Fergus J; Forman, Judith L; Giles, Graham G; Conti, David V; Bickeböller, Heike; Risch, Angela; Waldenberger, Melanie; Brüske-Hohlfeld, Irene; Hicks, Belynda D; Ling, Hua; McGuffog, Lesley; Lee, Andrew; Kuchenbaecker, Karoline; Soucy, Penny; Manz, Judith; Cunningham, Julie M; Butterbach, Katja; Kote-Jarai, Zsofia; Kraft, Peter; FitzGerald, Liesel; Lindström, Sara; Adams, Marcia; McKay, James D; Phelan, Catherine M; Benlloch, Sara; Kelemen, Linda E; Brennan, Paul; Riggan, Marjorie; O'Mara, Tracy A; Shen, Hongbing; Shi, Yongyong; Thompson, Deborah J; Goodman, Marc T; Nielsen, Sune F; Berchuck, Andrew; Laboissiere, Sylvie; Schmit, Stephanie L; Shelford, Tameka; Edlund, Christopher K; Taylor, Jack A; Field, John K; Park, Sue K; Offit, Kenneth; Thomassen, Mads; Schmutzler, Rita; Ottini, Laura; Hung, Rayjean J; Marchini, Jonathan; Amin Al Olama, Ali; Peters, Ulrike; Eeles, Rosalind A; Seldin, Michael F; Gillanders, Elizabeth; Seminara, Daniela; Antoniou, Antonis C; Pharoah, Paul D P; Chenevix-Trench, Georgia; Chanock, Stephen J; Simard, Jacques; Easton, Douglas F

2017-01-01

Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits. The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures. Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126-35. ©2016 AACR. ©2016 American Association for Cancer Research.

Meteorological Hazard Assessment and Risk Mitigation in Rwanda.

NASA Astrophysics Data System (ADS)

Nduwayezu, Emmanuel; Jaboyedoff, Michel; Bugnon, Pierre-Charles; Nsengiyumva, Jean-Baptiste; Horton, Pascal; Derron, Marc-Henri

2015-04-01

Between 10 and 13 April 2012, heavy rains hit sectors adjacent to the Vulcanoes National Park (Musanze District in the Northern Province and Nyabihu and Rubavu Districts in the Western Province of RWANDA), causing floods that affected about 11,000 persons. Flooding caused deaths and injuries among the affected population, and extensive damage to houses and properties. 348 houses were destroyed and 446 were partially damaged or have been underwater for several days. Families were forced to leave their flooded homes and seek temporal accommodation with their neighbors, often in overcrowded places. Along the West-northern border of RWANDA, Virunga mountain range consists of 6 major volcanoes. Mount Karisimbi is the highest volcano at 4507m. The oldest mountain is mount Sabyinyo which rises 3634m. The hydraulic network in Musanze District is formed by temporary torrents and permanent watercourses. Torrents surge during strong storms, and are provoked by water coming downhill from the volcanoes, some 20 km away. This area is periodically affected by flooding and landslides because of heavy rain (Rwanda has 2 rainy seasons from February to April and from September to November each year in general and 2 dry seasons) striking the Volcano National Park. Rain water creates big water channels (in already known torrents or new ones) that impact communities, agricultural soils and crop yields. This project aims at identifying hazardous and risky areas by producing susceptibility maps for floods, debris flow and landslides over this sector. Susceptibility maps are being drawn using field observations, during and after the 2012 events, and an empirical model of propagation for regional susceptibility assessments of debris flows (Flow-R). Input data are 10m and 30m resolution DEMs, satellite images, hydrographic network, and some information on geological substratum and soil occupation. Combining susceptibility maps with infrastructures, houses and population density maps will be used in identifying the most risky areas. Finally, based on practical experiences in this kind of field and produced documents some recommendations for low-cost mitigation measures will be proposed. Reference: MIDIMAR, Impacts of floods and landslides on socio-economic development profile. Case study: Musanze District. Kigali, June 2012.

A gene for familial psoriasis susceptibility maps to the distal end of human chromosome 17q

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bowcock, A.; Tomfohrde, J.; Barnes, R.

1994-09-01

Psoriasis is a chronic inflammatory dermatosis that affects approximately 2% of the population. A gene for psoriasis susceptibility was localized to the distal region of human chromosome 17q as a result of a genome wide linkage-analysis with polymorphic microsatellites and eight multiply affected psoriasis kindreds. With one large kindred a maximum two-point lod score with D17S784 was 5.70 at 15% recombination. Heterogeneity testing indicated that psoriasis susceptibility in 50% of the families was linked to distal 17q. Susceptibility to psoriasis has repeatedly been found to be associated with HLA-Cw6 and associated HLA alleles. We therefore genotyped the families for locimore » within and flanking HLA; these included PCR assays for susceptibility alleles. By lod score analysis no evidence of linkage of psoriasis susceptibility to HLA was detected. The distribution of HLA-Cw6 and HLA-Class II alleles showed that HLA-Cw6 was frequent among patients, particularly in 4 of the 5 unlinked families. All affected members of two of these unlinked families carried HLA-Cw6 (empirical P values of 0.027 and 0.004). In 2 other families 4 of 6 and 6 of 7 had HLA-Cw6. In some of these families, an inability to detect linkage to HLA may have been due to the occurrence of multiple haplotypes carrying the psoriasis associated allele, HLA-Cw6. Contrasting with these findings, we observed a lack of association between HLA-Cw6 and psoriasis in the 3 families in which 17q markers were linked to susceptibility. The ability to detect linkage to 17q confirms that some forms of familial psoriasis are due to molecular defects at a single major genetic locus other than HLA.« less

Rockfall susceptibility mapping of Yosemite Valley (USA) using a high-resolution digital elevation model

NASA Astrophysics Data System (ADS)

Pannatier, A.; Oppikofer, T.; Jaboyedoff, M.; Stock, G. M.

2009-04-01

In Yosemite National Park (California, USA) rockfalls from the steep valley flanks are frequent (>600 documented events in 150 years) and threaten infrastructure in this popular tourist area. This study focuses on a methodology to map the susceptibility to rockfall initiation based on a high-resolution digital elevation model (HRDEM) obtained from aerial laser scanning (1 meter cell size). This methodology is based on geometric factors derived from the HRDEM, i.e., the steepness of the topography, the presence of joints or fractures enabling either a planar or a wedge failure mechanism, and a high denudation potential. The slope angle histogram computed using standard GIS routines was simulated using Gaussian distributions, which were attributed to different parts of the topography, i.e., the cliffs, the valley flanks and the valley floor. Slopes steeper than 36° are found to form cliffs and thus potentially lead to rockfalls. A morpho-structural analysis of the HRDEM was performed in Coltop3D software to determine the major discontinuity sets that shape the topography. Kinematic analyses were made for each of these 7 discontinuity sets in order to determine the HRDEM cells that fulfil the geometric criteria for a planar or wedge failure mechanism. Most of the cliffs in Yosemite Valley enable one or both of these failure mechanisms. The denudation potential was assessed using the sloping local base level (SLBL) concept. The SLBL defines a basal erosion surface and the above lying rock masses (up to 400 m in some of the vertical cliffs) are susceptible to erosion by mass wasting. A thickness of 20 m above the SLBL surface was chosen as lower limit for the denudation potential criterion. The HRDEM cells that satisfy 1, 2 or all 3 criteria are considered having low, moderate and high susceptibility to rockfall initiation. The areas with highest susceptibility (El Capitan, Glacier Point, Yosemite Falls and Half Dome) coincide well with post-glacial talus accumulations and historic rockfall sources. Compared to previous maps of potential rockfall sources that were mainly based on the slope angle criterion, this study provides a more refined analysis of potential rockfall sources and is useful for focussing detailed field investigations on those areas with high susceptibility.

Fine-mapping identifies two additional breast cancer susceptibility loci at 9q31.2

PubMed Central

Orr, Nick; Dudbridge, Frank; Dryden, Nicola; Maguire, Sarah; Novo, Daniela; Perrakis, Eleni; Johnson, Nichola; Ghoussaini, Maya; Hopper, John L.; Southey, Melissa C.; Apicella, Carmel; Stone, Jennifer; Schmidt, Marjanka K.; Broeks, Annegien; Van't Veer, Laura J.; Hogervorst, Frans B.; Fasching, Peter A.; Haeberle, Lothar; Ekici, Arif B.; Beckmann, Matthias W.; Gibson, Lorna; Aitken, Zoe; Warren, Helen; Sawyer, Elinor; Tomlinson, Ian; Kerin, Michael J.; Miller, Nicola; Burwinkel, Barbara; Marme, Frederik; Schneeweiss, Andreas; Sohn, Chistof; Guénel, Pascal; Truong, Thérèse; Cordina-Duverger, Emilie; Sanchez, Marie; Bojesen, Stig E.; Nordestgaard, Børge G.; Nielsen, Sune F.; Flyger, Henrik; Benitez, Javier; Zamora, Maria Pilar; Arias Perez, Jose Ignacio; Menéndez, Primitiva; Anton-Culver, Hoda; Neuhausen, Susan L.; Brenner, Hermann; Dieffenbach, Aida Karina; Arndt, Volker; Stegmaier, Christa; Hamann, Ute; Brauch, Hiltrud; Justenhoven, Christina; Brüning, Thomas; Ko, Yon-Dschun; Nevanlinna, Heli; Aittomäki, Kristiina; Blomqvist, Carl; Khan, Sofia; Bogdanova, Natalia; Dörk, Thilo; Lindblom, Annika; Margolin, Sara; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M.; Chenevix-Trench, Georgia; Beesley, Jonathan; Lambrechts, Diether; Moisse, Matthieu; Floris, Guiseppe; Beuselinck, Benoit; Chang-Claude, Jenny; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Radice, Paolo; Peterlongo, Paolo; Peissel, Bernard; Pensotti, Valeria; Couch, Fergus J.; Olson, Janet E.; Slettedahl, Seth; Vachon, Celine; Giles, Graham G.; Milne, Roger L.; McLean, Catriona; Haiman, Christopher A.; Henderson, Brian E.; Schumacher, Fredrick; Le Marchand, Loic; Simard, Jacques; Goldberg, Mark S.; Labrèche, France; Dumont, Martine; Kristensen, Vessela; Alnæs, Grethe Grenaker; Nord, Silje; Borresen-Dale, Anne-Lise; Zheng, Wei; Deming-Halverson, Sandra; Shrubsole, Martha; Long, Jirong; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Andrulis, Irene L.; Knight, Julia A.; Glendon, Gord; Tchatchou, Sandrine; Devilee, Peter; Tollenaar, Robertus A. E. M.; Seynaeve, Caroline M.; Van Asperen, Christi J.; Garcia-Closas, Montserrat; Figueroa, Jonine; Chanock, Stephen J.; Lissowska, Jolanta; Czene, Kamila; Darabi, Hatef; Eriksson, Mikael; Klevebring, Daniel; Hooning, Maartje J.; Hollestelle, Antoinette; van Deurzen, Carolien H. M.; Kriege, Mieke; Hall, Per; Li, Jingmei; Liu, Jianjun; Humphreys, Keith; Cox, Angela; Cross, Simon S.; Reed, Malcolm W. R.; Pharoah, Paul D. P.; Dunning, Alison M.; Shah, Mitul; Perkins, Barbara J.; Jakubowska, Anna; Lubinski, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Ashworth, Alan; Swerdlow, Anthony; Jones, Michael; Schoemaker, Minouk J.; Meindl, Alfons; Schmutzler, Rita K.; Olswold, Curtis; Slager, Susan; Toland, Amanda E.; Yannoukakos, Drakoulis; Muir, Kenneth; Lophatananon, Artitaya; Stewart-Brown, Sarah; Siriwanarangsan, Pornthep; Matsuo, Keitaro; Ito, Hidema; Iwata, Hiroji; Ishiguro, Junko; Wu, Anna H.; Tseng, Chiu-chen; Van Den Berg, David; Stram, Daniel O.; Teo, Soo Hwang; Yip, Cheng Har; Kang, Peter; Ikram, Mohammad Kamran; Shu, Xiao-Ou; Lu, Wei; Gao, Yu-Tang; Cai, Hui; Kang, Daehee; Choi, Ji-Yeob; Park, Sue K.; Noh, Dong-Young; Hartman, Mikael; Miao, Hui; Lim, Wei Yen; Lee, Soo Chin; Sangrajrang, Suleeporn; Gaborieau, Valerie; Brennan, Paul; Mckay, James; Wu, Pei-Ei; Hou, Ming-Feng; Yu, Jyh-Cherng; Shen, Chen-Yang; Blot, William; Cai, Qiuyin; Signorello, Lisa B.; Luccarini, Craig; Bayes, Caroline; Ahmed, Shahana; Maranian, Mel; Healey, Catherine S.; González-Neira, Anna; Pita, Guillermo; Alonso, M. Rosario; Álvarez, Nuria; Herrero, Daniel; Tessier, Daniel C.; Vincent, Daniel; Bacot, Francois; Hunter, David J.; Lindstrom, Sara; Dennis, Joe; Michailidou, Kyriaki; Bolla, Manjeet K.; Easton, Douglas F.; dos Santos Silva, Isabel; Fletcher, Olivia; Peto, Julian

2015-01-01

We recently identified a novel susceptibility variant, rs865686, for estrogen-receptor positive breast cancer at 9q31.2. Here, we report a fine-mapping analysis of the 9q31.2 susceptibility locus using 43 160 cases and 42 600 controls of European ancestry ascertained from 52 studies and a further 5795 cases and 6624 controls of Asian ancestry from nine studies. Single nucleotide polymorphism (SNP) rs676256 was most strongly associated with risk in Europeans (odds ratios [OR] = 0.90 [0.88–0.92]; P-value = 1.58 × 10−25). This SNP is one of a cluster of highly correlated variants, including rs865686, that spans ∼14.5 kb. We identified two additional independent association signals demarcated by SNPs rs10816625 (OR = 1.12 [1.08–1.17]; P-value = 7.89 × 10−09) and rs13294895 (OR = 1.09 [1.06–1.12]; P-value = 2.97 × 10−11). SNP rs10816625, but not rs13294895, was also associated with risk of breast cancer in Asian individuals (OR = 1.12 [1.06–1.18]; P-value = 2.77 × 10−05). Functional genomic annotation using data derived from breast cancer cell-line models indicates that these SNPs localise to putative enhancer elements that bind known drivers of hormone-dependent breast cancer, including ER-α, FOXA1 and GATA-3. In vitro analyses indicate that rs10816625 and rs13294895 have allele-specific effects on enhancer activity and suggest chromatin interactions with the KLF4 gene locus. These results demonstrate the power of dense genotyping in large studies to identify independent susceptibility variants. Analysis of associations using subjects with different ancestry, combined with bioinformatic and genomic characterisation, can provide strong evidence for the likely causative alleles and their functional basis. PMID:25652398

Genome-wide association study of colorectal cancer in Hispanics

PubMed Central

Schmit, Stephanie L.; Schumacher, Fredrick R.; Edlund, Christopher K.; Conti, David V.; Ihenacho, Ugonna; Wan, Peggy; Van Den Berg, David; Casey, Graham; Fortini, Barbara K.; Lenz, Heinz-Josef; Tusié-Luna, Teresa; Aguilar-Salinas, Carlos A.; Moreno-Macías, Hortensia; Huerta-Chagoya, Alicia; Ordóñez-Sánchez, María Luisa; Rodríguez-Guillén, Rosario; Cruz-Bautista, Ivette; Rodríguez-Torres, Maribel; Muñóz-Hernández, Linda Liliana; Arellano-Campos, Olimpia; Gómez, Donají; Alvirde, Ulices; González-Villalpando, Clicerio; González-Villalpando, María Elena; Le Marchand, Loic; Haiman, Christopher A.; Figueiredo, Jane C.

2016-01-01

Genome-wide association studies (GWAS) have identified 58 susceptibility alleles across 37 regions associated with the risk of colorectal cancer (CRC) with P < 5×10−8. Most studies have been conducted in non-Hispanic whites and East Asians; however, the generalizability of these findings and the potential for ethnic-specific risk variation in Hispanic and Latino (HL) individuals have been largely understudied. We describe the first GWAS of common genetic variation contributing to CRC risk in HL (1611 CRC cases and 4330 controls). We also examine known susceptibility alleles and implement imputation-based fine-mapping to identify potential ethnicity-specific association signals in known risk regions. We discovered 17 variants across 4 independent regions that merit further investigation due to suggestive CRC associations (P < 1×10−6) at 1p34.3 (rs7528276; Odds Ratio (OR) = 1.86 [95% confidence interval (CI): 1.47–2.36); P = 2.5×10−7], 2q23.3 (rs1367374; OR = 1.37 (95% CI: 1.21–1.55); P = 4.0×10−7), 14q24.2 (rs143046984; OR = 1.65 (95% CI: 1.36–2.01); P = 4.1×10−7) and 16q12.2 [rs142319636; OR = 1.69 (95% CI: 1.37–2.08); P=7.8×10−7]. Among the 57 previously published CRC susceptibility alleles with minor allele frequency ≥1%, 76.5% of SNPs had a consistent direction of effect and 19 (33.3%) were nominally statistically significant (P < 0.05). Further, rs185423955 and rs60892987 were identified as novel secondary susceptibility variants at 3q26.2 (P = 5.3×10–5) and 11q12.2 (P = 6.8×10−5), respectively. Our findings demonstrate the importance of fine mapping in HL. These results are informative for variant prioritization in functional studies and future risk prediction modeling in minority populations. PMID:27207650

Fine-mapping identifies two additional breast cancer susceptibility loci at 9q31.2.

PubMed

Orr, Nick; Dudbridge, Frank; Dryden, Nicola; Maguire, Sarah; Novo, Daniela; Perrakis, Eleni; Johnson, Nichola; Ghoussaini, Maya; Hopper, John L; Southey, Melissa C; Apicella, Carmel; Stone, Jennifer; Schmidt, Marjanka K; Broeks, Annegien; Van't Veer, Laura J; Hogervorst, Frans B; Fasching, Peter A; Haeberle, Lothar; Ekici, Arif B; Beckmann, Matthias W; Gibson, Lorna; Aitken, Zoe; Warren, Helen; Sawyer, Elinor; Tomlinson, Ian; Kerin, Michael J; Miller, Nicola; Burwinkel, Barbara; Marme, Frederik; Schneeweiss, Andreas; Sohn, Chistof; Guénel, Pascal; Truong, Thérèse; Cordina-Duverger, Emilie; Sanchez, Marie; Bojesen, Stig E; Nordestgaard, Børge G; Nielsen, Sune F; Flyger, Henrik; Benitez, Javier; Zamora, Maria Pilar; Arias Perez, Jose Ignacio; Menéndez, Primitiva; Anton-Culver, Hoda; Neuhausen, Susan L; Brenner, Hermann; Dieffenbach, Aida Karina; Arndt, Volker; Stegmaier, Christa; Hamann, Ute; Brauch, Hiltrud; Justenhoven, Christina; Brüning, Thomas; Ko, Yon-Dschun; Nevanlinna, Heli; Aittomäki, Kristiina; Blomqvist, Carl; Khan, Sofia; Bogdanova, Natalia; Dörk, Thilo; Lindblom, Annika; Margolin, Sara; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M; Chenevix-Trench, Georgia; Beesley, Jonathan; Lambrechts, Diether; Moisse, Matthieu; Floris, Guiseppe; Beuselinck, Benoit; Chang-Claude, Jenny; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Radice, Paolo; Peterlongo, Paolo; Peissel, Bernard; Pensotti, Valeria; Couch, Fergus J; Olson, Janet E; Slettedahl, Seth; Vachon, Celine; Giles, Graham G; Milne, Roger L; McLean, Catriona; Haiman, Christopher A; Henderson, Brian E; Schumacher, Fredrick; Le Marchand, Loic; Simard, Jacques; Goldberg, Mark S; Labrèche, France; Dumont, Martine; Kristensen, Vessela; Alnæs, Grethe Grenaker; Nord, Silje; Borresen-Dale, Anne-Lise; Zheng, Wei; Deming-Halverson, Sandra; Shrubsole, Martha; Long, Jirong; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Andrulis, Irene L; Knight, Julia A; Glendon, Gord; Tchatchou, Sandrine; Devilee, Peter; Tollenaar, Robertus A E M; Seynaeve, Caroline M; Van Asperen, Christi J; Garcia-Closas, Montserrat; Figueroa, Jonine; Chanock, Stephen J; Lissowska, Jolanta; Czene, Kamila; Darabi, Hatef; Eriksson, Mikael; Klevebring, Daniel; Hooning, Maartje J; Hollestelle, Antoinette; van Deurzen, Carolien H M; Kriege, Mieke; Hall, Per; Li, Jingmei; Liu, Jianjun; Humphreys, Keith; Cox, Angela; Cross, Simon S; Reed, Malcolm W R; Pharoah, Paul D P; Dunning, Alison M; Shah, Mitul; Perkins, Barbara J; Jakubowska, Anna; Lubinski, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Ashworth, Alan; Swerdlow, Anthony; Jones, Michael; Schoemaker, Minouk J; Meindl, Alfons; Schmutzler, Rita K; Olswold, Curtis; Slager, Susan; Toland, Amanda E; Yannoukakos, Drakoulis; Muir, Kenneth; Lophatananon, Artitaya; Stewart-Brown, Sarah; Siriwanarangsan, Pornthep; Matsuo, Keitaro; Ito, Hidema; Iwata, Hiroji; Ishiguro, Junko; Wu, Anna H; Tseng, Chiu-Chen; Van Den Berg, David; Stram, Daniel O; Teo, Soo Hwang; Yip, Cheng Har; Kang, Peter; Ikram, Mohammad Kamran; Shu, Xiao-Ou; Lu, Wei; Gao, Yu-Tang; Cai, Hui; Kang, Daehee; Choi, Ji-Yeob; Park, Sue K; Noh, Dong-Young; Hartman, Mikael; Miao, Hui; Lim, Wei Yen; Lee, Soo Chin; Sangrajrang, Suleeporn; Gaborieau, Valerie; Brennan, Paul; Mckay, James; Wu, Pei-Ei; Hou, Ming-Feng; Yu, Jyh-Cherng; Shen, Chen-Yang; Blot, William; Cai, Qiuyin; Signorello, Lisa B; Luccarini, Craig; Bayes, Caroline; Ahmed, Shahana; Maranian, Mel; Healey, Catherine S; González-Neira, Anna; Pita, Guillermo; Alonso, M Rosario; Álvarez, Nuria; Herrero, Daniel; Tessier, Daniel C; Vincent, Daniel; Bacot, Francois; Hunter, David J; Lindstrom, Sara; Dennis, Joe; Michailidou, Kyriaki; Bolla, Manjeet K; Easton, Douglas F; dos Santos Silva, Isabel; Fletcher, Olivia; Peto, Julian

2015-05-15

We recently identified a novel susceptibility variant, rs865686, for estrogen-receptor positive breast cancer at 9q31.2. Here, we report a fine-mapping analysis of the 9q31.2 susceptibility locus using 43 160 cases and 42 600 controls of European ancestry ascertained from 52 studies and a further 5795 cases and 6624 controls of Asian ancestry from nine studies. Single nucleotide polymorphism (SNP) rs676256 was most strongly associated with risk in Europeans (odds ratios [OR] = 0.90 [0.88-0.92]; P-value = 1.58 × 10(-25)). This SNP is one of a cluster of highly correlated variants, including rs865686, that spans ∼14.5 kb. We identified two additional independent association signals demarcated by SNPs rs10816625 (OR = 1.12 [1.08-1.17]; P-value = 7.89 × 10(-09)) and rs13294895 (OR = 1.09 [1.06-1.12]; P-value = 2.97 × 10(-11)). SNP rs10816625, but not rs13294895, was also associated with risk of breast cancer in Asian individuals (OR = 1.12 [1.06-1.18]; P-value = 2.77 × 10(-05)). Functional genomic annotation using data derived from breast cancer cell-line models indicates that these SNPs localise to putative enhancer elements that bind known drivers of hormone-dependent breast cancer, including ER-α, FOXA1 and GATA-3. In vitro analyses indicate that rs10816625 and rs13294895 have allele-specific effects on enhancer activity and suggest chromatin interactions with the KLF4 gene locus. These results demonstrate the power of dense genotyping in large studies to identify independent susceptibility variants. Analysis of associations using subjects with different ancestry, combined with bioinformatic and genomic characterisation, can provide strong evidence for the likely causative alleles and their functional basis. © The Author 2015. Published by Oxford University Press.

Fine mapping and positional candidate studies on chromosome 5p13 identify multiple asthma susceptibility loci.

PubMed

Kurz, Thorsten; Hoffjan, Sabine; Hayes, M Geoffrey; Schneider, Dan; Nicolae, Raluca; Heinzmann, Andrea; Jerkic, Sylvija P; Parry, Rod; Cox, Nancy J; Deichmann, Klaus A; Ober, Carole

2006-08-01

Genome-wide linkage scans to identify asthma susceptibility loci have revealed many linked regions, including a broad region on chromosome 5p. To identify a 5p-linked asthma or bronchial hyperresponsiveness (BHR) locus. We performed fine mapping and positional candidate studies of this region in the Hutterites and an outbred case-control sample from Germany by genotyping 89 single nucleotide polymorphisms (SNPs) in 22 genes. SNP and haplotype analyses were performed. Three genes in a distal region (zinc finger RNA binding protein [ZFR], natriuretic peptide receptor C, and a disintegrin and metalloproteinase domain with thrombospondin type 1 motif [ADAMTS12]) were associated with BHR, whereas 4 genes in a proximal region (prolactin receptor, IL-7 receptor [IL7R], leukemia inhibitory factor receptor [LIFR], and prostaglandin E4 receptor [PTGER4]) were associated with asthma symptoms in the Hutterites. Furthermore, nearly the entire original linkage signal in the Hutterites was generated by individuals who had the risk-associated alleles in ZFR3, natriuretic peptide receptor C, ADAMTS12, LIFR, and PTGER4. Variation in ADAMTS12, IL7R, and PTGER4 were also associated with asthma in the outbred Germans, and the frequencies of long-range haplotypes composed of SNPs at ZFR, ADAMTS12, IL7R, LIFR, and PTGER4 were significantly different between both the German and Hutterite cases and controls. There is little linkage disequilbrium between alleles in these 2 regions in either population. These results suggest that a broad region on 5p, separated by >9 Mb, harbors at least 2 and possibly 5 asthma or BHR susceptibility loci. These findings are consistent with the hypothesis that regions providing evidence for linkage in multiple populations may, in fact, house more than 1 susceptibility locus, as appears to be the case for the linked region on 5p. Identifying asthma or BHR genes could lead to novel therapeutic approaches.

Evaluation of a signal intensity mask in the interpretation of functional MR imaging activation maps.

PubMed

Strigel, Roberta M; Moritz, Chad H; Haughton, Victor M; Badie, Behnam; Field, Aaron; Wood, David; Hartman, Michael; Rowley, Howard A

2005-03-01

The purpose of this study was to determine the incidence of susceptibility artifacts on functional MR imaging (fMRI) studies and their effect on fMRI readings. We hypothesized that the availability of the signal intensity maps (SIMs) changes the interpretation of fMRI studies in which susceptibility artifacts affected eloquent brain regions. We reviewed 152 consecutive clinical fMRI studies performed with a SIM. The SIM consisted of the initial echo-planar images (EPI) in each section thresholded to eliminate signal intensity from outside the brain and then overlaid on anatomic images. The cause of the artifact was then determined by examining the images. Cases with a susceptibility artifact in eloquent brain were included in a blinded study read by four readers, first without and then with the SIM. For each reader, the number of times the interpretation changed on viewing the SIM was counted. Of 152 patients, 44% had signal intensity loss involving cerebral cortex and 18% involving an eloquent brain region. Causes of the artifacts were: surgical site artifact, blood products, dental devices, calcium, basal ganglia calcifications, ICP monitors, embolization materials, and air. When provided with the SIM, readers changed interpretations in 8-38% of patient cases, depending on reader experience and size and location of susceptibility artifact. Patients referred for clinical fMRI have a high incidence of susceptibility artifacts, whose presence and size can be determined by inspection of the SIM but not anatomic images. The availability of the SIM may affect interpretation of the fMRI.

Effects of ferumoxytol on quantitative PET measurements in simultaneous PET/MR whole-body imaging: a pilot study in a baboon model.

PubMed

Borra, Ronald Jh; Cho, Hoon-Sung; Bowen, Spencer L; Attenberger, Ulrike; Arabasz, Grae; Catana, Ciprian; Josephson, Lee; Rosen, Bruce R; Guimaraes, Alexander R; Hooker, Jacob M

2015-12-01

Simultaneous PET/MR imaging depends on MR-derived attenuation maps (mu-maps) for accurate attenuation correction of PET data. Currently, these maps are derived from gradient-echo-based MR sequences, which are sensitive to susceptibility changes. Iron oxide magnetic nanoparticles have been used in the measurement of blood volume, tumor microvasculature, tumor-associated macrophages, and characterizing lymph nodes. Our aim in this study was to assess whether the susceptibility effects associated with iron oxide nanoparticles can potentially affect measured (18)F-FDG PET standardized uptake values (SUV) through effects on MR-derived attenuation maps. The study protocol was approved by the Institutional Animal Care and Use Committee. Using a Siemens Biograph mMR PET/MR scanner, we evaluated the effects of increasing concentrations of ferumoxytol and ferumoxytol aggregates on MR-derived mu-maps using an agarose phantom. In addition, we performed a baboon experiment evaluating the effects of a single i.v. ferumoxytol dose (10 mg/kg) on the liver, spleen, and pancreas (18)F-FDG SUV at baseline (ferumoxytol-naïve), within the first hour and at 1, 3, 5, and 11 weeks. Phantom experiments showed mu-map artifacts starting at ferumoxytol aggregate concentrations of 10 to 20 mg/kg. The in vivo baboon data demonstrated a 53% decrease of observed (18)F-FDG SUV compared to baseline within the first hour in the liver, persisting at least 11 weeks. A single ferumoxytol dose can affect measured SUV for at least 3 months, which should be taken into account when administrating ferumoxytol in patients needing sequential PET/MR scans. Advances in knowledge 1. Ferumoxytol aggregates, but not ferumoxytol alone, produce significant artifacts in MR-derived attenuation correction maps at approximate clinical dose levels of 10 mg/kg. 2. When performing simultaneous whole-body (18)F-FDG PET/MR, a single dose of ferumoxytol can result in observed SUV decreases up to 53%, depending on the amount of ferumoxytol aggregates in the studied tissue. Implications for patient care Administration of a single, clinically relevant, dose of ferumoxytol can potentially result in changes in observed SUV for a prolonged period of time in the setting of simultaneous PET/MR. These potential changes should be considered in particular when administering ferumoxytol to patients with expected future PET/MR studies, as ferumoxytol-induced SUV changes might interfere with therapy assessment.

Candidate Genes for Inherited Autism Susceptibility in the Lebanese Population.

PubMed

Kourtian, Silva; Soueid, Jihane; Makhoul, Nadine J; Guisso, Dikran Richard; Chahrour, Maria; Boustany, Rose-Mary N

2017-03-30

Autism spectrum disorder (ASD) is characterized by ritualistic-repetitive behaviors and impaired verbal/non-verbal communication. Many ASD susceptibility genes implicated in neuronal pathways/brain development have been identified. The Lebanese population is ideal for uncovering recessive genes because of shared ancestry and a high rate of consanguineous marriages. Aims here are to analyze for published ASD genes and uncover novel inherited ASD susceptibility genes specific to the Lebanese. We recruited 36 ASD families (ASD: 37, unaffected parents: 36, unaffected siblings: 33) and 100 unaffected Lebanese controls. Cytogenetics 2.7 M Microarrays/CytoScan™ HD arrays allowed mapping of homozygous regions of the genome. The CNTNAP2 gene was screened by Sanger sequencing. Homozygosity mapping uncovered DPP4, TRHR, and MLF1 as novel candidate susceptibility genes for ASD in the Lebanese. Sequencing of hot spot exons in CNTNAP2 led to discovery of a 5 bp insertion in 23/37 ASD patients. This mutation was present in unaffected family members and unaffected Lebanese controls. Although a slight increase in number was observed in ASD patients and family members compared to controls, there were no significant differences in allele frequencies between affecteds and controls (C/TTCTG: γ 2 value = 0.014; p = 0.904). The CNTNAP2 polymorphism identified in this population, hence, is not linked to the ASD phenotype.

Candidate Genes for Inherited Autism Susceptibility in the Lebanese Population

PubMed Central

Kourtian, Silva; Soueid, Jihane; Makhoul, Nadine J.; Guisso, Dikran Richard; Chahrour, Maria; Boustany, Rose-Mary N.

2017-01-01

Autism spectrum disorder (ASD) is characterized by ritualistic-repetitive behaviors and impaired verbal/non-verbal communication. Many ASD susceptibility genes implicated in neuronal pathways/brain development have been identified. The Lebanese population is ideal for uncovering recessive genes because of shared ancestry and a high rate of consanguineous marriages. Aims here are to analyze for published ASD genes and uncover novel inherited ASD susceptibility genes specific to the Lebanese. We recruited 36 ASD families (ASD: 37, unaffected parents: 36, unaffected siblings: 33) and 100 unaffected Lebanese controls. Cytogenetics 2.7 M Microarrays/CytoScan™ HD arrays allowed mapping of homozygous regions of the genome. The CNTNAP2 gene was screened by Sanger sequencing. Homozygosity mapping uncovered DPP4, TRHR, and MLF1 as novel candidate susceptibility genes for ASD in the Lebanese. Sequencing of hot spot exons in CNTNAP2 led to discovery of a 5 bp insertion in 23/37 ASD patients. This mutation was present in unaffected family members and unaffected Lebanese controls. Although a slight increase in number was observed in ASD patients and family members compared to controls, there were no significant differences in allele frequencies between affecteds and controls (C/TTCTG: γ2 value = 0.014; p = 0.904). The CNTNAP2 polymorphism identified in this population, hence, is not linked to the ASD phenotype. PMID:28358038

Morphological Characteristics of Motor Neurons Do Not Determine Their Relative Susceptibility to Degeneration in a Mouse Model of Severe Spinal Muscular Atrophy

PubMed Central

Mutsaers, Chantal A.; Thomson, Derek; Hamilton, Gillian; Parson, Simon H.; Gillingwater, Thomas H.

2012-01-01

Spinal muscular atrophy (SMA) is a leading genetic cause of infant mortality, resulting primarily from the degeneration and loss of lower motor neurons. Studies using mouse models of SMA have revealed widespread heterogeneity in the susceptibility of individual motor neurons to neurodegeneration, but the underlying reasons remain unclear. Data from related motor neuron diseases, such as amyotrophic lateral sclerosis (ALS), suggest that morphological properties of motor neurons may regulate susceptibility: in ALS larger motor units innervating fast-twitch muscles degenerate first. We therefore set out to determine whether intrinsic morphological characteristics of motor neurons influenced their relative vulnerability to SMA. Motor neuron vulnerability was mapped across 10 muscle groups in SMA mice. Neither the position of the muscle in the body, nor the fibre type of the muscle innervated, influenced susceptibility. Morphological properties of vulnerable and disease-resistant motor neurons were then determined from single motor units reconstructed in Thy.1-YFP-H mice. None of the parameters we investigated in healthy young adult mice – including motor unit size, motor unit arbor length, branching patterns, motor endplate size, developmental pruning and numbers of terminal Schwann cells at neuromuscular junctions - correlated with vulnerability. We conclude that morphological characteristics of motor neurons are not a major determinant of disease-susceptibility in SMA, in stark contrast to related forms of motor neuron disease such as ALS. This suggests that subtle molecular differences between motor neurons, or extrinsic factors arising from other cell types, are more likely to determine relative susceptibility in SMA. PMID:23285108

Rockfall hazard and risk assessments along roads at a regional scale: example in Swiss Alps

NASA Astrophysics Data System (ADS)

Michoud, C.; Derron, M.-H.; Horton, P.; Jaboyedoff, M.; Baillifard, F.-J.; Loye, A.; Nicolet, P.; Pedrazzini, A.; Queyrel, A.

2012-03-01

Unlike fragmental rockfall runout assessments, there are only few robust methods to quantify rock-mass-failure susceptibilities at regional scale. A detailed slope angle analysis of recent Digital Elevation Models (DEM) can be used to detect potential rockfall source areas, thanks to the Slope Angle Distribution procedure. However, this method does not provide any information on block-release frequencies inside identified areas. The present paper adds to the Slope Angle Distribution of cliffs unit its normalized cumulative distribution function. This improvement is assimilated to a quantitative weighting of slope angles, introducing rock-mass-failure susceptibilities inside rockfall source areas previously detected. Then rockfall runout assessment is performed using the GIS- and process-based software Flow-R, providing relative frequencies for runout. Thus, taking into consideration both susceptibility results, this approach can be used to establish, after calibration, hazard and risk maps at regional scale. As an example, a risk analysis of vehicle traffic exposed to rockfalls is performed along the main roads of the Swiss alpine valley of Bagnes.

Reduced B Lymphoid Kinase (Blk) Expression Enhances Proinflammatory Cytokine Production and Induces Nephrosis in C57BL/6-lpr/lpr Mice

PubMed Central

Papillion, Amber M.; Tatum, Arthur H.; Princiotta, Michael F.; Hayes, Sandra M.

2014-01-01

BLK, which encodes B lymphoid kinase, was recently identified in genome wide association studies as a susceptibility gene for systemic lupus erythematosus (SLE), and risk alleles mapping to the BLK locus result in reduced gene expression. To determine whether BLK is indeed a bona fide susceptibility gene, we developed an experimental mouse model, namely the Blk+/−.lpr/lpr (Blk+/−.lpr) mouse, in which Blk expression levels are reduced to levels comparable to those in individuals carrying a risk allele. Here, we report that Blk is expressed not only in B cells, but also in IL-17-producing γδ and DN αβ T cells and in plasmacytoid dendritic cells (pDCs). Moreover, we found that solely reducing Blk expression in C57BL/6-lpr/lpr mice enhanced proinflammatory cytokine production and accelerated the onset of lymphoproliferation, proteinuria, and kidney disease. Together, these findings suggest that BLK risk alleles confer susceptibility to SLE through the dysregulation of a proinflammatory cytokine network. PMID:24637841

Quantitative volcanic susceptibility analysis of Lanzarote and Chinijo Islands based on kernel density estimation via a linear diffusion process

PubMed Central

Galindo, I.; Romero, M. C.; Sánchez, N.; Morales, J. M.

2016-01-01

Risk management stakeholders in high-populated volcanic islands should be provided with the latest high-quality volcanic information. We present here the first volcanic susceptibility map of Lanzarote and Chinijo Islands and their submarine flanks based on updated chronostratigraphical and volcano structural data, as well as on the geomorphological analysis of the bathymetric data of the submarine flanks. The role of the structural elements in the volcanic susceptibility analysis has been reviewed: vents have been considered since they indicate where previous eruptions took place; eruptive fissures provide information about the stress field as they are the superficial expression of the dyke conduit; eroded dykes have been discarded since they are single non-feeder dykes intruded in deep parts of Miocene-Pliocene volcanic edifices; main faults have been taken into account only in those cases where they could modified the superficial movement of magma. The application of kernel density estimation via a linear diffusion process for the volcanic susceptibility assessment has been applied successfully to Lanzarote and could be applied to other fissure volcanic fields worldwide since the results provide information about the probable area where an eruption could take place but also about the main direction of the probable volcanic fissures. PMID:27265878

Quantitative volcanic susceptibility analysis of Lanzarote and Chinijo Islands based on kernel density estimation via a linear diffusion process.

PubMed

Galindo, I; Romero, M C; Sánchez, N; Morales, J M

2016-06-06

Risk management stakeholders in high-populated volcanic islands should be provided with the latest high-quality volcanic information. We present here the first volcanic susceptibility map of Lanzarote and Chinijo Islands and their submarine flanks based on updated chronostratigraphical and volcano structural data, as well as on the geomorphological analysis of the bathymetric data of the submarine flanks. The role of the structural elements in the volcanic susceptibility analysis has been reviewed: vents have been considered since they indicate where previous eruptions took place; eruptive fissures provide information about the stress field as they are the superficial expression of the dyke conduit; eroded dykes have been discarded since they are single non-feeder dykes intruded in deep parts of Miocene-Pliocene volcanic edifices; main faults have been taken into account only in those cases where they could modified the superficial movement of magma. The application of kernel density estimation via a linear diffusion process for the volcanic susceptibility assessment has been applied successfully to Lanzarote and could be applied to other fissure volcanic fields worldwide since the results provide information about the probable area where an eruption could take place but also about the main direction of the probable volcanic fissures.

Quantitative volcanic susceptibility analysis of Lanzarote and Chinijo Islands based on kernel density estimation via a linear diffusion process

NASA Astrophysics Data System (ADS)

Galindo, I.; Romero, M. C.; Sánchez, N.; Morales, J. M.

2016-06-01

Risk management stakeholders in high-populated volcanic islands should be provided with the latest high-quality volcanic information. We present here the first volcanic susceptibility map of Lanzarote and Chinijo Islands and their submarine flanks based on updated chronostratigraphical and volcano structural data, as well as on the geomorphological analysis of the bathymetric data of the submarine flanks. The role of the structural elements in the volcanic susceptibility analysis has been reviewed: vents have been considered since they indicate where previous eruptions took place; eruptive fissures provide information about the stress field as they are the superficial expression of the dyke conduit; eroded dykes have been discarded since they are single non-feeder dykes intruded in deep parts of Miocene-Pliocene volcanic edifices; main faults have been taken into account only in those cases where they could modified the superficial movement of magma. The application of kernel density estimation via a linear diffusion process for the volcanic susceptibility assessment has been applied successfully to Lanzarote and could be applied to other fissure volcanic fields worldwide since the results provide information about the probable area where an eruption could take place but also about the main direction of the probable volcanic fissures.

Semi-automated Approach to Mapping Sub-hectare Agricultural Fields using Very High Resolution Data in a High-Performance Computing Environment

NASA Astrophysics Data System (ADS)

Wooten, M.; Neigh, C. S. R.; Carroll, M.; McCarty, J. L.

2017-12-01

In areas susceptible to drought such as sub-Saharan Africa, Crop Area (CA) and agricultural mapping have become increasingly important as strain on natural ecosystems increases. In Ethiopia alone, the population has grown four-fold in the last 70 years, and rapidly growing human populations bring added stress to ecosystems as more wildlands are converted to pastures and subsistence agriculture. Monitoring change in agriculture is one of the more essential goals of famine early warning systems. However, due to the sub-hectare size of rainfed agricultural fields in regions such as Tigray, Ethiopia, moderate resolution satellite imagery is insufficient at capturing these smallholder farms. Thanks to the increasing density of observations and ease of access to very high resolution (VHR) data, we have developed a generalized method for mapping CA with VHR data and have used this to generate wall-to-wall CA map for the entire Tigray region and samples in Myanmar, Senegal, and Vietnam. Here we present the methodology and early results as well as potential future applications.

Susceptibility Evaluation and Mapping of CHINA'S Landslide Disaster Based on Multi-Temporal Ground and Remote Sensing Satellite Data

NASA Astrophysics Data System (ADS)

Liu, C.; Li, W.; Lu, P.; Sang, K.; Hong, Y.; Li, R.

2012-07-01

Under the circumstances of global climate change, nowadays landslide occurs in China more frequently than ever before. The landslide hazard and risk assessment remains an international focus on disaster prevention and mitigation. It is also an important approach for compiling and quantitatively characterizing landslide damages. By integrating empirical models for landslide disasters, and through multi-temporal ground data and remote sensing data, this paper will perform a landslide susceptibility assessment throughout China. A landslide susceptibility (LS) map will then be produced, which can be used for disaster evaluation, and provide basis for analyzing China's major landslide-affected regions. Firstly, based on previous research of landslide susceptibility assessment, this paper collects and analyzes the historical landslide event data (location, quantity and distribution) of past sixty years in China as a reference for late-stage studies. Secondly, this paper will make use of regional GIS data of the whole country provided by the National Geomatics Centre and China Meteorological Administration, including regional precipitation data, and satellite remote sensing data such as from TRMM and MODIS. By referring to historical landslide data of past sixty years, it is possible to develop models for assessing LS, including producing empirical models for prediction, and discovering both static and dynamic key factors, such as topography and landforms (elevation, curvature and slope), geologic conditions (lithology of the strata), soil type, vegetation cover, hydrological conditions (flow distribution). In addition, by analyzing historical data and combining empirical models, it is possible to synthesize a regional statistical model and perform a LS assessment. Finally, based on the 1km×1km grid, the LS map is then produced by ANN learning and multiplying the weighted factor layers. The validation is performed with reference to the frequency and distribution of historical data. This research reveals the spatiotemporal distribution of landslide disasters in China. The study develops a complete algorithm of data collecting, processing, modelling and synthesizing, which fulfils the assessment of landslide susceptibility, and provides theoretical basis for prediction and forecast of landslide disasters throughout China.

Evaluation of Renal Oxygenation Level Changes after Water Loading Using Susceptibility-Weighted Imaging and T2* Mapping.

PubMed

Ding, Jiule; Xing, Wei; Wu, Dongmei; Chen, Jie; Pan, Liang; Sun, Jun; Xing, Shijun; Dai, Yongming

2015-01-01

To assess the feasibility of susceptibility-weighted imaging (SWI) while monitoring changes in renal oxygenation level after water loading. Thirty-two volunteers (age, 28.0 ± 2.2 years) were enrolled in this study. SWI and multi-echo gradient echo sequence-based T2(*) mapping were used to cover the kidney before and after water loading. Cortical and medullary parameters were measured using small regions of interest, and their relative changes due to water loading were calculated based on baseline and post-water loading data. An intraclass correlation coefficient analysis was used to assess inter-observer reliability of each parameter. A receiver operating characteristic curve analysis was conducted to compare the performance of the two methods for detecting renal oxygenation changes due to water loading. Both medullary phase and medullary T2(*) values increased after water loading (p < 0.001), although poor correlations were found between the phase changes and the T2(*) changes (p > 0.05). Interobserver reliability was excellent for the T2(*) values, good for SWI cortical phase values, and moderate for the SWI medullary phase values. The area under receiver operating characteristic curve of the SWI medullary phase values was 0.85 and was not different from the medullary T2(*) value (0.84). Susceptibility-weighted imaging enabled monitoring changes in the oxygenation level in the medulla after water loading, and may allow comparable feasibility to detect renal oxygenation level changes due to water loading compared with that of T2(*) mapping.

Analysis and determination of susceptibility Risk from slope instability at Colima State Mexico due to the accelerators factors of rain and seismicity

NASA Astrophysics Data System (ADS)

Ramirez-Ruiz, J. J.

2016-12-01

Slope instability is presented each year in the mountain region of the Colima State, Mexico. It occurs due to the combination of different factors existing in this area as: Precipitation, topography contrast, type and mechanical properties of deposits that constitute the rocks and soils of the region and the erosion due to the elimination of vegetation deck to develop and grow urban areas. To these geological factors we can extend the tectonic activity of the Western part of Mexico that originate high seismicity by the interaction of Cocos plate and North America plate forming the region of Graben de Colima, were is located our study area. Here we will present a Zonification and determination of the Susceptibility maps of slope instability due to the rain and seismicity accelerators factors. The North part of the State Colima is covered by deposits of the Volcan de Colima with an elevation of 3860 masl. It is the area of major precipitation yearly with more than 1200 mm in comparison to the average precipitation of about 900 mm of the State of Colima. Using a SIG system and the mapping of more than 30 sites we realize a zonification and analysis of the Risk using a methodology developed by CENAPRED. The susceptibility map developed in this area in combination with erosion factors permit us to determine an approximation of the Risk considering some limitations that will be present in this study.

Sensitivity analysis of conditioning factors for landslide susceptibility evaluation in Santa Marta de Penaguiño (Douro valley - Portugal

NASA Astrophysics Data System (ADS)

Pereira, S.; Zêzere, J. L.; Bateira, C.

2009-04-01

The MapRisk project intends to develop a landslide hazard and risk analysis to support planning decisions at the municipal level. The municipality of Santa Marta de Penaguião (70 square kilometres) is one of the test sites of the project. The study area has been affected in recent years by destructive landslides that were responsible for deaths and house and roads destruction. Despite these losses, mitigation and zonation landslide programs are missing, and the land use planning at the municipal level did not solve yet the problem. The study area is located in the Douro Valley region, mainly composed by metamorphic rocks (e.g., schist and quartzites). These rocks are strongly fractured, and weathered materials are abundant in clayed schist, mainly in those areas with agricultural terraces. From the geomorphological point of view, the study area is characterized by a transition landscape between the Marão mountain and the transmontano plateau, with deep incised valleys, tectonic depressions and slopes controlled by the geological structure. This area is characterised by the vineyard monoculture cultivated in agricultural terraces over centuries to produce Oporto wine. The main landslide triggering factor is rainfall and the mean annual precipitation ranges from 2500 mm near Marão mountain to 700 mm in the Corgós Valley. In this area there are historical records of destructive landslides, although they were aggregated in a landslide geodatabase only recently. The most complete landslide inventory was performed in 2005-2008 using aerial photographs interpretation at 1/5.000 scale and field work verification. The geodatabase includes 725 landslides, most of shallow translational slide type (80% of total slope movements). The landslide density is 10.5 events/square kilometre, and the average landslide area is 535 square meters. In this work we present the results of GIS based landslide susceptibility assessment for the shallow translational slides using two bivariate statistical models: information value and fuzzy-logic. Success rate and prediction rate curves were calculated for susceptibility models performed with the total set of shallow translational slides, and using a landslide training group and a landslide test group, which were randomly defined, each corresponding to 50% of the complete landslide population. The obtained prediction rates are higher for the Information value method, in all experiences, when compared with the fuzzy logic. Using only the information value method, a sensitivity analysis was performed to identify the most important conditioning factors (8 spatial thematic layers) concerning the spatial distribution of shallow translational slides in the study area. Slope curvature, terrace structures build in slopes and lithology are the landslide conditioning factors that show the highest success rates. Susceptibility maps produced with different number of conditioning factors were tested and good results were achieved with only 4 factors (Slope curvature, terrace structures build in slopes, lithology and land use). The obtained results must be carefully analysed, because they result from few inventories and some landslides were erased in landscape due to reconstruction of terraces and roads, and seasonal soil ploughing. The land use changes rapidly in order to get more space for vineyard plantations and the slope structures that sustained the soil erosion are replaced for slopes without soil support structures. In this context, the conditioning factors and susceptibility maps have a short temporal validity and need to be regularly updated in response to changes in land use.

Fine mapping and characterization of candidate genes that control resistance to Cercospora Sojina K. Hara in two soybean germplasm accessions

USDA-ARS?s Scientific Manuscript database

In order to fine map the novel FLS resistance gene(s) in two PIs, PI 594891 and PI 594774, F2:3 seeds from the crosses Blackhawk (FLS susceptible genotype) ×PI 594891, and Blackhawk ×PI 594774 were genotyped with KASP markers that were designed based on the SoySNP 50k Infinium Chip data to identi...

A genomic map enriched for markers linked to Avr1 in Cronartium quercuum f.sp. fusiforme

Treesearch

Thomas L Kubisiak; Claire L Anderson; Henry V Amerson; Jason A Smith; John M Davis; C Dana Nelson

2011-01-01

A novel approach is presented to map avirulence gene Avr1 in the basidiomycete Cronartium quercuum f.sp. fusiforme, the causal agent of fusiform rust disease in pines. DNA markers tightly linked to resistance gene Fr1 in loblolly pine tree 10-5 were used to classify 10-5 seedling progeny as either resistant or susceptible. A single dikaryotic isolate (P2) heterozygous...

Shallow translational slides hazard evaluation in Santa Marta de Penaguião (Douro valley - Portugal)

NASA Astrophysics Data System (ADS)

Pereira, Susana; Luís Zêzere, José; Bateira, Carlos

2010-05-01

The present study is developed for the municipality of Santa Marta de Penaguião (70 square kilometers), located in the Douro Valley region (Northern Portugal). In the past, several destructive landslides occurred in this area, and were responsible for deaths and destruction of houses and roads. Despite these losses, mitigation and landslide zonation programs are missing, and the land use planning at the municipal level did not solve yet the problem. The study area is mainly composed by metamorphic rocks (e.g., schist and quartzite). These rocks are strongly fractured, and weathered materials are abundant in clayed schist, mainly in those areas where agricultural terraces were constructed centuries ago for the vineyard monoculture. From the geomorphologic point of view, the study area is characterized by deep incised valleys, tectonic depressions and slopes controlled by the geological structure. Elevation ranges from 49 m to 1416 m. The main landslide triggering factor is rainfall and the mean annual precipitation ranges from 700 mm (in the bottom of fluvial valleys) to 2500 mm (in the mountains top). A landslide inventory was performed in 2005-2009 using aerial photo-interpretation (1/5.000 scale) and field work. The inventory includes 848 landslides, most of shallow translational slide type (85% of total slope movements). The landslide density is 10.5 events/square kilometers, and the average landslide area is 535 square meters. The susceptibility to shallow translational slide occurrence was assessed at the 1: 10 000 scale in a GIS environment. Two different bivariate statistical methods were used to evaluate landslide susceptibility: the Information Value and the Fuzzy Logic Gamma operator. Eight conditioning factors were weighted and integrated to model susceptibility: slope angle, slope aspect, slope curvature, lithology, geomorphologic units, fault density, land use and terrace structures build in slopes. The susceptibility results were validated using a random partition of the total set of shallow translational slides in two groups (training group and validation group, which were randomly defined, each corresponding to 50% of the complete landslide population.). This strategy allows the independent validation of landslide susceptibility models and the construction of prediction rate curves. The best prediction results were obtained using the information value method (Area Under Curve - AUC = 0.78). The landslide susceptibility map was classified in 5 susceptibility classes using the slope breaks within the best prediction curve. The empirical probability for each class was also estimated. Landslide hazard was assessed based on empirical probabilities, using an instability scenario similar to the event occurred in January 2001, which generated 603 shallow translational slides with a total unstable area of 93,029 square meters. This landslide event was triggered by 1064 mm of cumulative rainfall in 90 days, having 18 years of return period. Therefore, we assume that future occurrence of such rainfall amount will generate the same consequences regarding slope instability in the study area (i.e., the same number of landslides and equivalent total unstable area). The landslide hazard was also calculated per year to allow hazard comparison with other areas. The obtained results have short temporal validity and must be carefully analyzed due to rapid changes in land use in order to get more space for vineyard plantations. In recent years, the slope structures which sustained the soil erosion have been replaced systematically by terraces without soil support structures. In this context, the conditioning factors, susceptibility and hazard maps need to be regularly reassessed.

Using Remotely Sensed Information for Near Real-Time Landslide Hazard Assessment

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Adler, Robert; Peters-Lidard, Christa

2013-01-01

The increasing availability of remotely sensed precipitation and surface products provides a unique opportunity to explore how landslide susceptibility and hazard assessment may be approached at larger spatial scales with higher resolution remote sensing products. A prototype global landslide hazard assessment framework has been developed to evaluate how landslide susceptibility and satellite-derived precipitation estimates can be used to identify potential landslide conditions in near-real time. Preliminary analysis of this algorithm suggests that forecasting errors are geographically variable due to the resolution and accuracy of the current susceptibility map and the application of satellite-based rainfall estimates. This research is currently working to improve the algorithm through considering higher spatial and temporal resolution landslide susceptibility information and testing different rainfall triggering thresholds, antecedent rainfall scenarios, and various surface products at regional and global scales.

Comparison and validation of shallow landslides susceptibility maps generated by bi-variate and multi-variate linear probabilistic GIS-based techniques. A case study from Ribeira Quente Valley (S. Miguel Island, Azores)

NASA Astrophysics Data System (ADS)

Marques, R.; Amaral, P.; Zêzere, J. L.; Queiroz, G.; Goulart, C.

2009-04-01

Slope instability research and susceptibility mapping is a fundamental component of hazard assessment and is of extreme importance for risk mitigation, land-use management and emergency planning. Landslide susceptibility zonation has been actively pursued during the last two decades and several methodologies are still being improved. Among all the methods presented in the literature, indirect quantitative probabilistic methods have been extensively used. In this work different linear probabilistic methods, both bi-variate and multi-variate (Informative Value, Fuzzy Logic, Weights of Evidence and Logistic Regression), were used for the computation of the spatial probability of landslide occurrence, using the pixel as mapping unit. The methods used are based on linear relationships between landslides and 9 considered conditioning factors (altimetry, slope angle, exposition, curvature, distance to streams, wetness index, contribution area, lithology and land-use). It was assumed that future landslides will be conditioned by the same factors as past landslides in the study area. The presented work was developed for Ribeira Quente Valley (S. Miguel Island, Azores), a study area of 9,5 km2, mainly composed of volcanic deposits (ash and pumice lapilli) produced by explosive eruptions in Furnas Volcano. This materials associated to the steepness of the slopes (38,9% of the area has slope angles higher than 35°, reaching a maximum of 87,5°), make the area very prone to landslide activity. A total of 1.495 shallow landslides were mapped (at 1:5.000 scale) and included in a GIS database. The total affected area is 401.744 m2 (4,5% of the study area). Most slope movements are translational slides frequently evolving into debris-flows. The landslides are elongated, with maximum length generally equivalent to the slope extent, and their width normally does not exceed 25 m. The failure depth rarely exceeds 1,5 m and the volume is usually smaller than 700 m3. For modelling purposes, the landslides were randomly divided in two sub-datasets: a modelling dataset with 748 events (2,2% of the study area) and a validation dataset with 747 events (2,3% of the study area). The susceptibility algorithms achieved with the different probabilistic techniques, were rated individually using success rate and prediction rate curves. The best model performance was obtained with the logistic regression, although the results from the different methods do not show significant differences neither in success nor in prediction rate curves. These evidences revealed that: (1) the modelling landslide dataset is representative of the entire landslide population characteristics; and (2) the increase of complexity and robustness in the probabilistic methodology did not produce a significant increase in success or prediction rates. Therefore, it was concluded that the resolution and quality of the input variables are much more important than the probabilistic model chosen to assess landslide susceptibility. This work was developed on the behalf of VOLCSOILRISK project (Volcanic Soils Geotechnical Characterization for Landslide Risk Mitigation), supported by Direcção Regional da Ciência e Tecnologia - Governo Regional dos Açores.

Integrated platform for genome-wide screening and construction of high-density genetic interaction maps in mammalian cells

PubMed Central

Kampmann, Martin; Bassik, Michael C.; Weissman, Jonathan S.

2013-01-01

A major challenge of the postgenomic era is to understand how human genes function together in normal and disease states. In microorganisms, high-density genetic interaction (GI) maps are a powerful tool to elucidate gene functions and pathways. We have developed an integrated methodology based on pooled shRNA screening in mammalian cells for genome-wide identification of genes with relevant phenotypes and systematic mapping of all GIs among them. We recently demonstrated the potential of this approach in an application to pathways controlling the susceptibility of human cells to the toxin ricin. Here we present the complete quantitative framework underlying our strategy, including experimental design, derivation of quantitative phenotypes from pooled screens, robust identification of hit genes using ultra-complex shRNA libraries, parallel measurement of tens of thousands of GIs from a single double-shRNA experiment, and construction of GI maps. We describe the general applicability of our strategy. Our pooled approach enables rapid screening of the same shRNA library in different cell lines and under different conditions to determine a range of different phenotypes. We illustrate this strategy here for single- and double-shRNA libraries. We compare the roles of genes for susceptibility to ricin and Shiga toxin in different human cell lines and reveal both toxin-specific and cell line-specific pathways. We also present GI maps based on growth and ricin-resistance phenotypes, and we demonstrate how such a comparative GI mapping strategy enables functional dissection of physical complexes and context-dependent pathways. PMID:23739767

Investigation of BOLD fMRI Resonance Frequency Shifts and Quantitative Susceptibility Changes at 7 T

PubMed Central

Bianciardi, Marta; van Gelderen, Peter; Duyn, Jeff H.

2013-01-01

Although blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) experiments of brain activity generally rely on the magnitude of the signal, they also provide frequency information that can be derived from the phase of the signal. However, because of confounding effects of instrumental and physiological origin, BOLD related frequency information is difficult to extract and therefore rarely used. Here, we explored the use of high field (7 T) and dedicated signal processing methods to extract frequency information and use it to quantify and interpret blood oxygenation and blood volume changes. We found that optimized preprocessing improves detection of task-evoked and spontaneous changes in phase signals and resonance frequency shifts over large areas of the cortex with sensitivity comparable to that of magnitude signals. Moreover, our results suggest the feasibility of mapping BOLD quantitative susceptibility changes in at least part of the activated area and its largest draining veins. Comparison with magnitude data suggests that the observed susceptibility changes originate from neuronal activity through induced blood volume and oxygenation changes in pial and intracortical veins. Further, from frequency shifts and susceptibility values, we estimated that, relative to baseline, the fractional oxygen saturation in large vessels increased by 0.02–0.05 during stimulation, which is consistent to previously published estimates. Together, these findings demonstrate that valuable information can be derived from fMRI imaging of BOLD frequency shifts and quantitative susceptibility changes. PMID:23897623

GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region

NASA Astrophysics Data System (ADS)

Kamp, Ulrich; Growley, Benjamin J.; Khattak, Ghazanfar A.; Owen, Lewis A.

2008-11-01

The Mw 7.6 October 8, 2005 Kashmir earthquake triggered several thousand landslides throughout the Himalaya of northern Pakistan and India. These were concentrated in six different geomorphic-geologic-anthropogenic settings. A spatial database, which included 2252 landslides, was developed and analyzed using ASTER satellite imagery and geographical information system (GIS) technology. A multi-criterion evaluation was applied to determine the significance of event-controlling parameters in triggering the landslides. The parameters included lithology, faults, slope gradient, slope aspect, elevation, land cover, rivers and roads. The results showed four classes of landslide susceptibility. Furthermore, they indicated that lithology had the strongest influence on landsliding, particularly when the rock is highly fractured, such as in shale, slate, clastic sediments, and limestone and dolomite. Moreover, the proximity of the landslides to faults, rivers, and roads was also an important factor in helping to initiate failures. In addition, landslides occurred particularly in moderate elevations on south facing slopes. Shrub land, grassland, and also agricultural land were highly susceptible to failures, while forested slopes had few landslides. One-third of the study area was highly or very highly susceptible to future landsliding and requires immediate mitigation action. The rest of the region had a low or moderate susceptibility to landsliding and remains relatively stable. This study supports the view that (1) earthquake-triggered landslides are concentrated in specific zones associated with event-controlling parameters; and (2) in the western Himalaya deforestation and road construction contributed significantly to landsliding during and shortly after earthquakes.

Quantitative susceptibility map analysis in preterm neonates with germinal matrix-intraventricular hemorrhage.

PubMed

Tortora, Domenico; Severino, Mariasavina; Sedlacik, Jan; Toselli, Benedetta; Malova, Mariya; Parodi, Alessandro; Morana, Giovanni; Fato, Marco Massimo; Ramenghi, Luca Antonio; Rossi, Andrea

2018-05-10

Germinal matrix-intraventricular hemorrhage (GMH-IVH) is a common form of intracranial hemorrhage occurring in preterm neonates that may affect normal brain development. Although the primary lesion is easily identified on MRI by the presence of blood products, its exact extent may not be recognizable with conventional sequences. Quantitative susceptibility mapping (QSM) quantify the spatial distribution of magnetic susceptibility within biological tissues, including blood degradation products. To evaluate magnetic susceptibility of normal-appearing white (WM) and gray matter regions in preterm neonates with and without GMH-IVH. Retrospective case-control. A total of 127 preterm neonates studied at term equivalent age: 20 had mild GMH-IVH (average gestational age 28.7 ± 2.1 weeks), 15 had severe GMH-IVH (average gestational age 29.3 ± 1.8 weeks), and 92 had normal brain MRI (average gestational age 29.8 ± 1.8 weeks). QSM at 1.5 Tesla. QSM analysis was performed for each brain hemisphere with a region of interest-based approach including five WM regions (centrum semiovale, frontal, parietal, temporal, and cerebellum), and a subcortical gray matter region (basal ganglia/thalami). Changes in magnetic susceptibility were explored using a one-way analysis of covariance, according to GMH-IVH severity (P < 0.05). In preterm neonates with normal brain MRI, all white and subcortical gray matter regions had negative magnetic susceptibility values (diamagnetic). Neonates with severe GMH-IVH showed higher positive magnetic susceptibility values (i.e. paramagnetic) in the centrum semiovale (0.0019 versus -0.0014 ppm; P < 0.001), temporal WM (0.0011 versus -0.0012 ppm; P = 0.037), and parietal WM (0.0005 versus -0.0001 ppm; P = 0.002) compared with controls. No differences in magnetic susceptibility were observed between neonates with mild GMH-IVH and controls (P = 0.236). Paramagnetic susceptibility changes occur in several normal-appearing WM regions of neonates with severe GMH-IVH, likely related to the accumulation of hemosiderin/ferritin iron secondary to diffusion of extracellular hemoglobin from the ventricle into the periventricular WM. 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Global Lithospheric Apparent Susceptibility Distribution Converted from Geomagnetic Models by CHAMP and Swarm Satellite Magnetic Measurements

NASA Astrophysics Data System (ADS)

Du, Jinsong; Chen, Chao; Xiong, Xiong; Li, Yongdong; Liang, Qing

2016-04-01

Recently, because of continually accumulated magnetic measurements by CHAMP satellite and Swarm constellation of three satellites and well developed methodologies and techniques of data processing and geomagnetic field modeling etc., global lithospheric magnetic anomaly field models become more and more reliable. This makes the quantitative interpretation of lithospheric magnetic anomaly field possible for having an insight into large-scale magnetic structures in the crust and uppermost mantle. Many different approaches have been utilized to understand the magnetized sources, such as forward, inversion, statistics, correlation analysis, Euler deconvolution, signal transformations etc. Among all quantitative interpretation methods, the directly converting a magnetic anomaly map into a magnetic susceptibility anomaly map proposed by Arkani-Hamed & Strangway (1985) is, we think, the most fast quantitative interpretation tool for global studies. We just call this method AS85 hereinafter for short. Although Gubbins et al. (2011) provided a formula to directly calculate the apparent magnetic vector distribution, the AS85 method introduced constraints of magnetized direction and thus corresponding results are expected to be more robust especially in world-wide continents. Therefore, in this study, we first improved the AS85 method further considering non-axial dipolar inducing field using formulae by Nolte & Siebert (1987), initial model or priori information for starting coefficients in the apparent susceptibility conversion, hidden longest-wavelength components of lithospheric magnetic field and field contaminations from global oceanic remanent magnetization. Then, we used the vertically integrated susceptibility model by Hemant & Maus (2005) and vertically integrated remanent magnetization model by Masterton et al. (2013) to test the validity of our improved method. Subsequently, we applied the conversion method to geomagnetic field models by CHAMP and Swarm satellite magnetic measurements and obtained global lithospheric apparent susceptibility distribution models. Finally, we compared these deduced models with previous results in the literature and some other geophysical, geodetic and geologic datum. Both tests and applications suggest, indeed, that the improved AS85 method can be adopted as a fast and effective interpretation tool of global induced large-scale magnetic anomaly field models in form of spherical harmonics. Arkani-Hamed, J. & Srangway, D.W., 1985. Lateral variations of apparent magnetic susceptibility of lithosphere deduced from Magsat data, J. Geophys. Res., 90(B3), 2655-2664. Gubbins, D., Ivers, D., Masterton, S.M. & Winch, D.E., 2011. Analysis of lithospheric magnetization in vector spherical harmonics, Geophys. J. Int., 187(1), 99-117. Hemant, K. & Maus, S., 2005. Geological modeling of the new CHAMP magnetic anomaly maps using a geographical information system technique, J. Geophys. Res., 110, B12103, doi: 10.1029/2005JB003837. Masterton, S.M., Gubbins, D., Müller, R.D. & Singh, K.H., 2013. Forward modeling of oceanic lithospheric magnetization, Geophys. J. Int., 192(3), 951-962. Nolte, H.J. & Siebert, M., 1987. An analytical approach to the magnetic field of the Earth's crust, J. Geophys., 61, 69-76. This study is supported by State Key Laboratory of Geodesy and Earth's Dynamics (Institute of Geodesy and Geophysics, Chinese Academy of Sciences) (SKLGED2015-5-5-EZ), Natural Science Fund of Hubei Province (2015CFB361), International Cooperation Project in Science and Technology of China (2010DFA24580), China Postdoctoral Science Foundation (2015M572217 and 2014T70753), Hubei Subsurface Multi-scale Imaging Key Laboratory (Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan) (SMIL-2015-06) and National Natural Science Foundation of China (41574070, 41104048 and 41504065).

Cellular entry via an actin and clathrin-dependent route is required for Lv2 restriction of HIV-2.

PubMed

Harrison, I P; McKnight, A

2011-06-20

Lv2 is a human factor that restricts infection of some HIV-2 viruses after entry into particular target cells. HIV-2 MCR is highly susceptible to Lv2 whereas HIV-2 MCN is not. The block is after reverse transcription but prior to nuclear entry. The viral determinants for this restriction have been mapped to the HIV-2 envelope and the capsid genes. Our model of Lv2 restriction suggests that the route taken into a cell is important in determining whether a productive infection occurs. Here we characterised the infectious routes used by MCN and MCR using chemical compounds and molecular techniques to distinguish between potential pathways. Our results suggest that susceptible MCR can enter restrictive HeLa(CD4) cells via two pathways; a clathrin/AP2 mediated endocytic route that is sensitive to Lv2 restriction and an alternative, non-clathrin mediated route, which results in more efficient infection. Copyright © 2011 Elsevier Inc. All rights reserved.

Integrating statistical and process-based models to produce probabilistic landslide hazard at regional scale

NASA Astrophysics Data System (ADS)

Strauch, R. L.; Istanbulluoglu, E.

2017-12-01

We develop a landslide hazard modeling approach that integrates a data-driven statistical model and a probabilistic process-based shallow landslide model for mapping probability of landslide initiation, transport, and deposition at regional scales. The empirical model integrates the influence of seven site attribute (SA) classes: elevation, slope, curvature, aspect, land use-land cover, lithology, and topographic wetness index, on over 1,600 observed landslides using a frequency ratio (FR) approach. A susceptibility index is calculated by adding FRs for each SA on a grid-cell basis. Using landslide observations we relate susceptibility index to an empirically-derived probability of landslide impact. This probability is combined with results from a physically-based model to produce an integrated probabilistic map. Slope was key in landslide initiation while deposition was linked to lithology and elevation. Vegetation transition from forest to alpine vegetation and barren land cover with lower root cohesion leads to higher frequency of initiation. Aspect effects are likely linked to differences in root cohesion and moisture controlled by solar insulation and snow. We demonstrate the model in the North Cascades of Washington, USA and identify locations of high and low probability of landslide impacts that can be used by land managers in their design, planning, and maintenance.

Low cortical iron and high entorhinal cortex volume promote cognitive functioning in the oldest-old.

PubMed

van Bergen, Jiri M G; Li, Xu; Quevenco, Frances C; Gietl, Anton F; Treyer, Valerie; Leh, Sandra E; Meyer, Rafael; Buck, Alfred; Kaufmann, Philipp A; Nitsch, Roger M; van Zijl, Peter C M; Hock, Christoph; Unschuld, Paul G

2018-04-01

The aging brain is characterized by an increased presence of neurodegenerative and vascular pathologies. However, there is substantial variation regarding the relationship between an individual's pathological burden and resulting cognitive impairment. To identify correlates of preserved cognitive functioning at highest age, the relationship between β-amyloid plaque load, presence of small vessel cerebrovascular disease (SVCD), iron-burden, and brain atrophy was investigated. Eighty cognitively unimpaired participants (44 oldest-old, aged 85-96 years; 36 younger-old, aged 55-80 years) were scanned by integrated positron emission tomography-magnetic resonance imaging for assessing beta regional amyloid plaque load (18F-flutemetamol), white matter hyperintensities as an indicator of SVCD (fluid-attenuated inversion recovery-magnetic resonance imaging), and iron load (quantitative susceptibility mapping). For the oldest-old group, lower cortical volume, increased β-amyloid plaque load, prevalence of SVCD, and lower cognitive performance in the normal range were found. However, compared to normal-old, cortical iron burden was lower in the oldest-old. Moreover, only in the oldest-old, entorhinal cortex volume positively correlated with β-amyloid plaque load. Our data thus indicate that the co-occurrence of aging-associated neuropathologies with reduced quantitative susceptibility mapping measures of cortical iron load constitutes a lower vulnerability to cognitive loss. Copyright © 2017 Elsevier Inc. All rights reserved.

An agent-based model evaluation of economic control strategies for paratuberculosis in a dairy herd.

PubMed

Verteramo Chiu, Leslie J; Tauer, Loren W; Al-Mamun, Mohammad A; Kaniyamattam, Karun; Smith, Rebecca L; Grohn, Yrjo T

2018-04-25

This paper uses an agent-based simulation model to estimate the costs associated with Mycobacterium avium ssp. paratuberculosis (MAP), or Johne's disease, in a milking herd, and to determine the net benefits of implementing various control strategies. The net present value (NPV) of a 1,000-cow milking herd is calculated over 20 yr, parametrized to a representative US commercial herd. The revenues of the herd are generated from sales of milk and culled animals. The costs include all variable and fixed costs necessary to operate a representative 1,000-cow milking herd. We estimate the NPV of the herd with no MAP infection, under an expected endemic infection distribution with no controls, and under an expected endemic infection distribution with various controls. The initial number of cows in a herd with an endemic MAP infection is distributed as 75% susceptible, 13% latent, 9% low MAP shedding, and 3% high MAP shedding. Control strategies include testing using ELISA and fecal culture tests and culling of cows that test positive, and culling based on observable milk production decrease. Results show that culling cows based on test results does not increase the herd's NPV and in most cases decreases NPV due to test costs as well as false positives and negatives with their associated costs (e.g., culling healthy cows and keeping infected cows). Culling consistently low producing cows when MAP is believed to be present in the herd produces higher NPV over the strategy of testing and culling MAP infected animals, and over the case of no MAP control. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Landslide databases review in the Geological Surveys of Europe

NASA Astrophysics Data System (ADS)

Herrera, Gerardo

2017-04-01

Landslides are one of the most widespread geohazards in Europe, producing significant social and economic damages. Rapid population growth in urban areas throughout many countries in Europe and extreme climatic scenarios can considerably increase landslide risk in the near future. However, many European countries do not include landslide risk into their legislation. Countries lack official methodological assessment guidelines and knowledge about landslide impacts. Although regional and national landslide databases exist in most countries, they are often not integrated because they are owed by different institutions. Hence, a European Landslides Directive, that provides a common legal framework for dealing with landslides, is necessary. With this long-term goal in mind, we present a review of the landslide databases from the Geological Surveys of Europe focusing on their interoperability. The same landslide classification was used for the 849,543 landslide records from the Geological Surveys, from which 36% are slides, 10 % falls, 20% flows, 11% complex slides and 24% remain either unclassified or correspond to another typology. A landslide density map was produced from the available records of the Geological Surveys of 17 countries showing the variable distribution of landslides. There are 0.2 million km2 of landslide prone areas. The comparison of this map with the European landslide susceptibility map ELSUS v1 was successful for 73% of the predictions, and permitted identification of 25% of susceptible areas where landslide records are not available from the Geological Surveys. Taking these results into account the completeness of these landslide databases was evaluated, revealing different landslide hazard management approaches between surveys and countries.

Application of PALSAR-2 Remote Sensing Data for Landslide Hazard Mapping in Kelantan River Basin, Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Beiranvand Pour, Amin; Hashim, Mazlan

2016-06-01

Yearly, several landslides ensued during heavy monsoons rainfall in Kelantan river basin, peninsular Malaysia, which are obviously connected to geological structures and topographical features of the region. In this study, the recently launched Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), remote sensing data were used to map geological structural and topographical features in the Kelantan river basin for identification of high potential risk and susceptible zones for landslides. Adaptive Local Sigma filter was selected and applied to accomplish speckle reduction and preserving both edges and features in PALSAR-2 fine mode observation images. Different polarization images were integrated to enhance geological structures. Additionally, directional filters were applied to the PALSAR-2 Local Sigma resultant image for edge enhancement and detailed identification of linear features. Several faults, drainage patterns and lithological contact layers were identified at regional scale. In order to assess the results, fieldwork and GPS survey were conducted in the landslide affected zones in the Kelantan river basin. Results demonstrate the most of the landslides were associated with N-S, NNW-SSE and NE-SW trending faults, angulated drainage pattern and metamorphic and Quaternary units. Consequently, structural and topographical geology maps were produced for Kelantan river basin using PALSAR-2 data, which could be broadly applicable for landslide hazard mapping.

Predicting the impact of lava flows at Mount Etna by an innovative method based on Cellular Automata: Applications regarding land-use and civil defence planning

NASA Astrophysics Data System (ADS)

Crisci, G. M.; Avolio, M. V.; D'Ambrosio, D.; di Gregorio, S.; Lupiano, G. V.; Rongo, R.; Spataro, W.; Benhcke, B.; Neri, M.

2009-04-01

Forecasting the time, character and impact of future eruptions is difficult at volcanoes with complex eruptive behaviour, such as Mount Etna, where eruptions occur from the summit and on the flanks, affecting areas distant from each other. Modern efforts for hazard evaluation and contingency planning in volcanic areas draw heavily on hazard maps and numerical simulations. The computational model here applied belongs to the SCIARA family of lava flow simulation models. In the specific case this is the SCIARA-fv release, which is considered to give the most accurate and efficient performance, given the extent (567 km2) of the study area and the great number of simulations to be carried out. The model is based on the Cellular Automata computational paradigm and, specifically, on the Macroscopic Cellular Automata approach for the modelling of spatially extended dynamic systems2. This work addresses the problem of compiling high-detailed susceptibility maps with an elaborate approach in the numerical simulation of Etnean lava flows, based on the results of 39,300 simulations of flows erupted from a grid of 393 hypothetical vents in the eastern sector of Etna. This sector was chosen because it is densely populated and frequently affected by flank eruptions. Besides the definition of general susceptibility maps, the availability of a large number of lava flows of different eruption types, magnitudes and locations simulated for this study allows the instantaneous extraction of various scenarios on demand. For instance, in a Civil Defence oriented application, it is possible to identify all source areas of lava flows capable of affecting a given area of interest, such as a town or a major infrastructure. Indeed, this application is rapidly accomplished by querying the simulation database, by selecting the lava flows that affect the area of interest and by circumscribing their sources. Eventually, a specific category of simulation is dedicated to the assessment of protective measures, such as earth barriers, for mitigating lava invasion susceptibility in given areas. For the case if the town of Nicolosi, results show that the barrier would be necessary to effectively protect the town centre. The methodology here described can therefore represent a substantial advance in the field of lava flows impact prediction and can also have immediate, far-reaching implications both in land-use and civil defence planning.

Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping

DOE PAGES

Pekin, Thomas C.; Gammer, Christoph; Ciston, Jim; ...

2017-01-28

Scanning nanobeam electron diffraction strain mapping is a technique by which the positions of diffracted disks sampled at the nanoscale over a crystalline sample can be used to reconstruct a strain map over a large area. However, it is important that the disk positions are measured accurately, as their positions relative to a reference are directly used to calculate strain. Here in this study, we compare several correlation methods using both simulated and experimental data in order to directly probe susceptibility to measurement error due to non-uniform diffracted disk illumination structure. We found that prefiltering the diffraction patterns with amore » Sobel filter before performing cross correlation or performing a square-root magnitude weighted phase correlation returned the best results when inner disk structure was present. Lastly, we have tested these methods both on simulated datasets, and experimental data from unstrained silicon as well as a twin grain boundary in 304 stainless steel.« less

MAGSAT investigation of crustal magnetic anomalies in the eastern Indian Ocean

NASA Technical Reports Server (NTRS)

Sailor, R. V.; Lazarewicz, A. R.

1983-01-01

Crustal magnetic anomalies in a region of the eastern Indian Ocean were studied using data from NASA's MAGSAT mission. The investigation region (0 deg to 50 deg South, 75 to 125 deg East) contains several important tectonic features, including the Broken Ridge, Java Trench, Ninetyeast Ridge, and Southeast Indian Ridge. A large positive magnetic anomaly is associated with the Broken Ridge and smaller positive anomalies correlate with the Ninetyeast Ridge and western Australia. Individual profiles of scalar data (computed from vector components) were considered to determine the overall data quality and resolution capability. A set of MAGSAT ""Quiet-Time'' data was used to compute an equivalent source crustal magnetic anomaly map of the study region. Maps of crustal magnetization and magnetic susceptibility were computed from the equivalent source dipoles. Gravity data were used to help interpretation, and a map of the ratio of magnetization to density contrasts was computed using Poisson's relation. The results are consistent with the hypothesis of induced magnetization of a crustal layer having varying thickness and composition.

New method for assessing the susceptibility of glacial lakes to outburst floods in the Cordillera Blanca, Peru

NASA Astrophysics Data System (ADS)

Emmer, A.; Vilímek, V.

2014-09-01

This paper presents a new and easily repeatable method for assessing the susceptibility of glacial lakes to outburst floods (GLOFs) within the Peruvian region of the Cordillera Blanca. The presented method was designed to: (a) be repeatable (from the point of view of the demands on input data), (b) be reproducible (to provide an instructive guide for different assessors), (c) provide multiple results for different GLOF scenarios and (d) be regionally focused on the lakes of the Cordillera Blanca. Based on the input data gained from remotely sensed images and digital terrain models/topographical maps, the susceptibility of glacial lakes to outburst floods is assessed using a combination of decision trees for clarity and numerical calculation for repeatability and reproducibility. A total of seventeen assessed characteristics are used, of which seven have not been used in this context before. Also, several ratios and calculations are defined for the first time. We assume that it is not relevant to represent the overall susceptibility of a particular lake to outburst floods by one result (number), thus it is described in the presented method by five separate results (representing five different GLOF scenarios). These are potentials for (a) dam overtopping resulting from a fast slope movement into the lake, (b) dam overtopping following the flood wave originating in a lake situated upstream, (c) dam failure resulting from a fast slope movement into the lake, (d) dam failure following the flood wave originating in a lake situated upstream and (e) dam failure following a strong earthquake. All of these potentials include two or three components and theoretically range from 0 to 1. The presented method was verified on the basis of assessing the pre-flood conditions of seven lakes which have produced ten glacial lake outburst floods in the past and ten lakes which have not. A comparison of these results showed that the presented method successfully identified lakes susceptible to outburst floods (pre-flood conditions of lakes which have already produced GLOFs).

Simulation of groundwater flow in the glacial aquifer system of northeastern Wisconsin with variable model complexity

USGS Publications Warehouse

Juckem, Paul F.; Clark, Brian R.; Feinstein, Daniel T.

2017-05-04

The U.S. Geological Survey, National Water-Quality Assessment seeks to map estimated intrinsic susceptibility of the glacial aquifer system of the conterminous United States. Improved understanding of the hydrogeologic characteristics that explain spatial patterns of intrinsic susceptibility, commonly inferred from estimates of groundwater age distributions, is sought so that methods used for the estimation process are properly equipped. An important step beyond identifying relevant hydrogeologic datasets, such as glacial geology maps, is to evaluate how incorporation of these resources into process-based models using differing levels of detail could affect resulting simulations of groundwater age distributions and, thus, estimates of intrinsic susceptibility.This report describes the construction and calibration of three groundwater-flow models of northeastern Wisconsin that were developed with differing levels of complexity to provide a framework for subsequent evaluations of the effects of process-based model complexity on estimations of groundwater age distributions for withdrawal wells and streams. Preliminary assessments, which focused on the effects of model complexity on simulated water levels and base flows in the glacial aquifer system, illustrate that simulation of vertical gradients using multiple model layers improves simulated heads more in low-permeability units than in high-permeability units. Moreover, simulation of heterogeneous hydraulic conductivity fields in coarse-grained and some fine-grained glacial materials produced a larger improvement in simulated water levels in the glacial aquifer system compared with simulation of uniform hydraulic conductivity within zones. The relation between base flows and model complexity was less clear; however, the relation generally seemed to follow a similar pattern as water levels. Although increased model complexity resulted in improved calibrations, future application of the models using simulated particle tracking is anticipated to evaluate if these model design considerations are similarly important for understanding the primary modeling objective - to simulate reasonable groundwater age distributions.

3D modelling of mechanical peat properties in the Holocene coastal-deltaic sequence of the Netherlands

NASA Astrophysics Data System (ADS)

Koster, Kay; Stouthamer, Esther; Cohen, Kim; Stafleu, Jan; Busschers, Freek; Middelkoop, Hans

2016-04-01

Peat is abundantly present within the Holocene coastal-deltaic sequence of the Netherlands, where it is alternating with clastic fluvial, estuarine and lagoonal deposits. The areas that are rich in peat are vulnerable to land subsidence, resulting from consolidation and oxidation, due to loading by overlying deposits, infrastructure and buildings, as well as excessive artificial drainage. The physical properties of the peat are very heterogeneous, with variable clastic admixture up to 80% of its mass and rapid decrease in porosity with increasing effective stress. Mapping the spatial distribution of the peat properties is essential for identifying areas most susceptible to future land subsidence, as mineral content determines volume loss by oxidation, and porosity influences the rate of consolidation. Here we present the outline of a study focusing on mapping mechanical peat properties in relation to density and amount of admixed clastic constituents of Holocene peat layers (in 3D). In this study we use a staged approach: 1) Identifying soil mechanical properties in two large datasets that are managed by Utrecht University and the Geological Survey. 2) Determining relations between these properties and palaeogeographical development of the area by evaluating these properties against known geological concepts such as distance to clastic source (river, estuary etc.). 3) Implementing the obtained relations in GeoTOP, which is a 3D geological subsurface model of the Netherlands developed by the Geological Survey. The model will be used, among others, to assess the susceptibility of different areas to peat related land subsidence and load bearing capacity of the subsurface. So far, our analysis has focused stage 1, by establishing empirical relations between mechanical peat properties in ~70 paired (piezometer) cone penetration tests and continuously cored boreholes with LOI measurements. Results show strong correlations between net cone resistance (qn), excess pore water (u1-u0), and total vertical stress (σvo), suggesting that the overburden strongly controls the vertical differential susceptibility of peat layers to consolidation.

The high-osmolarity glycerol- and cell wall integrity-MAP kinase pathways of Saccharomyces cerevisiae are involved in adaptation to the action of killer toxin HM-1.

PubMed

Miyamoto, Masahiko; Furuichi, Yasuhiro; Komiyama, Tadazumi

2012-11-01

Fps1p is an aquaglyceroporin important for turgor regulation of Saccharomyces cerevisiae. Previously we reported the involvement of Fps1p in the yeast-killing action of killer toxin HM-1. The fps1 cells showed a high HM-1-resistant phenotype in hypotonic medium and an HM-1-susceptible phenotype in hypertonic medium. This osmotic dependency in HM-1 susceptibility was similar to those observed in Congo red, but different from those observed in other cell wall-disturbing agents. These results indicate that HM-1 exerts fungicidal activity mainly by binding and inserting into the yeast cell wall structure, rather than by inhibiting 1,3-β-glucan synthase. We next determined HM-1-susceptibility and diphospho-MAP kinase inductions in S. cerevisiae. In the wild-type cell, expressions of diphospho-Hog1p and -Slt2p, and mRNA transcription of CWP1 and HOR2, were induced within 1 h after an addition of HM-1. ssk1 and pbs2 cells, but not sho1 and hkr1 cells, showed HM-1-sensitive phenotypes and lacked inductions of phospho-Hog1p in response to HM-1. mid2, rom2 and bck1 cells showed HM-1-sensitive phenotypes and decreased inductions of phospho-Slt2p in response to HM-1. From these results, we postulated that the Sln1-Ypd1-Ssk1 branch of the high-osmolality glycerol (HOG) pathway and plasma membrane sensors of the cell wall integrity (CWI) pathway detect cell wall stresses caused by HM-1. We further suggested that activations of both HOG and CWI pathways have an important role in the adaptive response to HM-1 toxicity. Copyright © 2012 John Wiley & Sons, Ltd.

Application of PALSAR-2 remote sensing data for structural geology and topographic mapping in Kelantan river basin, Malaysia

NASA Astrophysics Data System (ADS)

Beiranvand Pour, Amin; Hashim, Mazlan

2016-06-01

Natural hazards of geological origin are one of major problem during heavy monsoons rainfall in Kelantan state, peninsular Malaysia. Several landslides occur in this region are obviously connected to geological and topographical features, every year. Satellite synthetic aperture radar (SAR) data are particularly applicable for detection of geological structural and topographical features in tropical conditions. In this study, Phased Array type L-band Synthetic Aperture Radar (PALSAR-2), remote sensing data were used to identify high potential risk and susceptible zones for landslide in the Kelantan river basin. Adaptive Local Sigma filter was selected and applied to accomplish speckle reduction and preserving both edges and features in PALSAR-2 fine mode observation images. Different polarization images were integrated to enhance geological structures. Additionally, directional filters were applied to the PALSAR-2 Local Sigma resultant image for edge enhancement and detailed identification of linear features. Several faults, drainage patterns and lithological contact layers were identified at regional scale. In order to assess the results, fieldwork and GPS survey were conducted in the landslide affected zones in the Kelantan river basin. Results demonstrate the most of the landslides were associated with N-S, NNW-SSE and NE-SW trending faults, angulate drainage pattern and metamorphic and Quaternary units. Consequently, geologic structural map were produced for Kelantan river basin using recent PALSAR-2 data, which could be broadly applicable for landslide hazard assessment and delineation of high potential risk and susceptible areas. Landslide mitigation programmes could be conducted in the landslide recurrence regions for reducing catastrophes leading to economic losses and death.

Ground-water conditions in the Dutch Flats area, Scotts Bluff and Sioux Counties, Nebraska, with a section on chemical quality of the ground water

USGS Publications Warehouse

Babcock, H.M.; Visher, F.N.; Durum, W.H.

1951-01-01

The U.S. Department of the Interior (DOI) studied contamination induced by irrigation drainage in 26 areas of the Western United States during 1986-95. Comprehensive compilation, synthesis, and evaluation of the data resulting from these studies were initiated by DOI in 1992. Soils and ground water in irrigated areas of the West can contain high concentrations of selenium because of (1) residual selenium from the soil's parent rock beneath irrigated land; (2) selenium derived from rocks in mountains upland from irrigated land by erosion and transport along local drainages, and (3) selenium brought into the area in surface water imported for irrigation. Application of irrigation water to seleniferous soils can dissolve and mobilize selenium and create hydraulic gradients that cause the discharge of seleniferous ground water into irrigation drains. Given a source of selenium, the magnitude of selenium contamination in drainage-affected aquatic ecosystems is strongly related to the aridity of the area and the presence of terminal lakes and ponds. Marine sedimentary rocks and deposits of Late Cretaceous or Tertiary age are generally seleniferous in the Western United States. Depending on their origin and history, some Tertiary continental sedimentary deposits also are seleniferous. Irrigation of areas associated with these rocks and deposits can result in concentrations of selenium in water that exceed criteria for the protection of freshwater aquatic life. Geologic and climatic data for the Western United States were evaluated and incorporated into a geographic information system (GIS) to produce a map identifying areas susceptible to irrigation-induced selenium contamination. Land is considered susceptible where a geologic source of selenium is in or near the area and where the evaporation rate is more than 2.5 times the precipitation rate. In the Western United States, about 160,000 square miles of land, which includes about 4,100 square miles (2.6 million acres) of land irrigated for agriculture, has been identified as being susceptible. Biological data were used to evaluate the reliability of the map. In 12 of DOI's 26 study areas, concentrations of selenium measured in bird eggs were elevated sufficiently to significantly reduce hatchability of the eggs. The GIS map identifies 9 of those 12 areas. Deformed bird embryos having classic symptoms of selenium toxicosis were found in four of the study areas, and the map identifies all four as susceptible to irrigation-induced selenium contamination. The report describes the geography, geology, and ground-water resources of the Dutch Flats area in Scotts Bluff and Sioux Counties, Nebr. The area comprises about 60 square miles and consists predominantly of relatively flat-lying terraces. Farming is the principal occupation in the area. The farm lands are irrigated largely from surface water; ground water is used only as a supplementary supply during drought periods. The climate in the area is semiarid, and the mean annual precipitation is about 16 inches. The rocks exposed in the Dutch Flats area are of Tertiary sad Quaternary age. A map showing the areas of outcrop of the rock formations is included in the report. Sufficient unconfined ground water for irrigation supplies is contained in the deposits of the .third terrace, and wells that yield 1,000 to 2,000 gallons a minute probably could be developed. The depth to water in the area ranges from a few feet to about 80 feet sad averages about 30 feet. The depth to water varies throughout the year; it is least in the late summer when the recharge from irrigation is greatest, sad it is greatest in the early spring before irrigation is begun. A map showing the depth to water in September 1949 is included in the report. The ground-water reservoir is recharged by seepage from irrigation canals and laterals, by seepage from irrigation water applied to the farms, and, to a much lesser extent, by precipitation. In the area b

Assessment of Debris Flow Potential Hazardous Zones Using Numerical Models in the Mountain Foothills of Santiago, Chile

NASA Astrophysics Data System (ADS)

Celis, C.; Sepulveda, S. A.; Castruccio, A.; Lara, M.

2017-12-01

Debris and mudflows are some of the main geological hazards in the mountain foothills of Central Chile. The risk of flows triggered in the basins of ravines that drain the Andean frontal range into the capital city, Santiago, increases with time due to accelerated urban expansion. Susceptibility assessments were made by several authors to detect the main active ravines in the area. Macul and San Ramon ravines have a high to medium debris flow susceptibility, whereas Lo Cañas, Apoquindo and Las Vizcachas ravines have a medium to low debris flow susceptibility. This study emphasizes in delimiting the potential hazardous zones using the numerical simulation program RAMMS-Debris Flows with the Voellmy model approach, and the debris-flow model LAHARZ. This is carried out by back-calculating the frictional parameters in the depositional zone with a known event as the debris and mudflows in Macul and San Ramon ravines, on May 3rd, 1993, for the RAMMS approach. In the same scenario, we calibrate the coefficients to match conditions of the mountain foothills of Santiago for the LAHARZ model. We use the information obtained for every main ravine in the study area, mainly for the similarity in slopes and material transported. Simulations were made for the worst-case scenario, caused by the combination of intense rainfall storms, a high 0°C isotherm level and material availability in the basins where the flows are triggered. The results show that the runout distances are well simulated, therefore a debris-flow hazard map could be developed with these models. Correlation issues concerning the run-up, deposit thickness and transversal areas are reported. Hence, the models do not represent entirely the complexity of the phenomenon, but they are a reliable approximation for preliminary hazard maps.

A combined triggering-propagation modeling approach for the assessment of rainfall induced debris flow susceptibility

NASA Astrophysics Data System (ADS)

Stancanelli, Laura Maria; Peres, David Johnny; Cancelliere, Antonino; Foti, Enrico

2017-07-01

Rainfall-induced shallow slides can evolve into debris flows that move rapidly downstream with devastating consequences. Mapping the susceptibility to debris flow is an important aid for risk mitigation. We propose a novel practical approach to derive debris flow inundation maps useful for susceptibility assessment, that is based on the integrated use of DEM-based spatially-distributed hydrological and slope stability models with debris flow propagation models. More specifically, the TRIGRS infiltration and infinite slope stability model and the FLO-2D model for the simulation of the related debris flow propagation and deposition are combined. An empirical instability-to-debris flow triggering threshold calibrated on the basis of observed events, is applied to link the two models and to accomplish the task of determining the amount of unstable mass that develops as a debris flow. Calibration of the proposed methodology is carried out based on real data of the debris flow event occurred on 1 October 2009, in the Peloritani mountains area (Italy). Model performance, assessed by receiver-operating-characteristics (ROC) indexes, evidences fairly good reproduction of the observed event. Comparison with the performance of the traditional debris flow modeling procedure, in which sediment and water hydrographs are inputed as lumped at selected points on top of the streams, is also performed, in order to assess quantitatively the limitations of such commonly applied approach. Results show that the proposed method, besides of being more process-consistent than the traditional hydrograph-based approach, can potentially provide a more accurate simulation of debris-flow phenomena, in terms of spatial patterns of erosion and deposition as well on the quantification of mobilized volumes and depths, avoiding overestimation of debris flow triggering volume and, thus, of maximum inundation flow depths.

Detailed magnetic resonance imaging features of a case series of primary gliosarcoma.

PubMed

Sampaio, Luísa; Linhares, Paulo; Fonseca, José

2017-12-01

Objective We aimed to characterise the magnetic resonance imaging (MRI) features of a case series of primary gliosarcoma, with the inclusion of diffusion-weighted imaging and perfusion imaging with dynamic susceptibility contrast MRI. Materials and methods We conducted a retrospective study of cases of primary gliosarcoma from the Pathology Department database from January 2006 to December 2014. Clinical and demographic data were obtained. Two neuroradiologists, blinded to diagnosis, assessed tumour location, signal intensity in T1 and T2-weighted images, pattern of enhancement, diffusion-weighted imaging and dynamic susceptibility contrast MRI studies on preoperative MRI. Results Seventeen patients with primary gliosarcomas had preoperative MRI study: seven men and 10 women, with a mean age of 59 years (range 27-74). All lesions were well demarcated, supratentorial and solitary (frontal n = 5, temporal n = 4, parietal n = 3); 13 tumours abutted the dural surface (8/13 with dural enhancement); T1 and T2-weighted imaging patterns were heterogeneous and the majority of lesions (12/17) showed a rim-like enhancement pattern with focal nodularities/irregular thickness. Restricted diffusion (mean apparent diffusion coefficient values 0.64 × 10 -3 mm 2 /s) in the more solid/thick components was present in eight out of 11 patients with diffusion-weighted imaging study. Dynamic susceptibility contrast MRI study ( n = 8) consistently showed hyperperfusion in non-necrotic/cystic components on relative cerebral volume maps. Conclusions The main distinguishing features of primary gliosarcoma are supratentorial and peripheral location, well-defined boundaries and a rim-like pattern of enhancement with an irregular thick wall. Diffusion-weighted imaging and relative cerebral volume map analysis paralleled primary gliosarcoma with high-grade gliomas, thus proving helpful in differential diagnosis.

Comparative Genomic Analyses of the Human NPHP1 Locus Reveal Complex Genomic Architecture and Its Regional Evolution in Primates

PubMed Central

Yuan, Bo; Liu, Pengfei; Gupta, Aditya; Beck, Christine R.; Tejomurtula, Anusha; Campbell, Ian M.; Gambin, Tomasz; Simmons, Alexandra D.; Withers, Marjorie A.; Harris, R. Alan; Rogers, Jeffrey; Schwartz, David C.; Lupski, James R.

2015-01-01

Many loci in the human genome harbor complex genomic structures that can result in susceptibility to genomic rearrangements leading to various genomic disorders. Nephronophthisis 1 (NPHP1, MIM# 256100) is an autosomal recessive disorder that can be caused by defects of NPHP1; the gene maps within the human 2q13 region where low copy repeats (LCRs) are abundant. Loss of function of NPHP1 is responsible for approximately 85% of the NPHP1 cases—about 80% of such individuals carry a large recurrent homozygous NPHP1 deletion that occurs via nonallelic homologous recombination (NAHR) between two flanking directly oriented ~45 kb LCRs. Published data revealed a non-pathogenic inversion polymorphism involving the NPHP1 gene flanked by two inverted ~358 kb LCRs. Using optical mapping and array-comparative genomic hybridization, we identified three potential novel structural variant (SV) haplotypes at the NPHP1 locus that may protect a haploid genome from the NPHP1 deletion. Inter-species comparative genomic analyses among primate genomes revealed massive genomic changes during evolution. The aggregated data suggest that dynamic genomic rearrangements occurred historically within the NPHP1 locus and generated SV haplotypes observed in the human population today, which may confer differential susceptibility to genomic instability and the NPHP1 deletion within a personal genome. Our study documents diverse SV haplotypes at a complex LCR-laden human genomic region. Comparative analyses provide a model for how this complex region arose during primate evolution, and studies among humans suggest that intra-species polymorphism may potentially modulate an individual’s susceptibility to acquiring disease-associated alleles. PMID:26641089

The Influence of Environmental Hazard Maps on Risk Beliefs, Emotion, and Health-related Behavioral Intentions

PubMed Central

Severtson, Dolores

2013-01-01

To test a theoretical explanation of how attributes of mapped environmental health hazards influence health-related behavioral intentions and how beliefs and emotion mediate the influences of attributes, 24 maps were developed that varied by four attributes of a residential drinking water hazard: level, proximity, prevalence, and density. In a factorial design, student participants (N=446) answered questions for a subset of maps. Hazard level and proximity had the largest influences on intentions to test water and mitigate exposure. Belief in the problem’s seriousness mediated attributes’ influence on intention to test drinking water, and perceived susceptibility mediated the influence of attributes on intention to mitigate risk. Maps with carefully illustrated attributes of hazards may promote appropriate health-related risk beliefs, intentions, and behavior. PMID:23533022

Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains

NASA Astrophysics Data System (ADS)

Jacobs, Liesbet; Dewitte, Olivier; Poesen, Jean; Sekajugo, John; Nobile, Adriano; Rossi, Mauro; Thiery, Wim; Kervyn, Matthieu

2018-01-01

The inhabited zone of the Ugandan Rwenzori Mountains is affected by landslides, frequently causing loss of life, damage to infrastructure and loss of livelihood. This area of ca. 1230 km2 is characterized by contrasting geomorphologic, climatic and lithological patterns, resulting in different landslide types. In this study, the spatial pattern of landslide susceptibility is investigated based on an extensive field inventory constructed for five representative areas within the region (153 km2) and containing over 450 landslides. To achieve a reliable susceptibility assessment, the effects of (1) using different topographic data sources and spatial resolutions and (2) changing the scale of assessment by comparing local and regional susceptibility models on the susceptibility model performances are investigated using a pixel-based logistic regression approach. Topographic data are extracted from different digital elevation models (DEMs) based on radar interferometry (SRTM and TanDEM-X) and optical stereophotogrammetry (ASTER DEM). Susceptibility models using the radar-based DEMs tend to outperform the ones using the ASTER DEM. The model spatial resolution is varied between 10, 20, 30 and 90 m. The optimal resolution depends on the location of the investigated area within the region but the lowest model resolution (90 m) rarely yields the best model performances while the highest model resolution (10 m) never results in significant increases in performance compared to the 20 m resolution. Models built for the local case studies generally have similar or better performances than the regional model and better reflect site-specific controlling factors. At the regional level the effect of distinguishing landslide types between shallow and deep-seated landslides is investigated. The separation of landslide types allows us to improve model performances for the prediction of deep-seated landslides and to better understand factors influencing the occurrence of shallow landslides such as tangent curvature and total rainfall. Finally, the landslide susceptibility assessment is overlaid with a population density map in order to identify potential landslide risk hotspots, which could direct research and policy action towards reduced landslide risk in this under-researched, landslide-prone region.

Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude-frequency relationships: A case study in Hamadan province, Iran

NASA Astrophysics Data System (ADS)

Taheri, Kamal; Gutiérrez, Francisco; Mohseni, Hassan; Raeisi, Ezzat; Taheri, Milad

2015-04-01

Since 1989, an increasing number of sinkhole occurrences have been reported in the Kabudar Ahang and Razan-Qahavand subcatchments (KRQ) of Hamadan province, western Iran. The sinkhole-related subsidence phenomenon poses a significant threat for people and human structures, including sensitive facilities like the Hamadan Power Plant. Groundwater over-exploitation from the thick alluvial cover and the underlying cavernous limestone has been identified as the main factor involved in sinkhole development. A sinkhole susceptibility model was produced in a GIS environment applying the analytical hierarchy process (AHP) approach and considering a selection of eight factors, each categorized into five classes: distance to faults (DF), water level decline (WLD), groundwater exploitation (GE), penetration of deep wells into karst bedrock (PKA), distance to deep wells (DDW), groundwater alkalinity (GA), bedrock lithology (BL), and alluvium thickness (AT). Relative weights were preliminarily assigned to each factor and to their different classes through systematic pairwise comparisons based on expert judgment. The resulting sinkhole susceptibility index (SSI) values were then classified into four susceptibility classes: low, moderate, high and very high susceptibility. Subsequently, the model was refined through a trial and error process involving changes in the relative weights and iterative evaluation of the prediction capability. Independent evaluation of the final model indicates that 55% and 45% of the subsidence events fall within the very high and high, susceptibility zones, respectively. The results of this study show that AHP can be a useful approach for susceptibility assessment if data on the main controlling factors have sufficient accuracy and spatial coverage. The limitations of the model are partly related to the difficulty of gathering data on some important geological factors, due to their hidden nature. The magnitude and frequency relationship constructed with the 41 sinkholes with chronological and morphometric data indicates maximum recurrence intervals of 1.17, 2.14 and 4.18 years for sinkholes with major axial lengths equal to or higher than 10 m, 20 m, and 30 m, respectively.

Determination of grain-size characteristics from electromagnetic seabed mapping data: A NW Iberian shelf study

NASA Astrophysics Data System (ADS)

Baasch, Benjamin; Müller, Hendrik; von Dobeneck, Tilo; Oberle, Ferdinand K. J.

2017-05-01

The electric conductivity and magnetic susceptibility of sediments are fundamental parameters in environmental geophysics. Both can be derived from marine electromagnetic profiling, a novel, fast and non-invasive seafloor mapping technique. Here we present statistical evidence that electric conductivity and magnetic susceptibility can help to determine physical grain-size characteristics (size, sorting and mud content) of marine surficial sediments. Electromagnetic data acquired with the bottom-towed electromagnetic profiler MARUM NERIDIS III were analysed and compared with grain size data from 33 samples across the NW Iberian continental shelf. A negative correlation between mean grain size and conductivity (R=-0.79) as well as mean grain size and susceptibility (R=-0.78) was found. Simple and multiple linear regression analyses were carried out to predict mean grain size, mud content and the standard deviation of the grain-size distribution from conductivity and susceptibility. The comparison of both methods showed that multiple linear regression models predict the grain-size distribution characteristics better than the simple models. This exemplary study demonstrates that electromagnetic benthic profiling is capable to estimate mean grain size, sorting and mud content of marine surficial sediments at a very high significance level. Transfer functions can be calibrated using grains-size data from a few reference samples and extrapolated along shelf-wide survey lines. This study suggests that electromagnetic benthic profiling should play a larger role for coastal zone management, seafloor contamination and sediment provenance studies in worldwide continental shelf systems.