Using GIS for spatial analysis of rectal lesions in the human body.

PubMed

Garb, Jane L; Ganai, Sabha; Skinner, Ric; Boyd, Christopher S; Wait, Richard B

2007-03-15

Geographic Information Systems (GIS) have been used in a wide variety of applications to integrate data and explore the spatial relationship of geographic features. Traditionally this has referred to features on the surface of the earth. However, it is possible to apply GIS in medicine, at the scale of the human body, to visualize and analyze anatomic and clinical features. In the present study we used GIS to examine the findings of transanal endoscopic microsurgery (TEM), a minimally-invasive procedure to locate and remove both benign and cancerous lesions of the rectum. Our purpose was to determine whether anatomic features of the human rectum and clinical findings at the time of surgery could be rendered in a GIS and spatially analyzed for their relationship to clinical outcomes. Maps of rectal topology were developed in two and three dimensions. These maps highlight anatomic features of the rectum and the location of lesions found on TEM. Spatial analysis demonstrated a significant relationship between anatomic location of the lesion and procedural failure. This study demonstrates the feasibility of rendering anatomical locations and clinical events in a GIS and its value in clinical research. This allows the visualization and spatial analysis of clinical and pathologic features, increasing our awareness of the relationship between anatomic features and clinical outcomes as well as enhancing our understanding and management of this disease process.

Using GIS for spatial analysis of rectal lesions in the human body

PubMed Central

Garb, Jane L; Ganai, Sabha; Skinner, Ric; Boyd, Christopher S; Wait, Richard B

2007-01-01

Background Geographic Information Systems (GIS) have been used in a wide variety of applications to integrate data and explore the spatial relationship of geographic features. Traditionally this has referred to features on the surface of the earth. However, it is possible to apply GIS in medicine, at the scale of the human body, to visualize and analyze anatomic and clinical features. In the present study we used GIS to examine the findings of transanal endoscopic microsurgery (TEM), a minimally-invasive procedure to locate and remove both benign and cancerous lesions of the rectum. Our purpose was to determine whether anatomic features of the human rectum and clinical findings at the time of surgery could be rendered in a GIS and spatially analyzed for their relationship to clinical outcomes. Results Maps of rectal topology were developed in two and three dimensions. These maps highlight anatomic features of the rectum and the location of lesions found on TEM. Spatial analysis demonstrated a significant relationship between anatomic location of the lesion and procedural failure. Conclusion This study demonstrates the feasibility of rendering anatomical locations and clinical events in a GIS and its value in clinical research. This allows the visualization and spatial analysis of clinical and pathologic features, increasing our awareness of the relationship between anatomic features and clinical outcomes as well as enhancing our understanding and management of this disease process. PMID:17362510

Usefulness of the group-comparison method to demonstrate sex differences in spatial orientation and spatial visualization in older men and women.

PubMed

Cohen, D

1976-10-01

This paper reports an analysis of sex differences in cognitive test scores covering the dimensions of spatial orientation and spatial visualization in groups of 6 older men and 6 women matched for speed of performance on a maze test and level of performance on a spatial relations task. Older men were more proficient solving spatial problems using the body as a referent, whereas there was no significant difference between the sexes in imagining spatial displacement. Matched comparisons appear a useful adjunct to population research to understand the type(s) of cognitive processes where differential performance by the sexes is observed.

A far-infrared spatial/spectral Fourier interferometry laboratory-based testbed instrument

NASA Astrophysics Data System (ADS)

Spencer, Locke D.; Naylor, David A.; Scott, Jeremy P.; Weiler, Vince F.; MacCrimmon, Roderick K.; Sitwell, Geoffrey R. H.; Ade, Peter A. R.

2016-07-01

We describe the current status, including preliminary design, characterization efforts, and recent progress, in the development of a spatial/spectral double Fourier laboratory-based interferometer testbed instrument within the Astronomical Instrumentation Group (AIG) laboratories at the University of Lethbridge, Canada (UL). Supported by CRC, CFI, and NSERC grants, this instrument development will provide laboratory demonstration of spatial-spectral interferometry with a concentration of furthering progress in areas including the development of spatial/spectral interferometry observation, data processing, characterization, and analysis techniques in the Far-Infrared (FIR) region of the electromagnetic spectrum.

Few-mode fiber, splice and SDM component characterization by spatially-diverse optical vector network analysis.

PubMed

Rommel, Simon; Mendinueta, José Manuel Delgado; Klaus, Werner; Sakaguchi, Jun; Olmos, Juan José Vegas; Awaji, Yoshinari; Monroy, Idelfonso Tafur; Wada, Naoya

2017-09-18

This paper discusses spatially diverse optical vector network analysis for space division multiplexing (SDM) component and system characterization, which is becoming essential as SDM is widely considered to increase the capacity of optical communication systems. Characterization of a 108-channel photonic lantern spatial multiplexer, coupled to a 36-core 3-mode fiber, is experimentally demonstrated, extracting the full impulse response and complex transfer function matrices as well as insertion loss (IL) and mode-dependent loss (MDL) data. Moreover, the mode-mixing behavior of fiber splices in the few-mode multi-core fiber and their impact on system IL and MDL are analyzed, finding splices to cause significant mode-mixing and to be non-negligible in system capacity analysis.

Spatially resolved chemical analysis of cicada wings using laser-ablation electrospray ionization (LAESI) imaging mass spectrometry (IMS).

PubMed

Román, Jessica K; Walsh, Callee M; Oh, Junho; Dana, Catherine E; Hong, Sungmin; Jo, Kyoo D; Alleyne, Marianne; Miljkovic, Nenad; Cropek, Donald M

2018-03-01

Laser-ablation electrospray ionization (LAESI) imaging mass spectrometry (IMS) is an emerging bioanalytical tool for direct imaging and analysis of biological tissues. Performing ionization in an ambient environment, this technique requires little sample preparation and no additional matrix, and can be performed on natural, uneven surfaces. When combined with optical microscopy, the investigation of biological samples by LAESI allows for spatially resolved compositional analysis. We demonstrate here the applicability of LAESI-IMS for the chemical analysis of thin, desiccated biological samples, specifically Neotibicen pruinosus cicada wings. Positive-ion LAESI-IMS accurate ion-map data was acquired from several wing cells and superimposed onto optical images allowing for compositional comparisons across areas of the wing. Various putative chemical identifications were made indicating the presence of hydrocarbons, lipids/esters, amines/amides, and sulfonated/phosphorylated compounds. With the spatial resolution capability, surprising chemical distribution patterns were observed across the cicada wing, which may assist in correlating trends in surface properties with chemical distribution. Observed ions were either (1) equally dispersed across the wing, (2) more concentrated closer to the body of the insect (proximal end), or (3) more concentrated toward the tip of the wing (distal end). These findings demonstrate LAESI-IMS as a tool for the acquisition of spatially resolved chemical information from fragile, dried insect wings. This LAESI-IMS technique has important implications for the study of functional biomaterials, where understanding the correlation between chemical composition, physical structure, and biological function is critical. Graphical abstract Positive-ion laser-ablation electrospray ionization mass spectrometry coupled with optical imaging provides a powerful tool for the spatially resolved chemical analysis of cicada wings.

Incorporating spatial context into the analysis of salmonid habitat relations: Chapter 18

USGS Publications Warehouse

Torgersen, Christian E.; Baxter, Colden V.; Ebersole, J.L.; Gresswell, Bob; Church, Michael; Biron, Pascale M.; Roy, Andre G.

2012-01-01

In this response to the chapter by Lapointe (this volume), we discuss the question of why it is so difficult to predict salmonid-habitat relations in gravel-bed rivers and streams. We acknowledge that this cannot be an exhaustive treatment of the subject and, thus, identify what we believe are several key issues that demonstrate the necessity of incorporating spatial context into the analysis of fish-habitat data. Our emphasis is on spatial context (i.e., scale and location), but it is important to note that the same principles may be applied with some modification to temporal context, which is beyond the scope of this chapter.

Species composition and morphologic variation of Porites in the Gulf of California

NASA Astrophysics Data System (ADS)

López-Pérez, R. A.

2013-09-01

Morphometric analysis of corallite calices confirmed that from the late Miocene to the Recent, four species of Porites have inhabited the Gulf of California: the extinct Porites carrizensis, the locally extirpated Porites lobata and the extant Porites sverdrupi and Porites panamensis. Furthermore, large-scale spatial and temporal phenotypic plasticity was observed in the dominant species P. panamensis. Canonical discriminant analysis and ANOVA demonstrated that the calice structures of P. panamensis experienced size reduction between the late Pleistocene and Recent. Similarly, PERMANOVA, regression and correlation analyses demonstrated that across the 800 km north to south in the gulf, P. panamensis populations displayed a similar reduction in calice structures. Based on correlation analysis with environmental data, these large spatial changes are likely related to changes in nutrient concentration and sea surface temperature. As such, the large-scale spatial and temporal phenotypic variation recorded in populations of P. panamensis in the Gulf of California is likely related to optimization of corallite performance (energy acquisition) within various environmental scenarios. These findings may have relevance to modern conservation efforts within this ecological dominant genus.

Robust Global Image Registration Based on a Hybrid Algorithm Combining Fourier and Spatial Domain Techniques

DTIC Science & Technology

2012-09-01

Robust global image registration based on a hybrid algorithm combining Fourier and spatial domain techniques Peter N. Crabtree, Collin Seanor...00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Robust global image registration based on a hybrid algorithm combining Fourier and spatial domain...demonstrate performance of a hybrid algorithm . These results are from analysis of a set of images of an ISO 12233 [12] resolution chart captured in the

Random vectors and spatial analysis by geostatistics for geotechnical applications

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

Young, D.S.

1987-08-01

Geostatistics is extended to the spatial analysis of vector variables by defining the estimation variance and vector variogram in terms of the magnitude of difference vectors. Many random variables in geotechnology are in vectorial terms rather than scalars, and its structural analysis requires those sample variable interpolations to construct and characterize structural models. A better local estimator will result in greater quality of input models; geostatistics can provide such estimators; kriging estimators. The efficiency of geostatistics for vector variables is demonstrated in a case study of rock joint orientations in geological formations. The positive cross-validation encourages application of geostatistics tomore » spatial analysis of random vectors in geoscience as well as various geotechnical fields including optimum site characterization, rock mechanics for mining and civil structures, cavability analysis of block cavings, petroleum engineering, and hydrologic and hydraulic modelings.« less

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

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

Iliopoulos, AS; Sun, X; Floros, D

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well asmore » histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.« less

Time and space in the middle paleolithic: Spatial structure and occupation dynamics of seven open-air sites.

PubMed

Clark, Amy E

2016-05-06

The spatial structure of archeological sites can help reconstruct the settlement dynamics of hunter-gatherers by providing information on the number and length of occupations. This study seeks to access this information through a comparison of seven sites. These sites are open-air and were all excavated over large spatial areas, up to 2,000 m(2) , and are therefore ideal for spatial analysis, which was done using two complementary methods, lithic refitting and density zones. Both methods were assessed statistically using confidence intervals. The statistically significant results from each site were then compiled to evaluate trends that occur across the seven sites. These results were used to assess the "spatial consistency" of each assemblage and, through that, the number and duration of occupations. This study demonstrates that spatial analysis can be a powerful tool in research on occupation dynamics and can help disentangle the many occupations that often make up an archeological assemblage. © 2016 Wiley Periodicals, Inc.

Wavelet Analysis for RADARSAT Exploitation: Demonstration of Algorithms for Maritime Surveillance

DTIC Science & Technology

2007-02-01

this study , we demonstrate wavelet analysis for exploitation of RADARSAT ocean imagery, including wind direction estimation, oceanic and atmospheric ...of image striations that can arise as a texture pattern caused by turbulent coherent structures in the marine atmospheric boundary layer. The image...associated change in the pattern texture (i.e., the nature of the turbulent atmospheric structures) across the front. Due to the large spatial scale of

Spatial Analysis after Perinatal Stroke: Patterns of Neglect and Exploration in Extra-Personal Space

ERIC Educational Resources Information Center

Thareja, Tarika; Ballantyne, Angela O.; Trauner, Doris A.

2012-01-01

This study was conducted to determine whether school-aged children who had experienced a perinatal stroke demonstrate evidence of persistent spatial neglect, and if such neglect was specific to the visual domain or was more generalized. Two studies were carried out. In the first, 38 children with either left hemisphere (LH) or right hemisphere…

Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface.

PubMed

Kiran Kumar, G R; Reddy, M Ramasubba

2018-06-08

Traditional Spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. In this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. The experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. The results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain-computer interface (BCI). Copyright © 2018. Published by Elsevier B.V.

Remote sensing applications for environmental analysis in transportation planning : application to the Washington State I-405 corridor

DOT National Transportation Integrated Search

2004-08-30

The project's major objective is to demonstrate and assess the applicability of commercial remote sensing products and spatial information technologies to environmental analysis in transportation planning, using the I-405 corridor in Washington State...

Fusion and quality analysis for remote sensing images using contourlet transform

NASA Astrophysics Data System (ADS)

Choi, Yoonsuk; Sharifahmadian, Ershad; Latifi, Shahram

2013-05-01

Recent developments in remote sensing technologies have provided various images with high spatial and spectral resolutions. However, multispectral images have low spatial resolution and panchromatic images have low spectral resolution. Therefore, image fusion techniques are necessary to improve the spatial resolution of spectral images by injecting spatial details of high-resolution panchromatic images. The objective of image fusion is to provide useful information by improving the spatial resolution and the spectral information of the original images. The fusion results can be utilized in various applications, such as military, medical imaging, and remote sensing. This paper addresses two issues in image fusion: i) image fusion method and ii) quality analysis of fusion results. First, a new contourlet-based image fusion method is presented, which is an improvement over the wavelet-based fusion. This fusion method is then applied to a case study to demonstrate its fusion performance. Fusion framework and scheme used in the study are discussed in detail. Second, quality analysis for the fusion results is discussed. We employed various quality metrics in order to analyze the fusion results both spatially and spectrally. Our results indicate that the proposed contourlet-based fusion method performs better than the conventional wavelet-based fusion methods.

Towards understanding temporal and spatial dynamics of seagrass landscapes using time-series remote sensing

NASA Astrophysics Data System (ADS)

Lyons, Mitchell B.; Roelfsema, Chris M.; Phinn, Stuart R.

2013-03-01

The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (≈200 km2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and decline as well as the processes that drive it. This study demonstrates clear benefits over traditional seagrass mapping and monitoring approaches, and provides a proof of concept for the use of trend and time-series analysis of remotely sensed seagrass products to benefit current endeavours in seagrass ecology.

Next-generation technologies for spatial proteomics: Integrating ultra-high speed MALDI-TOF and high mass resolution MALDI FTICR imaging mass spectrometry for protein analysis.

PubMed

Spraggins, Jeffrey M; Rizzo, David G; Moore, Jessica L; Noto, Michael J; Skaar, Eric P; Caprioli, Richard M

2016-06-01

MALDI imaging mass spectrometry is a powerful analytical tool enabling the visualization of biomolecules in tissue. However, there are unique challenges associated with protein imaging experiments including the need for higher spatial resolution capabilities, improved image acquisition rates, and better molecular specificity. Here we demonstrate the capabilities of ultra-high speed MALDI-TOF and high mass resolution MALDI FTICR IMS platforms as they relate to these challenges. High spatial resolution MALDI-TOF protein images of rat brain tissue and cystic fibrosis lung tissue were acquired at image acquisition rates >25 pixels/s. Structures as small as 50 μm were spatially resolved and proteins associated with host immune response were observed in cystic fibrosis lung tissue. Ultra-high speed MALDI-TOF enables unique applications including megapixel molecular imaging as demonstrated for lipid analysis of cystic fibrosis lung tissue. Additionally, imaging experiments using MALDI FTICR IMS were shown to produce data with high mass accuracy (<5 ppm) and resolving power (∼75 000 at m/z 5000) for proteins up to ∼20 kDa. Analysis of clear cell renal cell carcinoma using MALDI FTICR IMS identified specific proteins localized to healthy tissue regions, within the tumor, and also in areas of increased vascularization around the tumor. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

PubMed

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-03-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran's I analysis was used to supplement the traditional geostatistics. According to Moran's I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran's I and the standardized Moran's I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran's I analysis was better than traditional geostatistics. Thus, Moran's I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China

PubMed Central

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-01-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran’s I analysis was used to supplement the traditional geostatistics. According to Moran’s I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran’s I and the standardized Moran’s I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran’s I analysis was better than traditional geostatistics. Thus, Moran’s I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals. PMID:22690179

SPHARA--a generalized spatial Fourier analysis for multi-sensor systems with non-uniformly arranged sensors: application to EEG.

PubMed

Graichen, Uwe; Eichardt, Roland; Fiedler, Patrique; Strohmeier, Daniel; Zanow, Frank; Haueisen, Jens

2015-01-01

Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction. We propose a new approach for spatial harmonic analysis (SPHARA) that extends the classical spatial Fourier analysis to EEG sensors positioned non-uniformly on the surface of the head. The proposed method is based on the eigenanalysis of the discrete Laplace-Beltrami operator defined on a triangular mesh. We present several ways to discretize the continuous Laplace-Beltrami operator and compare the properties of the resulting basis functions computed using these discretization methods. We apply SPHARA to somatosensory evoked potential data from eleven volunteers and demonstrate the ability of the method for spatial data decomposition, dimensionality reduction and noise suppression. When employing SPHARA for dimensionality reduction, a significantly more compact representation can be achieved using the FEM approach, compared to the other discretization methods. Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary. The capability of SPHARA for noise suppression is shown using artificial data. We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications.

Accelerating Sequences in the Presence of Metal by Exploiting the Spatial Distribution of Off-Resonance

PubMed Central

Smith, Matthew R.; Artz, Nathan S.; Koch, Kevin M.; Samsonov, Alexey; Reeder, Scott B.

2014-01-01

Purpose To demonstrate feasibility of exploiting the spatial distribution of off-resonance surrounding metallic implants for accelerating multispectral imaging techniques. Theory Multispectral imaging (MSI) techniques perform time-consuming independent 3D acquisitions with varying RF frequency offsets to address the extreme off-resonance from metallic implants. Each off-resonance bin provides a unique spatial sensitivity that is analogous to the sensitivity of a receiver coil, and therefore provides a unique opportunity for acceleration. Methods Fully sampled MSI was performed to demonstrate retrospective acceleration. A uniform sampling pattern across off-resonance bins was compared to several adaptive sampling strategies using a total hip replacement phantom. Monte Carlo simulations were performed to compare noise propagation of two of these strategies. With a total knee replacement phantom, positive and negative off-resonance bins were strategically sampled with respect to the B0 field to minimize aliasing. Reconstructions were performed with a parallel imaging framework to demonstrate retrospective acceleration. Results An adaptive sampling scheme dramatically improved reconstruction quality, which was supported by the noise propagation analysis. Independent acceleration of negative and positive off-resonance bins demonstrated reduced overlapping of aliased signal to improve the reconstruction. Conclusion This work presents the feasibility of acceleration in the presence of metal by exploiting the spatial sensitivities of off-resonance bins. PMID:24431210

Multifractal analysis of mobile social networks

NASA Astrophysics Data System (ADS)

Zheng, Wei; Zhang, Zifeng; Deng, Yufan

2017-09-01

As Wireless Fidelity (Wi-Fi)-enabled handheld devices have been widely used, the mobile social networks (MSNs) has been attracting extensive attention. Fractal approaches have also been widely applied to characterierize natural networks as useful tools to depict their spatial distribution and scaling properties. Moreover, when the complexity of the spatial distribution of MSNs cannot be properly charaterized by single fractal dimension, multifractal analysis is required. For further research, we introduced a multifractal analysis method based on box-covering algorithm to describe the structure of MSNs. Using this method, we find that the networks are multifractal at different time interval. The simulation results demonstrate that the proposed method is efficient for analyzing the multifractal characteristic of MSNs, which provides a distribution of singularities adequately describing both the heterogeneity of fractal patterns and the statistics of measurements across spatial scales in MSNs.

SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis.

PubMed

Shi, Yuhu; Zeng, Weiming; Wang, Nizhuan

2017-09-01

With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages. In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively. The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains. These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

NASA Technical Reports Server (NTRS)

Dong, D.; Fang, P.; Bock, F.; Webb, F.; Prawirondirdjo, L.; Kedar, S.; Jamason, P.

2006-01-01

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

NASA Astrophysics Data System (ADS)

Wang, Jun; Wang, Yang; Zeng, Hui

2016-01-01

A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

Multidimensional extended spatial evolutionary games.

PubMed

Krześlak, Michał; Świerniak, Andrzej

2016-02-01

The goal of this paper is to study the classical hawk-dove model using mixed spatial evolutionary games (MSEG). In these games, played on a lattice, an additional spatial layer is introduced for dependence on more complex parameters and simulation of changes in the environment. Furthermore, diverse polymorphic equilibrium points dependent on cell reproduction, model parameters, and their simulation are discussed. Our analysis demonstrates the sensitivity properties of MSEGs and possibilities for further development. We discuss applications of MSEGs, particularly algorithms for modelling cell interactions during the development of tumours. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Framework for Spatial Interaction Analysis Based on Large-Scale Mobile Phone Data

PubMed Central

Li, Weifeng; Cheng, Xiaoyun; Guo, Gaohua

2014-01-01

The overall understanding of spatial interaction and the exact knowledge of its dynamic evolution are required in the urban planning and transportation planning. This study aimed to analyze the spatial interaction based on the large-scale mobile phone data. The newly arisen mass dataset required a new methodology which was compatible with its peculiar characteristics. A three-stage framework was proposed in this paper, including data preprocessing, critical activity identification, and spatial interaction measurement. The proposed framework introduced the frequent pattern mining and measured the spatial interaction by the obtained association. A case study of three communities in Shanghai was carried out as verification of proposed method and demonstration of its practical application. The spatial interaction patterns and the representative features proved the rationality of the proposed framework. PMID:25435865

Self-similarity in nature

NASA Astrophysics Data System (ADS)

Timashev, S. F.

2000-02-01

A general phenomenological approach to the analysis of experimental temporal, spatial and energetic series for extracting truly physical non-model parameters ("passport data") is presented, which may be used to characterize and distinguish the evolution as well as the spatial and energetic structure of any open nonlinear dissipative system. This methodology is based on a postulate concerning the crucial information contained in the sequences of non-regularities of the measured dynamic variable (temporal, spatial, energetic). In accordance with this approach, multi-parametric formulas for dynamic variable power spectra as well as for structural functions of different orders are identical for every spatial-temporal-energetic level of the system under consideration. In effect, this entails the introduction of a new kind of self-similarity in Nature. An algorithm has been developed for obtaining as many "passport data" as are necessary for the characterization of a dynamic system. Applications of this approach in the analysis of various experimental series (temporal, spatial, energetic) demonstrate its potential for defining adequate phenomenological parameters of different dynamic processes and structures.

Hyperspectral infrared nanoimaging of organic samples based on Fourier transform infrared nanospectroscopy

PubMed Central

Amenabar, Iban; Poly, Simon; Goikoetxea, Monika; Nuansing, Wiwat; Lasch, Peter; Hillenbrand, Rainer

2017-01-01

Infrared nanospectroscopy enables novel possibilities for chemical and structural analysis of nanocomposites, biomaterials or optoelectronic devices. Here we introduce hyperspectral infrared nanoimaging based on Fourier transform infrared nanospectroscopy with a tunable bandwidth-limited laser continuum. We describe the technical implementations and present hyperspectral infrared near-field images of about 5,000 pixel, each one covering the spectral range from 1,000 to 1,900 cm−1. To verify the technique and to demonstrate its application potential, we imaged a three-component polymer blend and a melanin granule in a human hair cross-section, and demonstrate that multivariate data analysis can be applied for extracting spatially resolved chemical information. Particularly, we demonstrate that distribution and chemical interaction between the polymer components can be mapped with a spatial resolution of about 30 nm. We foresee wide application potential of hyperspectral infrared nanoimaging for valuable chemical materials characterization and quality control in various fields ranging from materials sciences to biomedicine. PMID:28198384

Hyperspectral infrared nanoimaging of organic samples based on Fourier transform infrared nanospectroscopy

NASA Astrophysics Data System (ADS)

Amenabar, Iban; Poly, Simon; Goikoetxea, Monika; Nuansing, Wiwat; Lasch, Peter; Hillenbrand, Rainer

2017-02-01

Infrared nanospectroscopy enables novel possibilities for chemical and structural analysis of nanocomposites, biomaterials or optoelectronic devices. Here we introduce hyperspectral infrared nanoimaging based on Fourier transform infrared nanospectroscopy with a tunable bandwidth-limited laser continuum. We describe the technical implementations and present hyperspectral infrared near-field images of about 5,000 pixel, each one covering the spectral range from 1,000 to 1,900 cm-1. To verify the technique and to demonstrate its application potential, we imaged a three-component polymer blend and a melanin granule in a human hair cross-section, and demonstrate that multivariate data analysis can be applied for extracting spatially resolved chemical information. Particularly, we demonstrate that distribution and chemical interaction between the polymer components can be mapped with a spatial resolution of about 30 nm. We foresee wide application potential of hyperspectral infrared nanoimaging for valuable chemical materials characterization and quality control in various fields ranging from materials sciences to biomedicine.

Object motion analysis study

NASA Technical Reports Server (NTRS)

1974-01-01

The use of optical data processing (ODP) techniques for motion analysis in two-dimensional imagery was studied. The basic feasibility of this approach was demonstrated, but inconsistent performance of the photoplastic used for recording spatial filters prevented totally automatic operation. Promising solutions to the problems encountered are discussed, and it is concluded that ODP techniques could be quite useful for motion analysis.

The geography of patient safety: a topical analysis of sterility.

PubMed

Mesman, Jessica

2009-12-01

Many studies on patient safety are geared towards prevention of adverse events by eliminating causes of error. In this article, I argue that patient safety research needs to widen its analytical scope and include causes of strength as well. This change of focus enables me to ask other questions, like why don't things go wrong more often? Or, what is the significance of time and space for patient safety? The focal point of this article is on the spatial dimension of patient safety. To gain insight into the 'geography' of patient safety and perform a topical analysis, I will focus on one specific kind of space (sterile space), one specific medical procedure (insertion of an intravenous line) and one specific medical ward (neonatology). Based on ethnographic data from research in the Netherlands, I demonstrate how spatial arrangements produce sterility and how sterility work produces spatial orders at the same time. Detailed analysis shows how a sterile line insertion involves the convergence of spatially distributed resources, relocations of the field of activity, an assemblage of an infrastructure of attention, a specific compositional order of materials, and the scaling down of one's degree of mobility. Sterility, I will argue, turns out to be a product of spatial orderings. Simultaneously, sterility work generates particular spatial orders, like open and restricted areas, by producing buffers and boundaries. However, the spatial order of sterility intersects with the spatial order of other lines of activity. Insight into the normative structure of these co-existing spatial orders turns out to be crucial for patient safety. By analyzing processes of spatial fine-tuning in everyday practice, it becomes possible to identify spatial competences and circumstances that enable staff members to provide safe health care. As such, a topical analysis offers an alternative perspective of patient safety, one that takes into account its spatial dimension.

Spatially resolved δ13C analysis using laser ablation isotope ratio mass spectrometry

NASA Astrophysics Data System (ADS)

Moran, J.; Riha, K. M.; Nims, M. K.; Linley, T. J.; Hess, N. J.; Nico, P. S.

2014-12-01

Inherent geochemical, organic matter, and microbial heterogeneity over small spatial scales can complicate studies of carbon dynamics through soils. Stable isotope analysis has a strong history of helping track substrate turnover, delineate rhizosphere activity zones, and identifying transitions in vegetation cover, but most traditional isotope approaches are limited in spatial resolution by a combination of physical separation techniques (manual dissection) and IRMS instrument sensitivity. We coupled laser ablation sampling with isotope measurement via IRMS to enable spatially resolved analysis over solid surfaces. Once a targeted sample region is ablated the resulting particulates are entrained in a helium carrier gas and passed through a combustion reactor where carbon is converted to CO2. Cyrotrapping of the resulting CO2 enables a reduction in carrier gas flow which improves overall measurement sensitivity versus traditional, high flow sample introduction. Currently we are performing sample analysis at 50 μm resolution, require 65 ng C per analysis, and achieve measurement precision consistent with other continuous flow techniques. We will discuss applications of the laser ablation IRMS (LA-IRMS) system to microbial communities and fish ecology studies to demonstrate the merits of this technique and how similar analytical approaches can be transitioned to soil systems. Preliminary efforts at analyzing soil samples will be used to highlight strengths and limitations of the LA-IRMS approach, paying particular attention to sample preparation requirements, spatial resolution, sample analysis time, and the types of questions most conducive to analysis via LA-IRMS.

Quantitative analysis of biological tissues using Fourier transform-second-harmonic generation imaging

NASA Astrophysics Data System (ADS)

Ambekar Ramachandra Rao, Raghu; Mehta, Monal R.; Toussaint, Kimani C., Jr.

2010-02-01

We demonstrate the use of Fourier transform-second-harmonic generation (FT-SHG) imaging of collagen fibers as a means of performing quantitative analysis of obtained images of selected spatial regions in porcine trachea, ear, and cornea. Two quantitative markers, preferred orientation and maximum spatial frequency are proposed for differentiating structural information between various spatial regions of interest in the specimens. The ear shows consistent maximum spatial frequency and orientation as also observed in its real-space image. However, there are observable changes in the orientation and minimum feature size of fibers in the trachea indicating a more random organization. Finally, the analysis is applied to a 3D image stack of the cornea. It is shown that the standard deviation of the orientation is sensitive to the randomness in fiber orientation. Regions with variations in the maximum spatial frequency, but with relatively constant orientation, suggest that maximum spatial frequency is useful as an independent quantitative marker. We emphasize that FT-SHG is a simple, yet powerful, tool for extracting information from images that is not obvious in real space. This technique can be used as a quantitative biomarker to assess the structure of collagen fibers that may change due to damage from disease or physical injury.

Distance to health services affects local-level vaccine efficacy for pneumococcal conjugate vaccine (PCV) among rural Filipino children.

PubMed

Root, Elisabeth Dowling; Lucero, Marilla; Nohynek, Hanna; Anthamatten, Peter; Thomas, Deborah S K; Tallo, Veronica; Tanskanen, Antti; Quiambao, Beatriz P; Puumalainen, Taneli; Lupisan, Socorro P; Ruutu, Petri; Ladesma, Erma; Williams, Gail M; Riley, Ian; Simões, Eric A F

2014-03-04

Pneumococcal conjugate vaccines (PCVs) have demonstrated efficacy against childhood pneumococcal disease in several regions globally. We demonstrate how spatial epidemiological analysis of a PCV trial can assist in developing vaccination strategies that target specific geographic subpopulations at greater risk for pneumococcal pneumonia. We conducted a secondary analysis of a randomized, placebo-controlled, double-blind vaccine trial that examined the efficacy of an 11-valent PCV among children less than 2 y of age in Bohol, Philippines. Trial data were linked to the residential location of each participant using a geographic information system. We use spatial interpolation methods to create smoothed surface maps of vaccination rates and local-level vaccine efficacy across the study area. We then measure the relationship between distance to the main study hospital and local-level vaccine efficacy, controlling for ecological factors, using spatial autoregressive models with spatial autoregressive disturbances. We find a significant amount of spatial variation in vaccination rates across the study area. For the primary study endpoint vaccine efficacy increased with distance from the main study hospital from -14% for children living less than 1.5 km from Bohol Regional Hospital (BRH) to 55% for children living greater than 8.5 km from BRH. Spatial regression models indicated that after adjustment for ecological factors, distance to the main study hospital was positively related to vaccine efficacy, increasing at a rate of 4.5% per kilometer distance. Because areas with poor access to care have significantly higher VE, targeted vaccination of children in these areas might allow for a more effective implementation of global programs.

Surface Desorption Dielectric-Barrier Discharge Ionization Mass Spectrometry.

PubMed

Zhang, Hong; Jiang, Jie; Li, Na; Li, Ming; Wang, Yingying; He, Jing; You, Hong

2017-07-18

A variant of dielectric-barrier discharge named surface desorption dielectric-barrier discharge ionization (SDDBDI) mass spectrometry was developed for high-efficiency ion transmission and high spatial resolution imaging. In SDDBDI, a tungsten nanotip and the inlet of the mass spectrometer are used as electrodes, and a piece of coverslip is used as a sample plate as well as an insulating dielectric barrier, which simplifies the configuration of instrument and thus the operation. Different from volume dielectric-barrier discharge (VDBD), the microdischarges are generated on the surface at SDDBDI, and therefore the plasma density is extremely high. Analyte ions are guided directly into the MS inlet without any deflection. This configuration significantly improves the ion transmission efficiency and thus the sensitivity. The dependence of sensitivity and spatial resolution of the SDDBDI on the operation parameters were systematically investigated. The application of SDDBDI was successfully demonstrated by analysis of multiple species including amino acids, pharmaceuticals, putative cancer biomarkers, and mixtures of both fatty acids and hormones. Limits of detection (S/N = 3) were determined to be 0.84 and 0.18 pmol, respectively, for the analysis of l-alanine and metronidazole. A spatial resolution of 22 μm was obtained for the analysis of an imprinted cyclophosphamide pattern, and imaging of a "T" character was successfully demonstrated under ambient conditions. These results indicate that SDDBDI has high-efficiency ion transmission, high sensitivity, and high spatial resolution, which render it a potential tool for mass spectrometry imaging.

SPHARA - A Generalized Spatial Fourier Analysis for Multi-Sensor Systems with Non-Uniformly Arranged Sensors: Application to EEG

PubMed Central

Graichen, Uwe; Eichardt, Roland; Fiedler, Patrique; Strohmeier, Daniel; Zanow, Frank; Haueisen, Jens

2015-01-01

Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction. We propose a new approach for spatial harmonic analysis (SPHARA) that extends the classical spatial Fourier analysis to EEG sensors positioned non-uniformly on the surface of the head. The proposed method is based on the eigenanalysis of the discrete Laplace-Beltrami operator defined on a triangular mesh. We present several ways to discretize the continuous Laplace-Beltrami operator and compare the properties of the resulting basis functions computed using these discretization methods. We apply SPHARA to somatosensory evoked potential data from eleven volunteers and demonstrate the ability of the method for spatial data decomposition, dimensionality reduction and noise suppression. When employing SPHARA for dimensionality reduction, a significantly more compact representation can be achieved using the FEM approach, compared to the other discretization methods. Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary. The capability of SPHARA for noise suppression is shown using artificial data. We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications. PMID:25885290

GEMAS: Spatial pattern analysis of Ni by using digital image processing techniques on European agricultural soil data

NASA Astrophysics Data System (ADS)

Jordan, Gyozo; Petrik, Attila; De Vivo, Benedetto; Albanese, Stefano; Demetriades, Alecos; Sadeghi, Martiya

2017-04-01

Several studies have investigated the spatial distribution of chemical elements in topsoil (0-20 cm) within the framework of the EuroGeoSurveys Geochemistry Expert Group's 'Geochemical Mapping of Agricultural and Grazing Land Soil' project . Most of these studies used geostatistical analyses and interpolated concentration maps, Exploratory and Compositional Data and Analysis to identify anomalous patterns. The objective of our investigation is to demonstrate the use of digital image processing techniques for reproducible spatial pattern recognition and quantitative spatial feature characterisation. A single element (Ni) concentration in agricultural topsoil is used to perform the detailed spatial analysis, and to relate these features to possible underlying processes. In this study, simple univariate statistical methods were implemented first, and Tukey's inner-fence criterion was used to delineate statistical outliers. The linear and triangular irregular network (TIN) interpolation was used on the outlier-free Ni data points, which was resampled to a 10*10 km grid. Successive moving average smoothing was applied to generalise the TIN model and to suppress small- and at the same time enhance significant large-scale features of Nickel concentration spatial distribution patterns in European topsoil. The TIN map smoothed with a moving average filter revealed the spatial trends and patterns without losing much detail, and it was used as the input into digital image processing, such as local maxima and minima determination, digital cross sections, gradient magnitude and gradient direction calculation, second derivative profile curvature calculation, edge detection, local variability assessment, lineament density and directional variogram analyses. The detailed image processing analysis revealed several NE-SW, E-W and NW-SE oriented elongated features, which coincide with different spatial parameter classes and alignment with local maxima and minima. The NE-SW oriented linear pattern is the dominant feature to the south of the last glaciation limit. Some of these linear features are parallel to the suture zone of the Iapetus Ocean, while the others follow the Alpine and Carpathian Chains. The highest variability zones of Ni concentration in topsoil are located in the Alps and in the Balkans where mafic and ultramafic rocks outcrop. The predominant NE-SW oriented pattern is also captured by the strong anisotropy in the semi-variograms in this direction. A single major E-W oriented north-facing feature runs along the southern border of the last glaciation zone. This zone also coincides with a series of local maxima in Ni concentration along the glaciofluvial deposits. The NW-SE elongated spatial features are less dominant and are located in the Pyrenees and Scandinavia. This study demonstrates the efficiency of systematic image processing analysis in identifying and characterising spatial geochemical patterns that often remain uncovered by the usual visual map interpretation techniques.

A generalized spatiotemporal covariance model for stationary background in analysis of MEG data.

PubMed

Plis, S M; Schmidt, D M; Jun, S C; Ranken, D M

2006-01-01

Using a noise covariance model based on a single Kronecker product of spatial and temporal covariance in the spatiotemporal analysis of MEG data was demonstrated to provide improvement in the results over that of the commonly used diagonal noise covariance model. In this paper we present a model that is a generalization of all of the above models. It describes models based on a single Kronecker product of spatial and temporal covariance as well as more complicated multi-pair models together with any intermediate form expressed as a sum of Kronecker products of spatial component matrices of reduced rank and their corresponding temporal covariance matrices. The model provides a framework for controlling the tradeoff between the described complexity of the background and computational demand for the analysis using this model. Ways to estimate the value of the parameter controlling this tradeoff are also discussed.

Resolution of VTI anisotropy with elastic full-waveform inversion: theory and basic numerical examples

NASA Astrophysics Data System (ADS)

Podgornova, O.; Leaney, S.; Liang, L.

2018-07-01

Extracting medium properties from seismic data faces some limitations due to the finite frequency content of the data and restricted spatial positions of the sources and receivers. Some distributions of the medium properties make low impact on the data (including none). If these properties are used as the inversion parameters, then the inverse problem becomes overparametrized, leading to ambiguous results. We present an analysis of multiparameter resolution for the linearized inverse problem in the framework of elastic full-waveform inversion. We show that the spatial and multiparameter sensitivities are intertwined and non-sensitive properties are spatial distributions of some non-trivial combinations of the conventional elastic parameters. The analysis accounts for the Hessian information and frequency content of the data; it is semi-analytical (in some scenarios analytical), easy to interpret and enhances results of the widely used radiation pattern analysis. Single-type scattering is shown to have limited sensitivity, even for full-aperture data. Finite-frequency data lose multiparameter sensitivity at smooth and fine spatial scales. Also, we establish ways to quantify a spatial-multiparameter coupling and demonstrate that the theoretical predictions agree well with the numerical results.

Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid Systems

PubMed Central

Wang, Kaibo; Huai, Yin; Lee, Rubao; Wang, Fusheng; Zhang, Xiaodong; Saltz, Joel H.

2012-01-01

As an important application of spatial databases in pathology imaging analysis, cross-comparing the spatial boundaries of a huge amount of segmented micro-anatomic objects demands extremely data- and compute-intensive operations, requiring high throughput at an affordable cost. However, the performance of spatial database systems has not been satisfactory since their implementations of spatial operations cannot fully utilize the power of modern parallel hardware. In this paper, we provide a customized software solution that exploits GPUs and multi-core CPUs to accelerate spatial cross-comparison in a cost-effective way. Our solution consists of an efficient GPU algorithm and a pipelined system framework with task migration support. Extensive experiments with real-world data sets demonstrate the effectiveness of our solution, which improves the performance of spatial cross-comparison by over 18 times compared with a parallelized spatial database approach. PMID:23355955

Application of Image Analysis for Characterization of Spatial Arrangements of Features in Microstructure

NASA Technical Reports Server (NTRS)

Louis, Pascal; Gokhale, Arun M.

1995-01-01

A number of microstructural processes are sensitive to the spatial arrangements of features in microstructure. However, very little attention has been given in the past to the experimental measurements of the descriptors of microstructural distance distributions due to the lack of practically feasible methods. We present a digital image analysis procedure to estimate the micro-structural distance distributions. The application of the technique is demonstrated via estimation of K function, radial distribution function, and nearest-neighbor distribution function of hollow spherical carbon particulates in a polymer matrix composite, observed in a metallographic section.

A high-performance spatial database based approach for pathology imaging algorithm evaluation

PubMed Central

Wang, Fusheng; Kong, Jun; Gao, Jingjing; Cooper, Lee A.D.; Kurc, Tahsin; Zhou, Zhengwen; Adler, David; Vergara-Niedermayr, Cristobal; Katigbak, Bryan; Brat, Daniel J.; Saltz, Joel H.

2013-01-01

Background: Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison with human annotations, combine results from multiple algorithms for performance improvement, and facilitate algorithm sensitivity studies. The sizes of images and image analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present an efficient parallel spatial database approach to model, normalize, manage, and query large volumes of analytical image result data. This provides an efficient platform for algorithm evaluation. Our experiments with a set of brain tumor images demonstrate the application, scalability, and effectiveness of the platform. Context: The paper describes an approach and platform for evaluation of pathology image analysis algorithms. The platform facilitates algorithm evaluation through a high-performance database built on the Pathology Analytic Imaging Standards (PAIS) data model. Aims: (1) Develop a framework to support algorithm evaluation by modeling and managing analytical results and human annotations from pathology images; (2) Create a robust data normalization tool for converting, validating, and fixing spatial data from algorithm or human annotations; (3) Develop a set of queries to support data sampling and result comparisons; (4) Achieve high performance computation capacity via a parallel data management infrastructure, parallel data loading and spatial indexing optimizations in this infrastructure. Materials and Methods: We have considered two scenarios for algorithm evaluation: (1) algorithm comparison where multiple result sets from different methods are compared and consolidated; and (2) algorithm validation where algorithm results are compared with human annotations. We have developed a spatial normalization toolkit to validate and normalize spatial boundaries produced by image analysis algorithms or human annotations. The validated data were formatted based on the PAIS data model and loaded into a spatial database. To support efficient data loading, we have implemented a parallel data loading tool that takes advantage of multi-core CPUs to accelerate data injection. The spatial database manages both geometric shapes and image features or classifications, and enables spatial sampling, result comparison, and result aggregation through expressive structured query language (SQL) queries with spatial extensions. To provide scalable and efficient query support, we have employed a shared nothing parallel database architecture, which distributes data homogenously across multiple database partitions to take advantage of parallel computation power and implements spatial indexing to achieve high I/O throughput. Results: Our work proposes a high performance, parallel spatial database platform for algorithm validation and comparison. This platform was evaluated by storing, managing, and comparing analysis results from a set of brain tumor whole slide images. The tools we develop are open source and available to download. Conclusions: Pathology image algorithm validation and comparison are essential to iterative algorithm development and refinement. One critical component is the support for queries involving spatial predicates and comparisons. In our work, we develop an efficient data model and parallel database approach to model, normalize, manage and query large volumes of analytical image result data. Our experiments demonstrate that the data partitioning strategy and the grid-based indexing result in good data distribution across database nodes and reduce I/O overhead in spatial join queries through parallel retrieval of relevant data and quick subsetting of datasets. The set of tools in the framework provide a full pipeline to normalize, load, manage and query analytical results for algorithm evaluation. PMID:23599905

Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits

NASA Astrophysics Data System (ADS)

Tsutsumi, Morito; Seya, Hajime

2009-12-01

This study discusses the theoretical foundation of the application of spatial hedonic approaches—the hedonic approach employing spatial econometrics or/and spatial statistics—to benefits evaluation. The study highlights the limitations of the spatial econometrics approach since it uses a spatial weight matrix that is not employed by the spatial statistics approach. Further, the study presents empirical analyses by applying the Spatial Autoregressive Error Model (SAEM), which is based on the spatial econometrics approach, and the Spatial Process Model (SPM), which is based on the spatial statistics approach. SPMs are conducted based on both isotropy and anisotropy and applied to different mesh sizes. The empirical analysis reveals that the estimated benefits are quite different, especially between isotropic and anisotropic SPM and between isotropic SPM and SAEM; the estimated benefits are similar for SAEM and anisotropic SPM. The study demonstrates that the mesh size does not affect the estimated amount of benefits. Finally, the study provides a confidence interval for the estimated benefits and raises an issue with regard to benefit evaluation.

Texture-adaptive hyperspectral video acquisition system with a spatial light modulator

NASA Astrophysics Data System (ADS)

Fang, Xiaojing; Feng, Jiao; Wang, Yongjin

2014-10-01

We present a new hybrid camera system based on spatial light modulator (SLM) to capture texture-adaptive high-resolution hyperspectral video. The hybrid camera system records a hyperspectral video with low spatial resolution using a gray camera and a high-spatial resolution video using a RGB camera. The hyperspectral video is subsampled by the SLM. The subsampled points can be adaptively selected according to the texture characteristic of the scene by combining with digital imaging analysis and computational processing. In this paper, we propose an adaptive sampling method utilizing texture segmentation and wavelet transform (WT). We also demonstrate the effectiveness of the sampled pattern on the SLM with the proposed method.

Spatially variable stage-driven groundwater-surface water interaction inferred from time-frequency analysis of distributed temperature sensing data

USGS Publications Warehouse

Mwakanyamale, Kisa; Slater, Lee; Day-Lewis, Frederick D.; Elwaseif, Mehrez; Johnson, Carole D.

2012-01-01

Characterization of groundwater-surface water exchange is essential for improving understanding of contaminant transport between aquifers and rivers. Fiber-optic distributed temperature sensing (FODTS) provides rich spatiotemporal datasets for quantitative and qualitative analysis of groundwater-surface water exchange. We demonstrate how time-frequency analysis of FODTS and synchronous river stage time series from the Columbia River adjacent to the Hanford 300-Area, Richland, Washington, provides spatial information on the strength of stage-driven exchange of uranium contaminated groundwater in response to subsurface heterogeneity. Although used in previous studies, the stage-temperature correlation coefficient proved an unreliable indicator of the stage-driven forcing on groundwater discharge in the presence of other factors influencing river water temperature. In contrast, S-transform analysis of the stage and FODTS data definitively identifies the spatial distribution of discharge zones and provided information on the dominant forcing periods (≥2 d) of the complex dam operations driving stage fluctuations and hence groundwater-surface water exchange at the 300-Area.

Area variations in multiple morbidity using a life table methodology.

PubMed

Congdon, Peter

Analysis of healthy life expectancy is typically based on a binary distinction between health and ill-health. By contrast, this paper considers spatial modelling of disease free life expectancy taking account of the number of chronic conditions. Thus the analysis is based on population sub-groups with no disease, those with one disease only, and those with two or more diseases (multiple morbidity). Data on health status is accordingly modelled using a multinomial likelihood. The analysis uses data for 258 small areas in north London, and shows wide differences in the disease burden related to multiple morbidity. Strong associations between area socioeconomic deprivation and multiple morbidity are demonstrated, as well as strong spatial clustering.

Reconfigurable wavefront sensor for ultrashort pulses.

PubMed

Bock, Martin; Das, Susanta Kumar; Fischer, Carsten; Diehl, Michael; Börner, Peter; Grunwald, Ruediger

2012-04-01

A highly flexible Shack-Hartmann wavefront sensor for ultrashort pulse diagnostics is presented. The temporal system performance is studied in detail. Reflective operation is enabled by programming tilt-tolerant microaxicons into a liquid-crystal-on-silicon spatial light modulator. Nearly undistorted pulse transfer is obtained by generating nondiffracting needle beams as subbeams. Reproducible wavefront analysis and spatially resolved second-order autocorrelation are demonstrated at incident angles up to 50° and pulse durations down to 6 fs.

Spatial analysis of alcohol-related motor vehicle crash injuries in southeastern Michigan.

PubMed

Meliker, Jaymie R; Maio, Ronald F; Zimmerman, Marc A; Kim, Hyungjin Myra; Smith, Sarah C; Wilson, Mark L

2004-11-01

Temporal, behavioral and social risk factors that affect injuries resulting from alcohol-related motor vehicle crashes have been characterized in previous research. Much less is known about spatial patterns and environmental associations of alcohol-related motor vehicle crashes. The aim of this study was to evaluate geographic patterns of alcohol-related motor vehicle crashes and to determine if locations of alcohol outlets are associated with those crashes. In addition, we sought to demonstrate the value of integrating spatial and traditional statistical techniques in the analysis of this preventable public health risk. The study design was a cross-sectional analysis of individual-level blood alcohol content, traffic report information, census block group data, and alcohol distribution outlets. Besag and Newell's spatial analysis and traditional logistic regression both indicated that areas of low population density had more alcohol-related motor vehicle crashes than expected (P < 0.05). There was no significant association between alcohol outlets and alcohol-related motor vehicle crashes using distance analyses, logistic regression, and Chi-square. Differences in environmental or behavioral factors characteristic of areas of low population density may be responsible for the higher proportion of alcohol-related crashes occurring in these areas.

Crack Imaging and Quantification in Aluminum Plates with Guided Wave Wavenumber Analysis Methods

NASA Technical Reports Server (NTRS)

Yu, Lingyu; Tian, Zhenhua; Leckey, Cara A. C.

2015-01-01

Guided wavefield analysis methods for detection and quantification of crack damage in an aluminum plate are presented in this paper. New wavenumber components created by abrupt wave changes at the structural discontinuity are identified in the frequency-wavenumber spectra. It is shown that the new wavenumbers can be used to detect and characterize the crack dimensions. Two imaging based approaches, filter reconstructed imaging and spatial wavenumber imaging, are used to demonstrate how the cracks can be evaluated with wavenumber analysis. The filter reconstructed imaging is shown to be a rapid method to map the plate and any existing damage, but with less precision in estimating crack dimensions; while the spatial wavenumber imaging provides an intensity image of spatial wavenumber values with enhanced resolution of crack dimensions. These techniques are applied to simulated wavefield data, and the simulation based studies show that spatial wavenumber imaging method is able to distinguish cracks of different severities. Laboratory experimental validation is performed for a single crack case to confirm the methods' capabilities for imaging cracks in plates.

Electro-focusing liquid extractive surface analysis (EF-LESA) coupled to mass spectrometry.

PubMed

Brenton, A Gareth; Godfrey, A Ruth

2014-04-01

Analysis of the chemical composition of surfaces by liquid sampling devices interfaced to mass spectrometry is attractive as the sample stream can be continuously monitored at good sensitivity and selectivity. A sampling probe has been constructed that takes discrete liquid samples (typically <100 nL) of a surface. It incorporates an electrostatic lens system, comprising three electrodes, to which static and pulsed voltages are applied to form a conical "liquid tip", employed to dissolve analytes at a surface. A prototype system demonstrates spatial resolution of 0.093 mm(2). Time of contact between the liquid tip and the surface is controlled to standardize extraction. Calibration graphs of different analyte concentrations on a stainless surface have been measured, together with the probe's reproducibility, carryover, and recovery. A leucine enkephalin-coated surface demonstrated good linearity (R(2) = 0.9936), with a recovery of 90% and a limit of detection of 38 fmol per single spot sampled. The probe is compact and can be fitted into automated sample analysis equipment having potential for rapid analysis of surfaces at a good spatial resolution.

Electro-Focusing Liquid Extractive Surface Analysis (EF-LESA) Coupled to Mass Spectrometry

PubMed Central

2014-01-01

Analysis of the chemical composition of surfaces by liquid sampling devices interfaced to mass spectrometry is attractive as the sample stream can be continuously monitored at good sensitivity and selectivity. A sampling probe has been constructed that takes discrete liquid samples (typically <100 nL) of a surface. It incorporates an electrostatic lens system, comprising three electrodes, to which static and pulsed voltages are applied to form a conical “liquid tip”, employed to dissolve analytes at a surface. A prototype system demonstrates spatial resolution of 0.093 mm2. Time of contact between the liquid tip and the surface is controlled to standardize extraction. Calibration graphs of different analyte concentrations on a stainless surface have been measured, together with the probe’s reproducibility, carryover, and recovery. A leucine enkephalin-coated surface demonstrated good linearity (R2 = 0.9936), with a recovery of 90% and a limit of detection of 38 fmol per single spot sampled. The probe is compact and can be fitted into automated sample analysis equipment having potential for rapid analysis of surfaces at a good spatial resolution. PMID:24597530

False Discovery Control in Large-Scale Spatial Multiple Testing

PubMed Central

Sun, Wenguang; Reich, Brian J.; Cai, T. Tony; Guindani, Michele; Schwartzman, Armin

2014-01-01

Summary This article develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both point-wise and cluster-wise spatial analyses, and derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate, respectively. A data-driven finite approximation strategy is developed to mimic the oracle procedures on a continuous spatial domain. Our multiple testing procedures are asymptotically valid and can be effectively implemented using Bayesian computational algorithms for analysis of large spatial data sets. Numerical results show that the proposed procedures lead to more accurate error control and better power performance than conventional methods. We demonstrate our methods for analyzing the time trends in tropospheric ozone in eastern US. PMID:25642138

On the temporal dynamics of spatial stimulus-response transfer between spatial incompatibility and Simon tasks

PubMed Central

Ivanoff, Jason; Blagdon, Ryan; Feener, Stefanie; McNeil, Melanie; Muir, Paul H.

2014-01-01

The Simon effect refers to the performance (response time and accuracy) advantage for responses that spatially correspond to the task-irrelevant location of a stimulus. It has been attributed to a natural tendency to respond toward the source of stimulation. When location is task-relevant, however, and responses are intentionally directed away (incompatible) or toward (compatible) the source of the stimulation, there is also an advantage for spatially compatible responses over spatially incompatible responses. Interestingly, a number of studies have demonstrated a reversed, or reduced, Simon effect following practice with a spatial incompatibility task. One interpretation of this finding is that practicing a spatial incompatibility task disables the natural tendency to respond toward stimuli. Here, the temporal dynamics of this stimulus-response (S-R) transfer were explored with speed-accuracy trade-offs (SATs). All experiments used the mixed-task paradigm in which Simon and spatial compatibility/incompatibility tasks were interleaved across blocks of trials. In general, bidirectional S-R transfer was observed: while the spatial incompatibility task had an influence on the Simon effect, the task-relevant S-R mapping of the Simon task also had a small impact on congruency effects within the spatial compatibility and incompatibility tasks. These effects were generally greater when the task contexts were similar. Moreover, the SAT analysis of performance in the Simon task demonstrated that the tendency to respond to the location of the stimulus was not eliminated because of the spatial incompatibility task. Rather, S-R transfer from the spatial incompatibility task appeared to partially mask the natural tendency to respond to the source of stimulation with a conflicting inclination to respond away from it. These findings support the use of SAT methodology to quantitatively describe rapid response tendencies. PMID:25191217

Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis

PubMed Central

Zhou, Ying; Levy, Jonathan I

2007-01-01

Background There has been growing interest among exposure assessors, epidemiologists, and policymakers in the concept of "hot spots", or more broadly, the "spatial extent" of impacts from traffic-related air pollutants. This review attempts to quantitatively synthesize findings about the spatial extent under various circumstances. Methods We include both the peer-reviewed literature and government reports, and focus on four significant air pollutants: carbon monoxide, benzene, nitrogen oxides, and particulate matter (including both ultrafine particle counts and fine particle mass). From the identified studies, we extracted information about significant factors that would be hypothesized to influence the spatial extent within the study, such as the study type (e.g., monitoring, air dispersion modeling, GIS-based epidemiological studies), focus on concentrations or health risks, pollutant under study, background concentration, emission rate, and meteorological factors, as well as the study's implicit or explicit definition of spatial extent. We supplement this meta-analysis with results from some illustrative atmospheric dispersion modeling. Results We found that pollutant characteristics and background concentrations best explained variability in previously published spatial extent estimates, with a modifying influence of local meteorology, once some extreme values based on health risk estimates were removed from the analysis. As hypothesized, inert pollutants with high background concentrations had the largest spatial extent (often demonstrating no significant gradient), and pollutants formed in near-source chemical reactions (e.g., nitrogen dioxide) had a larger spatial extent than pollutants depleted in near-source chemical reactions or removed through coagulation processes (e.g., nitrogen oxide and ultrafine particles). Our illustrative dispersion model illustrated the complex interplay of spatial extent definitions, emission rates, background concentrations, and meteorological conditions on spatial extent estimates even for non-reactive pollutants. Our findings indicate that, provided that a health risk threshold is not imposed, the spatial extent of impact for mobile sources reviewed in this study is on the order of 100–400 m for elemental carbon or particulate matter mass concentration (excluding background concentration), 200–500 m for nitrogen dioxide and 100–300 m for ultrafine particle counts. Conclusion First, to allow for meaningful comparisons across studies, it is important to state the definition of spatial extent explicitly, including the comparison method, threshold values, and whether background concentration is included. Second, the observation that the spatial extent is generally within a few hundred meters for highway or city roads demonstrates the need for high resolution modeling near the source. Finally, our findings emphasize that policymakers should be able to develop reasonable estimates of the "zone of influence" of mobile sources, provided that they can clarify the pollutant of concern, the general site characteristics, and the underlying definition of spatial extent that they wish to utilize. PMID:17519039

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain.

PubMed

Dominkovics, Pau; Granell, Carlos; Pérez-Navarro, Antoni; Casals, Martí; Orcau, Angels; Caylà, Joan A

2011-11-29

Health professionals and authorities strive to cope with heterogeneous data, services, and statistical models to support decision making on public health. Sophisticated analysis and distributed processing capabilities over geocoded epidemiological data are seen as driving factors to speed up control and decision making in these health risk situations. In this context, recent Web technologies and standards-based web services deployed on geospatial information infrastructures have rapidly become an efficient way to access, share, process, and visualize geocoded health-related information. Data used on this study is based on Tuberculosis (TB) cases registered in Barcelona city during 2009. Residential addresses are geocoded and loaded into a spatial database that acts as a backend database. The web-based application architecture and geoprocessing web services are designed according to the Representational State Transfer (REST) principles. These web processing services produce spatial density maps against the backend database. The results are focused on the use of the proposed web-based application to the analysis of TB cases in Barcelona. The application produces spatial density maps to ease the monitoring and decision making process by health professionals. We also include a discussion of how spatial density maps may be useful for health practitioners in such contexts. In this paper, we developed web-based client application and a set of geoprocessing web services to support specific health-spatial requirements. Spatial density maps of TB incidence were generated to help health professionals in analysis and decision-making tasks. The combined use of geographic information tools, map viewers, and geoprocessing services leads to interesting possibilities in handling health data in a spatial manner. In particular, the use of spatial density maps has been effective to identify the most affected areas and its spatial impact. This study is an attempt to demonstrate how web processing services together with web-based mapping capabilities suit the needs of health practitioners in epidemiological analysis scenarios.

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain

PubMed Central

2011-01-01

Background Health professionals and authorities strive to cope with heterogeneous data, services, and statistical models to support decision making on public health. Sophisticated analysis and distributed processing capabilities over geocoded epidemiological data are seen as driving factors to speed up control and decision making in these health risk situations. In this context, recent Web technologies and standards-based web services deployed on geospatial information infrastructures have rapidly become an efficient way to access, share, process, and visualize geocoded health-related information. Methods Data used on this study is based on Tuberculosis (TB) cases registered in Barcelona city during 2009. Residential addresses are geocoded and loaded into a spatial database that acts as a backend database. The web-based application architecture and geoprocessing web services are designed according to the Representational State Transfer (REST) principles. These web processing services produce spatial density maps against the backend database. Results The results are focused on the use of the proposed web-based application to the analysis of TB cases in Barcelona. The application produces spatial density maps to ease the monitoring and decision making process by health professionals. We also include a discussion of how spatial density maps may be useful for health practitioners in such contexts. Conclusions In this paper, we developed web-based client application and a set of geoprocessing web services to support specific health-spatial requirements. Spatial density maps of TB incidence were generated to help health professionals in analysis and decision-making tasks. The combined use of geographic information tools, map viewers, and geoprocessing services leads to interesting possibilities in handling health data in a spatial manner. In particular, the use of spatial density maps has been effective to identify the most affected areas and its spatial impact. This study is an attempt to demonstrate how web processing services together with web-based mapping capabilities suit the needs of health practitioners in epidemiological analysis scenarios. PMID:22126392

Physical vs. photolithographic patterning of plasma polymers: an investigation by ToF-SSIMS and multivariate analysis

PubMed Central

Mishra, Gautam; Easton, Christopher D.; McArthur, Sally L.

2009-01-01

Physical and photolithographic techniques are commonly used to create chemical patterns for a range of technologies including cell culture studies, bioarrays and other biomedical applications. In this paper, we describe the fabrication of chemical micropatterns from commonly used plasma polymers. Atomic force microcopy (AFM) imaging, Time-of-Flight Static Secondary Ion Mass Spectrometry (ToF-SSIMS) imaging and multivariate analysis have been employed to visualize the chemical boundaries created by these patterning techniques and assess the spatial and chemical resolution of the patterns. ToF-SSIMS analysis demonstrated that well defined chemical and spatial boundaries were obtained from photolithographic patterning, while the resolution of physical patterning via a transmission electron microscopy (TEM) grid varied depending on the properties of the plasma system including the substrate material. In general, physical masking allowed diffusion of the plasma species below the mask and bleeding of the surface chemistries. Multivariate analysis techniques including Principal Component Analysis (PCA) and Region of Interest (ROI) assessment were used to investigate the ToF-SSIMS images of a range of different plasma polymer patterns. In the most challenging case, where two strongly reacting polymers, allylamine and acrylic acid were deposited, PCA confirmed the fabrication of micropatterns with defined spatial resolution. ROI analysis allowed for the identification of an interface between the two plasma polymers for patterns fabricated using the photolithographic technique which has been previously overlooked. This study clearly demonstrated the versatility of photolithographic patterning for the production of multichemistry plasma polymer arrays and highlighted the need for complimentary characterization and analytical techniques during the fabrication plasma polymer micropatterns. PMID:19950941

Prostate-specific membrane antigen PET/MRI validation of MR textural analysis for detection of transition zone prostate cancer.

PubMed

Bates, Anthony; Miles, Kenneth

2017-12-01

To validate MR textural analysis (MRTA) for detection of transition zone (TZ) prostate cancer through comparison with co-registered prostate-specific membrane antigen (PSMA) PET-MR. Retrospective analysis was performed for 30 men who underwent simultaneous PSMA PET-MR imaging for staging of prostate cancer. Thirty texture features were derived from each manually contoured T2-weighted, transaxial, prostatic TZ using texture analysis software that applies a spatial band-pass filter and quantifies texture through histogram analysis. Texture features of the TZ were compared to PSMA expression on the corresponding PET images. The Benjamini-Hochberg correction controlled the false discovery rate at <5%. Eighty-eight T2-weighted images in 18 patients demonstrated abnormal PSMA expression within the TZ on PET-MR. 123 images were PSMA negative. Based on the corrected p-value of 0.005, significant differences between PSMA positive and negative slices were found for 16 texture parameters: Standard deviation and mean of positive pixels for all spatial filters (p = <0.0001 for both at all spatial scaling factor (SSF) values) and mean intensity following filtration for SSF 3-6 mm (p = 0.0002-0.0018). Abnormal expression of PSMA within the TZ is associated with altered texture on T2-weighted MR, providing validation of MRTA for the detection of TZ prostate cancer. • Prostate transition zone (TZ) MR texture analysis may assist in prostate cancer detection. • Abnormal transition zone PSMA expression correlates with altered texture on T2-weighted MR. • TZ with abnormal PSMA expression demonstrates significantly reduced MI, SD and MPP.

Spatial variation of volcanic rock geochemistry in the Virunga Volcanic Province: Statistical analysis of an integrated database

NASA Astrophysics Data System (ADS)

Barette, Florian; Poppe, Sam; Smets, Benoît; Benbakkar, Mhammed; Kervyn, Matthieu

2017-10-01

We present an integrated, spatially-explicit database of existing geochemical major-element analyses available from (post-) colonial scientific reports, PhD Theses and international publications for the Virunga Volcanic Province, located in the western branch of the East African Rift System. This volcanic province is characterised by alkaline volcanism, including silica-undersaturated, alkaline and potassic lavas. The database contains a total of 908 geochemical analyses of eruptive rocks for the entire volcanic province with a localisation for most samples. A preliminary analysis of the overall consistency of the database, using statistical techniques on sets of geochemical analyses with contrasted analytical methods or dates, demonstrates that the database is consistent. We applied a principal component analysis and cluster analysis on whole-rock major element compositions included in the database to study the spatial variation of the chemical composition of eruptive products in the Virunga Volcanic Province. These statistical analyses identify spatially distributed clusters of eruptive products. The known geochemical contrasts are highlighted by the spatial analysis, such as the unique geochemical signature of Nyiragongo lavas compared to other Virunga lavas, the geochemical heterogeneity of the Bulengo area, and the trachyte flows of Karisimbi volcano. Most importantly, we identified separate clusters of eruptive products which originate from primitive magmatic sources. These lavas of primitive composition are preferentially located along NE-SW inherited rift structures, often at distance from the central Virunga volcanoes. Our results illustrate the relevance of a spatial analysis on integrated geochemical data for a volcanic province, as a complement to classical petrological investigations. This approach indeed helps to characterise geochemical variations within a complex of magmatic systems and to identify specific petrologic and geochemical investigations that should be tackled within a study area.

Scanning photoelectron microscope for nanoscale three-dimensional spatial-resolved electron spectroscopy for chemical analysis.

PubMed

Horiba, K; Nakamura, Y; Nagamura, N; Toyoda, S; Kumigashira, H; Oshima, M; Amemiya, K; Senba, Y; Ohashi, H

2011-11-01

In order to achieve nondestructive observation of the three-dimensional spatially resolved electronic structure of solids, we have developed a scanning photoelectron microscope system with the capability of depth profiling in electron spectroscopy for chemical analysis (ESCA). We call this system 3D nano-ESCA. For focusing the x-ray, a Fresnel zone plate with a diameter of 200 μm and an outermost zone width of 35 nm is used. In order to obtain the angular dependence of the photoelectron spectra for the depth-profile analysis without rotating the sample, we adopted a modified VG Scienta R3000 analyzer with an acceptance angle of 60° as a high-resolution angle-resolved electron spectrometer. The system has been installed at the University-of-Tokyo Materials Science Outstation beamline, BL07LSU, at SPring-8. From the results of the line-scan profiles of the poly-Si/high-k gate patterns, we achieved a total spatial resolution better than 70 nm. The capability of our system for pinpoint depth-profile analysis and high-resolution chemical state analysis is demonstrated. © 2011 American Institute of Physics

Hyperspectral Image Denoising Using a Nonlocal Spectral Spatial Principal Component Analysis

NASA Astrophysics Data System (ADS)

Li, D.; Xu, L.; Peng, J.; Ma, J.

2018-04-01

Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification and so on. In this paper, we develop a noise reduction method based on principal component analysis (PCA) for hyperspectral imagery, which is dependent on the assumption that the noise can be removed by selecting the leading principal components. The main contribution of paper is to introduce the spectral spatial structure and nonlocal similarity of the HSIs into the PCA denoising model. PCA with spectral spatial structure can exploit spectral correlation and spatial correlation of HSI by using 3D blocks instead of 2D patches. Nonlocal similarity means the similarity between the referenced pixel and other pixels in nonlocal area, where Mahalanobis distance algorithm is used to estimate the spatial spectral similarity by calculating the distance in 3D blocks. The proposed method is tested on both simulated and real hyperspectral images, the results demonstrate that the proposed method is superior to several other popular methods in HSI denoising.

Additional studies of forest classification accuracy as influenced by multispectral scanner spatial resolution

NASA Technical Reports Server (NTRS)

Sadowski, F. E.; Sarno, J. E.

1976-01-01

First, an analysis of forest feature signatures was used to help explain the large variation in classification accuracy that can occur among individual forest features for any one case of spatial resolution and the inconsistent changes in classification accuracy that were demonstrated among features as spatial resolution was degraded. Second, the classification rejection threshold was varied in an effort to reduce the large proportion of unclassified resolution elements that previously appeared in the processing of coarse resolution data when a constant rejection threshold was used for all cases of spatial resolution. For the signature analysis, two-channel ellipse plots showing the feature signature distributions for several cases of spatial resolution indicated that the capability of signatures to correctly identify their respective features is dependent on the amount of statistical overlap among signatures. Reductions in signature variance that occur in data of degraded spatial resolution may not necessarily decrease the amount of statistical overlap among signatures having large variance and small mean separations. Features classified by such signatures may thus continue to have similar amounts of misclassified elements in coarser resolution data, and thus, not necessarily improve in classification accuracy.

The role of visualization in learning from computer-based images

NASA Astrophysics Data System (ADS)

Piburn, Michael D.; Reynolds, Stephen J.; McAuliffe, Carla; Leedy, Debra E.; Birk, James P.; Johnson, Julia K.

2005-05-01

Among the sciences, the practice of geology is especially visual. To assess the role of spatial ability in learning geology, we designed an experiment using: (1) web-based versions of spatial visualization tests, (2) a geospatial test, and (3) multimedia instructional modules built around QuickTime Virtual Reality movies. Students in control and experimental sections were administered measures of spatial orientation and visualization, as well as a content-based geospatial examination. All subjects improved significantly in their scores on spatial visualization and the geospatial examination. There was no change in their scores on spatial orientation. A three-way analysis of variance, with the geospatial examination as the dependent variable, revealed significant main effects favoring the experimental group and a significant interaction between treatment and gender. These results demonstrate that spatial ability can be improved through instruction, that learning of geological content will improve as a result, and that differences in performance between the genders can be eliminated.

Summer spatial patterning of chukars in relation to free water in Western Utah

USGS Publications Warehouse

Larsen, R.T.; Bissonette, J.A.; Flinders, J.T.; Hooten, M.B.; Wilson, T.L.

2010-01-01

Free water is considered important to wildlife in arid regions. In the western United States, thousands of water developments have been built to benefit wildlife in arid landscapes. Agencies and researchers have yet to clearly demonstrate their effectiveness. We combined a spatial analysis of summer chukar (Alectoris chukar) covey locations with dietary composition analysis in western Utah. Our specific objectives were to determine if chukars showed a spatial pattern that suggested association with free water in four study areas and to document summer dietary moisture content in relation to average distance from water. The observed data for the Cedar Mountains study area fell within the middle of the random mean distance to water distribution suggesting no association with free water. The observed mean distance to water for the other three areas was much closer than expected compared to a random spatial process, suggesting the importance of free water to these populations. Dietary moisture content of chukar food items from the Cedar Mountains (59%) was significantly greater (P < 0.05) than that of birds from Box Elder (44%) and Keg-Dugway (44%). Water developments on the Cedar Mountains are likely ineffective for chukars. Spatial patterns on the other areas, however, suggest association with free water and our results demonstrate the need for site-specific considerations. Researchers should be aware of the potential to satisfy water demand with pre-formed and metabolic water for a variety of species in studies that address the effects of wildlife water developments. We encourage incorporation of spatial structure in model error components in future ecological research. ?? Springer Science+Business Media B.V. 2009.

Distance to health services affects local-level vaccine efficacy for pneumococcal conjugate vaccine (PCV) among rural Filipino children

PubMed Central

Root, Elisabeth Dowling; Lucero, Marilla; Nohynek, Hanna; Anthamatten, Peter; Thomas, Deborah S. K.; Tallo, Veronica; Tanskanen, Antti; Quiambao, Beatriz P.; Puumalainen, Taneli; Lupisan, Socorro P.; Ruutu, Petri; Ladesma, Erma; Williams, Gail M.; Riley, Ian; Simões, Eric A. F.

2014-01-01

Pneumococcal conjugate vaccines (PCVs) have demonstrated efficacy against childhood pneumococcal disease in several regions globally. We demonstrate how spatial epidemiological analysis of a PCV trial can assist in developing vaccination strategies that target specific geographic subpopulations at greater risk for pneumococcal pneumonia. We conducted a secondary analysis of a randomized, placebo-controlled, double-blind vaccine trial that examined the efficacy of an 11-valent PCV among children less than 2 y of age in Bohol, Philippines. Trial data were linked to the residential location of each participant using a geographic information system. We use spatial interpolation methods to create smoothed surface maps of vaccination rates and local-level vaccine efficacy across the study area. We then measure the relationship between distance to the main study hospital and local-level vaccine efficacy, controlling for ecological factors, using spatial autoregressive models with spatial autoregressive disturbances. We find a significant amount of spatial variation in vaccination rates across the study area. For the primary study endpoint vaccine efficacy increased with distance from the main study hospital from −14% for children living less than 1.5 km from Bohol Regional Hospital (BRH) to 55% for children living greater than 8.5 km from BRH. Spatial regression models indicated that after adjustment for ecological factors, distance to the main study hospital was positively related to vaccine efficacy, increasing at a rate of 4.5% per kilometer distance. Because areas with poor access to care have significantly higher VE, targeted vaccination of children in these areas might allow for a more effective implementation of global programs. PMID:24550454

Recent variations in seasonality of temperature and precipitation in Canada, 1976-95

NASA Astrophysics Data System (ADS)

Whitfield, Paul H.; Bodtker, Karin; Cannon, Alex J.

2002-11-01

A previously reported analysis of rehabilitated monthly temperature and precipitation time series for several hundred stations across Canada showed generally spatially coherent patterns of variation between two decades (1976-85 and 1986-95). The present work expands that analysis to finer time scales and a greater number of stations. We demonstrate how the finer temporal resolution, at 5 day or 11 day intervals, increases the separation between clusters of recent variations in seasonal patterns of temperature and precipitation. We also expand the analysis by increasing the number of stations from only rehabilitated monthly data sets to rehabilitated daily sets, then to approximately 1500 daily observation stations. This increases the spatial density of data and allows a finer spatial resolution of patterns between the two decades. We also examine the success of clustering partial records, i.e. sites where the data record is incomplete. The intent of this study was to be consistent with previous work and explore how greater temporal and spatial detail in the climate data affects the resolution of patterns of recent climate variations. The variations we report for temperature and precipitation are taking place at different temporal and spatial scales. Further, the spatial patterns are much broader than local climate regions and ecozones, indicating that the differences observed may be the result of variations in atmospheric circulation.

Geospatial and machine learning techniques for wicked social science problems: analysis of crash severity on a regional highway corridor

NASA Astrophysics Data System (ADS)

Effati, Meysam; Thill, Jean-Claude; Shabani, Shahin

2015-04-01

The contention of this paper is that many social science research problems are too "wicked" to be suitably studied using conventional statistical and regression-based methods of data analysis. This paper argues that an integrated geospatial approach based on methods of machine learning is well suited to this purpose. Recognizing the intrinsic wickedness of traffic safety issues, such approach is used to unravel the complexity of traffic crash severity on highway corridors as an example of such problems. The support vector machine (SVM) and coactive neuro-fuzzy inference system (CANFIS) algorithms are tested as inferential engines to predict crash severity and uncover spatial and non-spatial factors that systematically relate to crash severity, while a sensitivity analysis is conducted to determine the relative influence of crash severity factors. Different specifications of the two methods are implemented, trained, and evaluated against crash events recorded over a 4-year period on a regional highway corridor in Northern Iran. Overall, the SVM model outperforms CANFIS by a notable margin. The combined use of spatial analysis and artificial intelligence is effective at identifying leading factors of crash severity, while explicitly accounting for spatial dependence and spatial heterogeneity effects. Thanks to the demonstrated effectiveness of a sensitivity analysis, this approach produces comprehensive results that are consistent with existing traffic safety theories and supports the prioritization of effective safety measures that are geographically targeted and behaviorally sound on regional highway corridors.

Spatially Resolved Chemical Imaging for Biosignature Analysis: Terrestrial and Extraterrestrial Examples

NASA Astrophysics Data System (ADS)

Bhartia, R.; Wanger, G.; Orphan, V. J.; Fries, M.; Rowe, A. R.; Nealson, K. H.; Abbey, W. J.; DeFlores, L. P.; Beegle, L. W.

2014-12-01

Detection of in situ biosignatures on terrestrial and planetary missions is becoming increasingly more important. Missions that target the Earth's deep biosphere, Mars, moons of Jupiter (including Europa), moons of Saturn (Titan and Enceladus), and small bodies such as asteroids or comets require methods that enable detection of materials for both in-situ analysis that preserve context and as a means to select high priority sample for return to Earth. In situ instrumentation for biosignature detection spans a wide range of analytical and spectroscopic methods that capitalize on amino acid distribution, chirality, lipid composition, isotopic fractionation, or textures that persist in the environment. Many of the existing analytical instruments are bulk analysis methods and while highly sensitive, these require sample acquisition and sample processing. However, by combining with triaging spectroscopic methods, biosignatures can be targeted on a surface and preserve spatial context (including mineralogy, textures, and organic distribution). To provide spatially correlated chemical analysis at multiple spatial scales (meters to microns) we have employed a dual spectroscopic approach that capitalizes on high sensitivity deep UV native fluorescence detection and high specificity deep UV Raman analysis.. Recently selected as a payload on the Mars 2020 mission, SHERLOC incorporates these optical methods for potential biosignatures detection on Mars. We present data from both Earth analogs that operate as our only examples known biosignatures and meteorite samples that provide an example of abiotic organic formation, and demonstrate how provenance effects the spatial distribution and composition of organics.

Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinson's disease.

PubMed

Zhuang, Xiaowei; Walsh, Ryan R; Sreenivasan, Karthik; Yang, Zhengshi; Mishra, Virendra; Cordes, Dietmar

2018-05-15

The dynamics of the brain's intrinsic networks have been recently studied using co-activation pattern (CAP) analysis. The CAP method relies on few model assumptions and CAP-based measurements provide quantitative information of network temporal dynamics. One limitation of existing CAP-related methods is that the computed CAPs share considerable spatial overlap that may or may not be functionally distinct relative to specific network dynamics. To more accurately describe network dynamics with spatially distinct CAPs, and to compare network dynamics between different populations, a novel data-driven CAP group analysis method is proposed in this study. In the proposed method, a dominant-CAP (d-CAP) set is synthesized across CAPs from multiple clustering runs for each group with the constraint of low spatial similarities among d-CAPs. Alternating d-CAPs with less overlapping spatial patterns can better capture overall network dynamics. The number of d-CAPs, the temporal fraction and spatial consistency of each d-CAP, and the subject-specific switching probability among all d-CAPs are then calculated for each group and used to compare network dynamics between groups. The spatial dissimilarities among d-CAPs computed with the proposed method were first demonstrated using simulated data. High consistency between simulated ground-truth and computed d-CAPs was achieved, and detailed comparisons between the proposed method and existing CAP-based methods were conducted using simulated data. In an effort to physiologically validate the proposed technique and investigate network dynamics in a relevant brain network disorder, the proposed method was then applied to data from the Parkinson's Progression Markers Initiative (PPMI) database to compare the network dynamics in Parkinson's disease (PD) and normal control (NC) groups. Fewer d-CAPs, skewed distribution of temporal fractions of d-CAPs, and reduced switching probabilities among final d-CAPs were found in most networks in the PD group, as compared to the NC group. Furthermore, an overall negative association between switching probability among d-CAPs and disease severity was observed in most networks in the PD group as well. These results expand upon previous findings from in vivo electrophysiological recording studies in PD. Importantly, this novel analysis also demonstrates that changes in network dynamics can be measured using resting-state fMRI data from subjects with early stage PD. Copyright © 2018 Elsevier Inc. All rights reserved.

Differential spatial activity patterns of acupuncture by a machine learning based analysis

NASA Astrophysics Data System (ADS)

You, Youbo; Bai, Lijun; Xue, Ting; Zhong, Chongguang; Liu, Zhenyu; Tian, Jie

2011-03-01

Acupoint specificity, lying at the core of the Traditional Chinese Medicine, underlies the theoretical basis of acupuncture application. However, recent studies have reported that acupuncture stimulation at nonacupoint and acupoint can both evoke similar signal intensity decreases in multiple regions. And these regions were spatially overlapped. We used a machine learning based Support Vector Machine (SVM) approach to elucidate the specific neural response pattern induced by acupuncture stimulation. Group analysis demonstrated that stimulation at two different acupoints (belong to the same nerve segment but different meridians) could elicit distinct neural response patterns. Our findings may provide evidence for acupoint specificity.

Submillimeter-scale heterogeneity of labile phosphorus in sediments characterized by diffusive gradients in thin films and spatial analysis.

PubMed

Meng, Yuting; Ding, Shiming; Gong, Mengdan; Chen, Musong; Wang, Yan; Fan, Xianfang; Shi, Lei; Zhang, Chaosheng

2018-03-01

Sediments have a heterogeneous distribution of labile redox-sensitive elements due to a drastic downward transition from oxic to anoxic condition as a result of organic matter degradation. Characterization of the heterogeneous nature of sediments is vital for understanding of small-scale biogeochemical processes. However, there are limited reports on the related specialized methodology. In this study, the monthly distributions of labile phosphorus (P), a redox-sensitive limiting nutrient, were measured in the eutrophic Lake Taihu by Zr-oxide diffusive gradients in thin films (Zr-oxide DGT) on a two-dimensional (2D) submillimeter level. Geographical information system (GIS) techniques were used to visualize the labile P distribution at such a micro-scale, showing that the DGT-labile P was low in winter and high in summer. Spatial analysis methods, including semivariogram and Moran's I, were used to quantify the spatial variation of DGT-labile P. The distribution of DGT-labile P had clear submillimeter-scale spatial patterns with significant spatial autocorrelation during the whole year and displayed seasonal changes. High values of labile P with strong spatial variation were observed in summer, while low values of labile P with relatively uniform spatial patterns were detected in winter, demonstrating the strong influences of temperature on the mobility and spatial distribution of P in sediment profiles. Copyright © 2017 Elsevier Ltd. All rights reserved.

Regulation of Oligomeric Organization of the Serotonin 5-Hydroxytryptamine 2C (5-HT2C) Receptor Observed by Spatial Intensity Distribution Analysis*

PubMed Central

Ward, Richard J.; Pediani, John D.; Godin, Antoine G.; Milligan, Graeme

2015-01-01

The questions of whether G protein-coupled receptors exist as monomers, dimers, and/or oligomers and if these species interconvert in a ligand-dependent manner are among the most contentious current issues in biology. When employing spatial intensity distribution analysis to laser scanning confocal microscope images of cells stably expressing either a plasma membrane-associated form of monomeric enhanced green fluorescent protein (eGFP) or a tandem version of this fluorophore, the eGFP tandem was identified as a dimer. Similar studies on cells stably expressing an eGFP-tagged form of the epidermal growth factor receptor demonstrated that, although largely a monomer in the basal state, this receptor rapidly became predominantly dimeric upon the addition of its ligand epidermal growth factor. In cells induced to express an eGFP-tagged form of the serotonin 5-hydroxytryptamine 2C (5-HT2C) receptor, global analysis of construct quantal brightness was consistent with the predominant form of the receptor being dimeric. However, detailed spatial intensity distribution analysis demonstrated the presence of multiple forms ranging from monomers to higher-order oligomers. Furthermore, treatment with chemically distinct 5-HT2C receptor antagonists resulted in a time-dependent change in the quaternary organization to one in which there was a preponderance of receptor monomers. This antagonist-mediated effect was reversible, because washout of the ligand resulted in the regeneration of many of the oligomeric forms of the receptor. PMID:25825490

`spup' - An R Package for Analysis of Spatial Uncertainty Propagation and Application to Trace Gas Emission Simulations

NASA Astrophysics Data System (ADS)

Sawicka, K.; Breuer, L.; Houska, T.; Santabarbara Ruiz, I.; Heuvelink, G. B. M.

2016-12-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Advances in uncertainty propagation analysis and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability, including case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the `spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo techniques, as well as several uncertainty visualization functions. Here we will demonstrate that the 'spup' package is an effective and easy-to-use tool to be applied even in a very complex study case, and that it can be used in multi-disciplinary research and model-based decision support. As an example, we use the ecological LandscapeDNDC model to analyse propagation of uncertainties associated with spatial variability of the model driving forces such as rainfall, nitrogen deposition and fertilizer inputs. The uncertainty propagation is analysed for the prediction of emissions of N2O and CO2 for a German low mountainous, agriculturally developed catchment. The study tests the effect of spatial correlations on spatially aggregated model outputs, and could serve as an advice for developing best management practices and model improvement strategies.

An implicit spatial and high-order temporal finite difference scheme for 2D acoustic modelling

NASA Astrophysics Data System (ADS)

Wang, Enjiang; Liu, Yang

2018-01-01

The finite difference (FD) method exhibits great superiority over other numerical methods due to its easy implementation and small computational requirement. We propose an effective FD method, characterised by implicit spatial and high-order temporal schemes, to reduce both the temporal and spatial dispersions simultaneously. For the temporal derivative, apart from the conventional second-order FD approximation, a special rhombus FD scheme is included to reach high-order accuracy in time. Compared with the Lax-Wendroff FD scheme, this scheme can achieve nearly the same temporal accuracy but requires less floating-point operation times and thus less computational cost when the same operator length is adopted. For the spatial derivatives, we adopt the implicit FD scheme to improve the spatial accuracy. Apart from the existing Taylor series expansion-based FD coefficients, we derive the least square optimisation based implicit spatial FD coefficients. Dispersion analysis and modelling examples demonstrate that, our proposed method can effectively decrease both the temporal and spatial dispersions, thus can provide more accurate wavefields.

Regional-specific Stochastic Simulation of Spatially-distributed Ground-motion Time Histories using Wavelet Packet Analysis

NASA Astrophysics Data System (ADS)

Huang, D.; Wang, G.

2014-12-01

Stochastic simulation of spatially distributed ground-motion time histories is important for performance-based earthquake design of geographically distributed systems. In this study, we develop a novel technique to stochastically simulate regionalized ground-motion time histories using wavelet packet analysis. First, a transient acceleration time history is characterized by wavelet-packet parameters proposed by Yamamoto and Baker (2013). The wavelet-packet parameters fully characterize ground-motion time histories in terms of energy content, time- frequency-domain characteristics and time-frequency nonstationarity. This study further investigates the spatial cross-correlations of wavelet-packet parameters based on geostatistical analysis of 1500 regionalized ground motion data from eight well-recorded earthquakes in California, Mexico, Japan and Taiwan. The linear model of coregionalization (LMC) is used to develop a permissible spatial cross-correlation model for each parameter group. The geostatistical analysis of ground-motion data from different regions reveals significant dependence of the LMC structure on regional site conditions, which can be characterized by the correlation range of Vs30 in each region. In general, the spatial correlation and cross-correlation of wavelet-packet parameters are stronger if the site condition is more homogeneous. Using the regional-specific spatial cross-correlation model and cokriging technique, wavelet packet parameters at unmeasured locations can be best estimated, and regionalized ground-motion time histories can be synthesized. Case studies and blind tests demonstrated that the simulated ground motions generally agree well with the actual recorded data, if the influence of regional-site conditions is considered. The developed method has great potential to be used in computational-based seismic analysis and loss estimation in a regional scale.

Search strategy selection in the Morris water maze indicates allocentric map formation during learning that underpins spatial memory formation.

PubMed

Rogers, Jake; Churilov, Leonid; Hannan, Anthony J; Renoir, Thibault

2017-03-01

Using a Matlab classification algorithm, we demonstrate that a highly salient distal cue array is required for significantly increased likelihoods of spatial search strategy selection during Morris water maze spatial learning. We hypothesized that increased spatial search strategy selection during spatial learning would be the key measure demonstrating the formation of an allocentric map to the escape location. Spatial memory, as indicated by quadrant preference for the area of the pool formally containing the hidden platform, was assessed as the main measure that this allocentric map had formed during spatial learning. Our C57BL/6J wild-type (WT) mice exhibit quadrant preference in the highly salient cue paradigm but not the low, corresponding with a 120% increase in the odds of a spatial search strategy selection during learning. In contrast, quadrant preference remains absent in serotonin 1A receptor (5-HT 1A R) knockout (KO) mice, who exhibit impaired search strategy selection during spatial learning. Additionally, we also aimed to assess the impact of the quality of the distal cue array on the spatial learning curves of both latency to platform and path length using mixed-effect regression models and found no significant associations or interactions. In contrast, we demonstrated that the spatial learning curve for search strategy selection was absent during training in the low saliency paradigm. Therefore, we propose that allocentric search strategy selection during spatial learning is the learning parameter in mice that robustly indicates the formation of a cognitive map for the escape goal location. These results also suggest that both latency to platform and path length spatial learning curves do not discriminate between allocentric and egocentric spatial learning and do not reliably predict spatial memory formation. We also show that spatial memory, as indicated by the absolute time in the quadrant formerly containing the hidden platform alone (without reference to the other areas of the pool), was not sensitive to cue saliency or impaired in 5-HT 1A R KO mice. Importantly, in the absence of a search strategy analysis, this suggests that to establish that the Morris water maze has worked (i.e. control mice have formed an allocentric map to the escape goal location), a measure of quadrant preference needs to be reported to establish spatial memory formation. This has implications for studies that claim hippocampal functioning is impaired using latency to platform or path length differences within the existing Morris water maze literature. Copyright © 2016 Elsevier Inc. All rights reserved.

[Design and implementation of Geographical Information System on prevention and control of cholera].

PubMed

Li, Xiu-jun; Fang, Li-qun; Wang, Duo-chun; Wang, Lu-xi; Li, Ya-pin; Li, Yan-li; Yang, Hong; Kan, Biao; Cao, Wu-chun

2012-04-01

To build the Geographical Information System (GIS) database for prevention and control of cholera programs as well as using management analysis and function demonstration to show the spatial attribute of cholera. Data from case reporting system regarding diarrhoea, vibrio cholerae, serotypes of vibrio cholerae at the surveillance spots and seafoods, as well as surveillance data on ambient environment and climate were collected. All the data were imported to system database to show the incidence of vibrio cholerae in different provinces, regions and counties to support the spatial analysis through the spatial analysis of GIS. The epidemic trends of cholera, seasonal characteristics of the cholera and the variation of the vibrio cholerae with times were better understood. Information on hotspots, regions and time of epidemics was collected, and helpful in providing risk prediction on the incidence of vibrio cholerae. The exploitation of the software can predict and simulate the spatio-temporal risks, so as to provide guidance for the prevention and control of the disease.

Stochastic analysis of multiphase flow in porous media: II. Numerical simulations

NASA Astrophysics Data System (ADS)

Abin, A.; Kalurachchi, J. J.; Kemblowski, M. W.; Chang, C.-M.

1996-08-01

The first paper (Chang et al., 1995b) of this two-part series described the stochastic analysis using spectral/perturbation approach to analyze steady state two-phase (water and oil) flow in a, liquid-unsaturated, three fluid-phase porous medium. In this paper, the results between the numerical simulations and closed-form expressions obtained using the perturbation approach are compared. We present the solution to the one-dimensional, steady-state oil and water flow equations. The stochastic input processes are the spatially correlated logk where k is the intrinsic permeability and the soil retention parameter, α. These solutions are subsequently used in the numerical simulations to estimate the statistical properties of the key output processes. The comparison between the results of the perturbation analysis and numerical simulations showed a good agreement between the two methods over a wide range of logk variability with three different combinations of input stochastic processes of logk and soil parameter α. The results clearly demonstrated the importance of considering the spatial variability of key subsurface properties under a variety of physical scenarios. The variability of both capillary pressure and saturation is affected by the type of input stochastic process used to represent the spatial variability. The results also demonstrated the applicability of perturbation theory in predicting the system variability and defining effective fluid properties through the ergodic assumption.

Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state FMRI.

PubMed

Spisák, Tamás; Jakab, András; Kis, Sándor A; Opposits, Gábor; Aranyi, Csaba; Berényi, Ervin; Emri, Miklós

2014-01-01

Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.

Spectral refractive index assessment of turbid samples by combining spatial frequency near-infrared spectroscopy with Kramers-Kronig analysis

NASA Astrophysics Data System (ADS)

Meitav, Omri; Shaul, Oren; Abookasis, David

2018-03-01

A practical algorithm for estimating the wavelength-dependent refractive index (RI) of a turbid sample in the spatial frequency domain with the aid of Kramers-Kronig (KK) relations is presented. In it, phase-shifted sinusoidal patterns (structured illumination) are serially projected at a high spatial frequency onto the sample surface (mouse scalp) at different near-infrared wavelengths while a camera mounted normally to the sample surface captures the reflected diffuse light. In the offline analysis pipeline, recorded images at each wavelength are converted to spatial absorption maps by logarithmic function, and once the absorption coefficient information is obtained, the imaginary part (k) of the complex RI (CRI), based on Maxell's equations, can be calculated. Using the data represented by k, the real part of the CRI (n) is then resolved by KK analysis. The wavelength dependence of n ( λ ) is then fitted separately using four standard dispersion models: Cornu, Cauchy, Conrady, and Sellmeier. In addition, three-dimensional surface-profile distribution of n is provided based on phase profilometry principles and a phase-unwrapping-based phase-derivative-variance algorithm. Experimental results demonstrate the capability of the proposed idea for sample's determination of a biological sample's RI value.

Multi-scale imaging and informatics pipeline for in situ pluripotent stem cell analysis.

PubMed

Gorman, Bryan R; Lu, Junjie; Baccei, Anna; Lowry, Nathan C; Purvis, Jeremy E; Mangoubi, Rami S; Lerou, Paul H

2014-01-01

Human pluripotent stem (hPS) cells are a potential source of cells for medical therapy and an ideal system to study fate decisions in early development. However, hPS cells cultured in vitro exhibit a high degree of heterogeneity, presenting an obstacle to clinical translation. hPS cells grow in spatially patterned colony structures, necessitating quantitative single-cell image analysis. We offer a tool for analyzing the spatial population context of hPS cells that integrates automated fluorescent microscopy with an analysis pipeline. It enables high-throughput detection of colonies at low resolution, with single-cellular and sub-cellular analysis at high resolutions, generating seamless in situ maps of single-cellular data organized by colony. We demonstrate the tool's utility by analyzing inter- and intra-colony heterogeneity of hPS cell cycle regulation and pluripotency marker expression. We measured the heterogeneity within individual colonies by analyzing cell cycle as a function of distance. Cells loosely associated with the outside of the colony are more likely to be in G1, reflecting a less pluripotent state, while cells within the first pluripotent layer are more likely to be in G2, possibly reflecting a G2/M block. Our multi-scale analysis tool groups colony regions into density classes, and cells belonging to those classes have distinct distributions of pluripotency markers and respond differently to DNA damage induction. Lastly, we demonstrate that our pipeline can robustly handle high-content, high-resolution single molecular mRNA FISH data by using novel image processing techniques. Overall, the imaging informatics pipeline presented offers a novel approach to the analysis of hPS cells that includes not only single cell features but also colony wide, and more generally, multi-scale spatial configuration.

Visuospatial selective attention in chickens.

PubMed

Sridharan, Devarajan; Ramamurthy, Deepa L; Schwarz, Jason S; Knudsen, Eric I

2014-05-13

Voluntary control of attention promotes intelligent, adaptive behaviors by enabling the selective processing of information that is most relevant for making decisions. Despite extensive research on attention in primates, the capacity for selective attention in nonprimate species has never been quantified. Here we demonstrate selective attention in chickens by applying protocols that have been used to characterize visual spatial attention in primates. Chickens were trained to localize and report the vertical position of a target in the presence of task-relevant distracters. A spatial cue, the location of which varied across individual trials, indicated the horizontal, but not vertical, position of the upcoming target. Spatial cueing improved localization performance: accuracy (d') increased and reaction times decreased in a space-specific manner. Distracters severely impaired perceptual performance, and this impairment was greatly reduced by spatial cueing. Signal detection analysis with an "indecision" model demonstrated that spatial cueing significantly increased choice certainty in localizing targets. By contrast, error-aversion certainty (certainty of not making an error) remained essentially constant across cueing protocols, target contrasts, and individuals. The results show that chickens shift spatial attention rapidly and dynamically, following principles of stimulus selection that closely parallel those documented in primates. The findings suggest that the mechanisms that control attention have been conserved through evolution, and establish chickens--a highly visual species that is easily trained and amenable to cutting-edge experimental technologies--as an attractive model for linking behavior to neural mechanisms of selective attention.

COMPOSITE SAMPLING FOR SOIL VOC ANALYSIS

EPA Science Inventory

Data published by numerous researchers over the last decade demonstrate that there is a high degree of spatial variability in the measurement of volatile organic compounds (VOCs) in soil at contaminated waste sites. This phenomenon is confounded by the use of a small sample aliqu...

Crack Detection with Lamb Wave Wavenumber Analysis

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Leckey, Cara; Rogge, Matt; Yu, Lingyu

2013-01-01

In this work, we present our study of Lamb wave crack detection using wavenumber analysis. The aim is to demonstrate the application of wavenumber analysis to 3D Lamb wave data to enable damage detection. The 3D wavefields (including vx, vy and vz components) in time-space domain contain a wealth of information regarding the propagating waves in a damaged plate. For crack detection, three wavenumber analysis techniques are used: (i) two dimensional Fourier transform (2D-FT) which can transform the time-space wavefield into frequency-wavenumber representation while losing the spatial information; (ii) short space 2D-FT which can obtain the frequency-wavenumber spectra at various spatial locations, resulting in a space-frequency-wavenumber representation; (iii) local wavenumber analysis which can provide the distribution of the effective wavenumbers at different locations. All of these concepts are demonstrated through a numerical simulation example of an aluminum plate with a crack. The 3D elastodynamic finite integration technique (EFIT) was used to obtain the 3D wavefields, of which the vz (out-of-plane) wave component is compared with the experimental measurement obtained from a scanning laser Doppler vibrometer (SLDV) for verification purposes. The experimental and simulated results are found to be in close agreement. The application of wavenumber analysis on 3D EFIT simulation data shows the effectiveness of the analysis for crack detection. Keywords: : Lamb wave, crack detection, wavenumber analysis, EFIT modeling

Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce.

PubMed

Aji, Ablimit; Sun, Xiling; Vo, Hoang; Liu, Qioaling; Lee, Rubao; Zhang, Xiaodong; Saltz, Joel; Wang, Fusheng

2013-11-01

The proliferation of GPS-enabled devices, and the rapid improvement of scientific instruments have resulted in massive amounts of spatial data in the last decade. Support of high performance spatial queries on large volumes data has become increasingly important in numerous fields, which requires a scalable and efficient spatial data warehousing solution as existing approaches exhibit scalability limitations and efficiency bottlenecks for large scale spatial applications. In this demonstration, we present Hadoop-GIS - a scalable and high performance spatial query system over MapReduce. Hadoop-GIS provides an efficient spatial query engine to process spatial queries, data and space based partitioning, and query pipelines that parallelize queries implicitly on MapReduce. Hadoop-GIS also provides an expressive, SQL-like spatial query language for workload specification. We will demonstrate how spatial queries are expressed in spatially extended SQL queries, and submitted through a command line/web interface for execution. Parallel to our system demonstration, we explain the system architecture and details on how queries are translated to MapReduce operators, optimized, and executed on Hadoop. In addition, we will showcase how the system can be used to support two representative real world use cases: large scale pathology analytical imaging, and geo-spatial data warehousing.

Characterizing the Diurnal Cycle of Land Surface Temperature and Evapotranspiration at High Spatial Resolution Using Thermal Observations from sUAS.

NASA Astrophysics Data System (ADS)

Dutta, D.; Drewry, D.; Johnson, W. R.

2017-12-01

The surface temperature of plant canopies is an important indicator of the stomatal regulation of plant water use and the associated water flux from plants to atmosphere (evapotranspiration (ET)). Remotely sensed thermal observations using compact, low-cost, lightweight sensors from small unmanned aerial systems (sUAS) have the potential to provide surface temperature (ST) and ET estimates at unprecedented spatial and temporal resolutions, allowing us to characterize the intra-field diurnal variations in canopy ST and ET for a variety of vegetation systems. However, major challenges exist for obtaining accurate surface temperature estimates from low-cost uncooled microbolometer-type sensors. Here we describe the development of calibration methods using thermal chamber experiments, taking into account the ambient optics and sensor temperatures, and applying simple models of spatial non-uniformity correction to the sensor focal-plane-array. We present a framework that can be used to derive accurate surface temperatures using radiometric observations from low-cost sensors, and demonstrate this framework using a sUAS-mounted sensor across a diverse set of calibration and vegetation targets. Further, we demonstrate the use of the Surface Temperature Initiated Closure (STIC) model for computing spatially explicit, high spatial resolution ET estimates across several well-monitored agricultural systems, as driven by sUAS acquired surface temperatures. STIC provides a physically-based surface energy balance framework for the simultaneous retrieval of the surface and atmospheric vapor conductances and surface energy fluxes, by physically integrating radiometric surface temperature information into the Penman-Monteith equation. Results of our analysis over agricultural systems in Ames, IA and Davis, CA demonstrate the power of this approach for quantifying the intra-field spatial variability in the diurnal cycle of plant water use at sub-meter resolutions.

High resolution propagation-based imaging system for in vivo dynamic computed tomography of lungs in small animals

NASA Astrophysics Data System (ADS)

Preissner, M.; Murrie, R. P.; Pinar, I.; Werdiger, F.; Carnibella, R. P.; Zosky, G. R.; Fouras, A.; Dubsky, S.

2018-04-01

We have developed an x-ray imaging system for in vivo four-dimensional computed tomography (4DCT) of small animals for pre-clinical lung investigations. Our customized laboratory facility is capable of high resolution in vivo imaging at high frame rates. Characterization using phantoms demonstrate a spatial resolution of slightly below 50 μm at imaging rates of 30 Hz, and the ability to quantify material density differences of at least 3%. We benchmark our system against existing small animal pre-clinical CT scanners using a quality factor that combines spatial resolution, image noise, dose and scan time. In vivo 4DCT images obtained on our system demonstrate resolution of important features such as blood vessels and small airways, of which the smallest discernible were measured as 55–60 μm in cross section. Quantitative analysis of the images demonstrate regional differences in ventilation between injured and healthy lungs.

Bayesian spatial transformation models with applications in neuroimaging data

PubMed Central

Miranda, Michelle F.; Zhu, Hongtu; Ibrahim, Joseph G.

2013-01-01

Summary The aim of this paper is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. Our STMs include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov Random Field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. PMID:24128143

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors.

PubMed

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H; Gambhir, Manoj; Fu, Joshua S; Liu, Yang; Remais, Justin V

2013-09-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis , the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001-2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057-2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses-including altered phenology-of disease vectors to altered climate.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors

PubMed Central

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H.; Gambhir, Manoj; Fu, Joshua S.; Liu, Yang; Remais, Justin V.

2014-01-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis, the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001–2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057–2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses—including altered phenology—of disease vectors to altered climate. PMID:24772388

“Spatial Energetics”: Integrating Data From GPS, Accelerometry, and GIS to Address Obesity and Inactivity

PubMed Central

James, Peter; Jankowska, Marta; Marx, Christine; Hart, Jaime E.; Berrigan, David; Kerr, Jacqueline; Hurvitz, Philip M.; Hipp, J. Aaron; Laden, Francine

2016-01-01

To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high–spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding, and suggests solutions to move this promising area of research forward. PMID:27528538

Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects.

PubMed

Dong, Ni; Huang, Helai; Zheng, Liang

2015-09-01

In zone-level crash prediction, accounting for spatial dependence has become an extensively studied topic. This study proposes Support Vector Machine (SVM) model to address complex, large and multi-dimensional spatial data in crash prediction. Correlation-based Feature Selector (CFS) was applied to evaluate candidate factors possibly related to zonal crash frequency in handling high-dimension spatial data. To demonstrate the proposed approaches and to compare them with the Bayesian spatial model with conditional autoregressive prior (i.e., CAR), a dataset in Hillsborough county of Florida was employed. The results showed that SVM models accounting for spatial proximity outperform the non-spatial model in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-zonal spatial correlations. The best model predictive capability, relatively, is associated with the model considering proximity of the centroid distance by choosing the RBF kernel and setting the 10% of the whole dataset as the testing data, which further exhibits SVM models' capacity for addressing comparatively complex spatial data in regional crash prediction modeling. Moreover, SVM models exhibit the better goodness-of-fit compared with CAR models when utilizing the whole dataset as the samples. A sensitivity analysis of the centroid-distance-based spatial SVM models was conducted to capture the impacts of explanatory variables on the mean predicted probabilities for crash occurrence. While the results conform to the coefficient estimation in the CAR models, which supports the employment of the SVM model as an alternative in regional safety modeling. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using spatial analysis to demonstrate the heterogeneity of the cardiovascular drug-prescribing pattern in Taiwan

PubMed Central

2011-01-01

Background Geographic Information Systems (GIS) combined with spatial analytical methods could be helpful in examining patterns of drug use. Little attention has been paid to geographic variation of cardiovascular prescription use in Taiwan. The main objective was to use local spatial association statistics to test whether or not the cardiovascular medication-prescribing pattern is homogenous across 352 townships in Taiwan. Methods The statistical methods used were the global measures of Moran's I and Local Indicators of Spatial Association (LISA). While Moran's I provides information on the overall spatial distribution of the data, LISA provides information on types of spatial association at the local level. LISA statistics can also be used to identify influential locations in spatial association analysis. The major classes of prescription cardiovascular drugs were taken from Taiwan's National Health Insurance Research Database (NHIRD), which has a coverage rate of over 97%. The dosage of each prescription was converted into defined daily doses to measure the consumption of each class of drugs. Data were analyzed with ArcGIS and GeoDa at the township level. Results The LISA statistics showed an unusual use of cardiovascular medications in the southern townships with high local variation. Patterns of drug use also showed more low-low spatial clusters (cold spots) than high-high spatial clusters (hot spots), and those low-low associations were clustered in the rural areas. Conclusions The cardiovascular drug prescribing patterns were heterogeneous across Taiwan. In particular, a clear pattern of north-south disparity exists. Such spatial clustering helps prioritize the target areas that require better education concerning drug use. PMID:21609462

Simultaneous detection of landmarks and key-frame in cardiac perfusion MRI using a joint spatial-temporal context model

NASA Astrophysics Data System (ADS)

Lu, Xiaoguang; Xue, Hui; Jolly, Marie-Pierre; Guetter, Christoph; Kellman, Peter; Hsu, Li-Yueh; Arai, Andrew; Zuehlsdorff, Sven; Littmann, Arne; Georgescu, Bogdan; Guehring, Jens

2011-03-01

Cardiac perfusion magnetic resonance imaging (MRI) has proven clinical significance in diagnosis of heart diseases. However, analysis of perfusion data is time-consuming, where automatic detection of anatomic landmarks and key-frames from perfusion MR sequences is helpful for anchoring structures and functional analysis of the heart, leading toward fully automated perfusion analysis. Learning-based object detection methods have demonstrated their capabilities to handle large variations of the object by exploring a local region, i.e., context. Conventional 2D approaches take into account spatial context only. Temporal signals in perfusion data present a strong cue for anchoring. We propose a joint context model to encode both spatial and temporal evidence. In addition, our spatial context is constructed not only based on the landmark of interest, but also the landmarks that are correlated in the neighboring anatomies. A discriminative model is learned through a probabilistic boosting tree. A marginal space learning strategy is applied to efficiently learn and search in a high dimensional parameter space. A fully automatic system is developed to simultaneously detect anatomic landmarks and key frames in both RV and LV from perfusion sequences. The proposed approach was evaluated on a database of 373 cardiac perfusion MRI sequences from 77 patients. Experimental results of a 4-fold cross validation show superior landmark detection accuracies of the proposed joint spatial-temporal approach to the 2D approach that is based on spatial context only. The key-frame identification results are promising.

Assessing the spatial distribution of Tuta absoluta (Lepidoptera: Gelechiidae) eggs in open-field tomato cultivation through geostatistical analysis.

PubMed

Martins, Júlio C; Picanço, Marcelo C; Silva, Ricardo S; Gonring, Alfredo Hr; Galdino, Tarcísio Vs; Guedes, Raul Nc

2018-01-01

The spatial distribution of insects is due to the interaction between individuals and the environment. Knowledge about the within-field pattern of spatial distribution of a pest is critical to planning control tactics, developing efficient sampling plans, and predicting pest damage. The leaf miner Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is the main pest of tomato crops in several regions of the world. Despite the importance of this pest, the pattern of spatial distribution of T. absoluta on open-field tomato cultivation remains unknown. Therefore, this study aimed to characterize the spatial distribution of T. absoluta in 22 commercial open-field tomato cultivations with plants at the three phenological development stages by using geostatistical analysis. Geostatistical analysis revealed that there was strong evidence for spatially dependent (aggregated) T. absoluta eggs in 19 of the 22 sample tomato cultivations. The maps that were obtained demonstrated the aggregated structure of egg densities at the edges of the crops. Further, T. absoluta was found to accomplish egg dispersal along the rows more frequently than it does between rows. Our results indicate that the greatest egg densities of T. absoluta occur at the edges of tomato crops. These results are discussed in relation to the behavior of T. absoluta distribution within fields and in terms of their implications for improved sampling guidelines and precision targeting control methods that are essential for effective pest monitoring and management. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

The spatial pattern of suicide in the US in relation to deprivation, fragmentation and rurality.

PubMed

Congdon, Peter

2011-01-01

Analysis of geographical patterns of suicide and psychiatric morbidity has demonstrated the impact of latent ecological variables (such as deprivation, rurality). Such latent variables may be derived by conventional multivariate techniques from sets of observed indices (for example, by principal components), by composite variable methods or by methods which explicitly consider the spatial framework of areas and, in particular, the spatial clustering of latent risks and outcomes. This article considers a latent random variable approach to explaining geographical contrasts in suicide in the US; and it develops a spatial structural equation model incorporating deprivation, social fragmentation and rurality. The approach allows for such latent spatial constructs to be correlated both within and between areas. Potential effects of area ethnic mix are also included. The model is applied to male and female suicide deaths over 2002–06 in 3142 US counties.

Edge-Preserving Image Smoothing Constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) of Hyperspectral Data.

PubMed

Hugelier, Siewert; Vitale, Raffaele; Ruckebusch, Cyril

2018-03-01

This article explores smoothing with edge-preserving properties as a spatial constraint for the resolution of hyperspectral images with multivariate curve resolution-alternating least squares (MCR-ALS). For each constrained component image (distribution map), irrelevant spatial details and noise are smoothed applying an L 1 - or L 0 -norm penalized least squares regression, highlighting in this way big changes in intensity of adjacent pixels. The feasibility of the constraint is demonstrated on three different case studies, in which the objects under investigation are spatially clearly defined, but have significant spectral overlap. This spectral overlap is detrimental for obtaining a good resolution and additional spatial information should be provided. The final results show that the spatial constraint enables better image (map) abstraction, artifact removal, and better interpretation of the results obtained, compared to a classical MCR-ALS analysis of hyperspectral images.

a Multidisciplinary Analytical Framework for Studying Active Mobility Patterns

NASA Astrophysics Data System (ADS)

Orellana, D.; Hermida, C.; Osorio, P.

2016-06-01

Intermediate cities are urged to change and adapt their mobility systems from a high energy-demanding motorized model to a sustainable low-motorized model. In order to accomplish such a model, city administrations need to better understand active mobility patterns and their links to socio-demographic and cultural aspects of the population. During the last decade, researchers have demonstrated the potential of geo-location technologies and mobile devices to gather massive amounts of data for mobility studies. However, the analysis and interpretation of this data has been carried out by specialized research groups with relatively narrow approaches from different disciplines. Consequently, broader questions remain less explored, mainly those relating to spatial behaviour of individuals and populations with their geographic environment and the motivations and perceptions shaping such behaviour. Understanding sustainable mobility and exploring new research paths require an interdisciplinary approach given the complex nature of mobility systems and their social, economic and environmental impacts. Here, we introduce the elements for a multidisciplinary analytical framework for studying active mobility patterns comprised of three components: a) Methodological, b) Behavioural, and c) Perceptual. We demonstrate the applicability of the framework by analysing mobility patterns of cyclists and pedestrians in an intermediate city integrating a range of techniques, including: GPS tracking, spatial analysis, auto-ethnography, and perceptual mapping. The results demonstrated the existence of non-evident spatial behaviours and how perceptual features affect mobility. This knowledge is useful for developing policies and practices for sustainable mobility planning.

The geography of solar energy in the United States: Market definition, industry structure, and choice in solar PV adoption

DOE PAGES

O’Shaughnessy, Eric; Nemet, Gregory F.; Darghouth, Naïm

2018-01-30

The solar photovoltaic (PV) installation industry comprises thousands of firms around the world who collectively installed nearly 200 million panels in 2015. Spatial analysis of the emerging industry has received considerable attention from the literature, especially on the demand side concerning peer effects and adopter clustering. However this research area does not include similarly sophisticated spatial analysis on the supply side of the installation industry. The lack of understanding of the spatial structure of the PV installation industry leaves PV market research to rely on jurisdictional lines, such as counties, to define geographic PV markets. We develop an approach thatmore » uses the spatial distribution of installers' activity to define geographic boundaries for PV markets. Our method is useful for PV market research and applicable in the contexts of other industries. We use our approach to demonstrate that the PV industry in the United States is spatially heterogeneous. Despite the emergence of some national-scale PV installers, installers are largely local and installer communities are unique from one region to the next. The social implications of the spatial heterogeneity of the emerging PV industry involve improving understanding of issues such as market power, industry consolidation, and how much choice potential adopters have.« less

The geography of solar energy in the United States: Market definition, industry structure, and choice in solar PV adoption

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

O’Shaughnessy, Eric; Nemet, Gregory F.; Darghouth, Naïm

The solar photovoltaic (PV) installation industry comprises thousands of firms around the world who collectively installed nearly 200 million panels in 2015. Spatial analysis of the emerging industry has received considerable attention from the literature, especially on the demand side concerning peer effects and adopter clustering. However this research area does not include similarly sophisticated spatial analysis on the supply side of the installation industry. The lack of understanding of the spatial structure of the PV installation industry leaves PV market research to rely on jurisdictional lines, such as counties, to define geographic PV markets. We develop an approach thatmore » uses the spatial distribution of installers' activity to define geographic boundaries for PV markets. Our method is useful for PV market research and applicable in the contexts of other industries. We use our approach to demonstrate that the PV industry in the United States is spatially heterogeneous. Despite the emergence of some national-scale PV installers, installers are largely local and installer communities are unique from one region to the next. The social implications of the spatial heterogeneity of the emerging PV industry involve improving understanding of issues such as market power, industry consolidation, and how much choice potential adopters have.« less

Evaluating shrub-associated spatial patterns of soil properties in a shrub-steppe ecosystem using multiple-variable geostatistics

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

Halvorson, J.J.; Smith, J.L.; Bolton, H. Jr.

1995-09-01

Geostatistics are often calculated for a single variable at a time, even though many natural phenomena are functions of several variables. The objective of this work was to demonstrate a nonparametric approach for assessing the spatial characteristics of multiple-variable phenomena. Specifically, we analyzed the spatial characteristics of resource islands in the soil under big sagebrush (Artemisia tridentala Nutt.), a dominant shrub in the intermountain western USA. For our example, we defined resource islands as a function of six soil variables representing concentrations of soil resources, populations of microorganisms, and soil microbial physiological variables. By collectively evaluating the indicator transformations ofmore » these individual variables, we created a new data set, termed a multiple-variable indicator transform or MVIT. Alternate MVITs were obtained by varying the selection criteria. Each MVIT was analyzed with variography to characterize spatial continuity, and with indicator kriging to predict the combined probability of their occurrence at unsampled locations in the landscape. Simple graphical analysis and variography demonstrated spatial dependence for all individual soil variables. Maps derived from ordinary kriging of MVITs suggested that the combined probabilities for encountering zones of above-median resources were greatest near big sagebrush. 51 refs., 5 figs., 1 tab.« less

The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis.

PubMed

Siemann, Julia; Herrmann, Manfred; Galashan, Daniela

2018-01-25

The present study examined whether feature-based cueing affects early or late stages of flanker conflict processing using EEG and fMRI. Feature cues either directed participants' attention to the upcoming colour of the target or were neutral. Validity-specific modulations during interference processing were investigated using the N200 event-related potential (ERP) component and BOLD signal differences. Additionally, both data sets were integrated using an fMRI-constrained source analysis. Finally, the results were compared with a previous study in which spatial instead of feature-based cueing was applied to an otherwise identical flanker task. Feature-based and spatial attention recruited a common fronto-parietal network during conflict processing. Irrespective of attention type (feature-based; spatial), this network responded to focussed attention (valid cueing) as well as context updating (invalid cueing), hinting at domain-general mechanisms. However, spatially and non-spatially directed attention also demonstrated domain-specific activation patterns for conflict processing that were observable in distinct EEG and fMRI data patterns as well as in the respective source analyses. Conflict-specific activity in visual brain regions was comparable between both attention types. We assume that the distinction between spatially and non-spatially directed attention types primarily applies to temporal differences (domain-specific dynamics) between signals originating in the same brain regions (domain-general localization).

Portable measurement system for real-time acquisition and analysis of in-vivo spatially resolved reflectance in the subdiffusive regime

NASA Astrophysics Data System (ADS)

Naglič, Peter; Ivančič, Matic; Pernuš, Franjo; Likar, Boštjan; Bürmen, Miran

2018-02-01

A measurement system was developed to acquire and analyze subdiffusive spatially resolved reflectance using an optical fiber probe with short source-detector separations. Since subdiffusive reflectance significantly depends on the scattering phase function, the analysis of the acquired reflectance is based on a novel inverse Monte Carlo model that allows estimation of phase function related parameters in addition to the absorption and reduced scattering coefficients. In conjunction with our measurement system, the model allowed real-time estimation of optical properties, which we demonstrate for a case of dynamically induced changes in human skin by applying pressure with an optical fiber probe.

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Nanoscale spatial analysis of clay minerals containing cesium by synchrotron radiation photoemission electron microscopy

NASA Astrophysics Data System (ADS)

Yoshigoe, Akitaka; Shiwaku, Hideaki; Kobayashi, Toru; Shimoyama, Iwao; Matsumura, Daiju; Tsuji, Takuya; Nishihata, Yasuo; Kogure, Toshihiro; Ohkochi, Takuo; Yasui, Akira; Yaita, Tsuyoshi

2018-01-01

A synchrotron radiation photoemission electron microscope (SR-PEEM) was applied to demonstrate the pinpoint analysis of micrometer-sized weathered biotite clay particles with artificially adsorbed cesium (Cs) atoms. Despite the insulating properties of the clay, we observed the spatial distributions of constituent elements (Si, Al, Cs, Mg, and Fe) without charging issues and clarified reciprocal site-correlations among these elements with nanometer resolution. We found that Cs atoms were likely to be adsorbed evenly over the entire particle; however, we identified an occupational conflict between Cs and Mg atoms, implying that Cs sorption involves ion exchange processes. Spatially resolved X-ray absorption spectra (XAS) of the Cs4,5 M-edge region showed Cs to be present in a monocation state (Cs+) as typically observed for Cs compounds. Further pinpoint XAS measurements were also performed at the Fe L2,3-edge to determine the chemical valence of the Fe atoms. The shapes of the spectra were similar to those for Fe2O3, indicating that Fe in the clay was in a 3+ oxidation state. From these observations, we infer that charge compensation facilitates Cs adsorption in the vicinity of a substitution site where Si4+ ions are replaced by Fe3+ ions in SiO4 tetrahedral sheets. Our results demonstrate the utility of SR-PEEM as a tool for spatially resolved chemical analyses of various environmental substances, which is not limited by the poor conductivity of samples.

An Innovative Metric to Evaluate Satellite Precipitation's Spatial Distribution

NASA Astrophysics Data System (ADS)

Liu, H.; Chu, W.; Gao, X.; Sorooshian, S.

2011-12-01

Thanks to its capability to cover the mountains, where ground measurement instruments cannot reach, satellites provide a good means of estimating precipitation over mountainous regions. In regions with complex terrains, accurate information on high-resolution spatial distribution of precipitation is critical for many important issues, such as flood/landslide warning, reservoir operation, water system planning, etc. Therefore, in order to be useful in many practical applications, satellite precipitation products should possess high quality in characterizing spatial distribution. However, most existing validation metrics, which are based on point/grid comparison using simple statistics, cannot effectively measure satellite's skill of capturing the spatial patterns of precipitation fields. This deficiency results from the fact that point/grid-wised comparison does not take into account of the spatial coherence of precipitation fields. Furth more, another weakness of many metrics is that they can barely provide information on why satellite products perform well or poor. Motivated by our recent findings of the consistent spatial patterns of the precipitation field over the western U.S., we developed a new metric utilizing EOF analysis and Shannon entropy. The metric can be derived through two steps: 1) capture the dominant spatial patterns of precipitation fields from both satellite products and reference data through EOF analysis, and 2) compute the similarities between the corresponding dominant patterns using mutual information measurement defined with Shannon entropy. Instead of individual point/grid, the new metric treat the entire precipitation field simultaneously, naturally taking advantage of spatial dependence. Since the dominant spatial patterns are shaped by physical processes, the new metric can shed light on why satellite product can or cannot capture the spatial patterns. For demonstration, a experiment was carried out to evaluate a satellite precipitation product, CMORPH, against the U.S. daily precipitation analysis of Climate Prediction Center (CPC) at a daily and .25o scale over the Western U.S.

Signal enhancement and Patterson-search phasing for high-spatial-resolution coherent X-ray diffraction imaging of biological objects.

PubMed

Takayama, Yuki; Maki-Yonekura, Saori; Oroguchi, Tomotaka; Nakasako, Masayoshi; Yonekura, Koji

2015-01-28

In this decade coherent X-ray diffraction imaging has been demonstrated to reveal internal structures of whole biological cells and organelles. However, the spatial resolution is limited to several tens of nanometers due to the poor scattering power of biological samples. The challenge is to recover correct phase information from experimental diffraction patterns that have a low signal-to-noise ratio and unmeasurable lowest-resolution data. Here, we propose a method to extend spatial resolution by enhancing diffraction signals and by robust phasing. The weak diffraction signals from biological objects are enhanced by interference with strong waves from dispersed colloidal gold particles. The positions of the gold particles determined by Patterson analysis serve as the initial phase, and this dramatically improves reliability and convergence of image reconstruction by iterative phase retrieval. A set of calculations based on current experiments demonstrates that resolution is improved by a factor of two or more.

Signal enhancement and Patterson-search phasing for high-spatial-resolution coherent X-ray diffraction imaging of biological objects

PubMed Central

Takayama, Yuki; Maki-Yonekura, Saori; Oroguchi, Tomotaka; Nakasako, Masayoshi; Yonekura, Koji

2015-01-01

In this decade coherent X-ray diffraction imaging has been demonstrated to reveal internal structures of whole biological cells and organelles. However, the spatial resolution is limited to several tens of nanometers due to the poor scattering power of biological samples. The challenge is to recover correct phase information from experimental diffraction patterns that have a low signal-to-noise ratio and unmeasurable lowest-resolution data. Here, we propose a method to extend spatial resolution by enhancing diffraction signals and by robust phasing. The weak diffraction signals from biological objects are enhanced by interference with strong waves from dispersed colloidal gold particles. The positions of the gold particles determined by Patterson analysis serve as the initial phase, and this dramatically improves reliability and convergence of image reconstruction by iterative phase retrieval. A set of calculations based on current experiments demonstrates that resolution is improved by a factor of two or more. PMID:25627480

Demonstration of polarization-insensitive spatial light modulation using a single polarization-sensitive spatial light modulator.

PubMed

Liu, Jun; Wang, Jian

2015-07-06

We present a simple configuration incorporating a single polarization-sensitive phase-only liquid crystal spatial light modulator (LC-SLM) to facilitate polarization-insensitive spatial light modulation. The polarization-insensitive configuration is formed by a polarization beam splitter (PBS), a polarization-sensitive phase-only LC-SLM, a half-wave plate (HWP), and a mirror in a loop structure. We experimentally demonstrate polarization-insensitive spatial light modulations for incident linearly polarized beams with different polarization states and polarization-multiplexed beams. Polarization-insensitive spatial light modulations generating orbital angular momentum (OAM) beams are demonstrated in the experiment. The designed polarization-insensitive configuration may find promising applications in spatial light modulations accommodating diverse incident polarizations.

Macroecological factors shape local-scale spatial patterns in agriculturalist settlements.

PubMed

Tao, Tingting; Abades, Sebastián; Teng, Shuqing; Huang, Zheng Y X; Reino, Luís; Chen, Bin J W; Zhang, Yong; Xu, Chi; Svenning, Jens-Christian

2017-11-15

Macro-scale patterns of human systems ranging from population distribution to linguistic diversity have attracted recent attention, giving rise to the suggestion that macroecological rules shape the assembly of human societies. However, in which aspects the geography of our own species is shaped by macroecological factors remains poorly understood. Here, we provide a first demonstration that macroecological factors shape strong local-scale spatial patterns in human settlement systems, through an analysis of spatial patterns in agriculturalist settlements in eastern mainland China based on high-resolution Google Earth images. We used spatial point pattern analysis to show that settlement spatial patterns are characterized by over-dispersion at fine spatial scales (0.05-1.4 km), consistent with territory segregation, and clumping at coarser spatial scales beyond the over-dispersion signals, indicating territorial clustering. Statistical modelling shows that, at macroscales, potential evapotranspiration and topographic heterogeneity have negative effects on territory size, but positive effects on territorial clustering. These relationships are in line with predictions from territory theory for hunter-gatherers as well as for many animal species. Our results help to disentangle the complex interactions between intrinsic spatial processes in agriculturalist societies and external forcing by macroecological factors. While one may speculate that humans can escape ecological constraints because of unique abilities for environmental modification and globalized resource transportation, our work highlights that universal macroecological principles still shape the geography of current human agricultural societies. © 2017 The Author(s).

A novel principal component analysis for spatially misaligned multivariate air pollution data.

PubMed

Jandarov, Roman A; Sheppard, Lianne A; Sampson, Paul D; Szpiro, Adam A

2017-01-01

We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

An implicit higher-order spatially accurate scheme for solving time dependent flows on unstructured meshes

NASA Astrophysics Data System (ADS)

Tomaro, Robert F.

1998-07-01

The present research is aimed at developing a higher-order, spatially accurate scheme for both steady and unsteady flow simulations using unstructured meshes. The resulting scheme must work on a variety of general problems to ensure the creation of a flexible, reliable and accurate aerodynamic analysis tool. To calculate the flow around complex configurations, unstructured grids and the associated flow solvers have been developed. Efficient simulations require the minimum use of computer memory and computational times. Unstructured flow solvers typically require more computer memory than a structured flow solver due to the indirect addressing of the cells. The approach taken in the present research was to modify an existing three-dimensional unstructured flow solver to first decrease the computational time required for a solution and then to increase the spatial accuracy. The terms required to simulate flow involving non-stationary grids were also implemented. First, an implicit solution algorithm was implemented to replace the existing explicit procedure. Several test cases, including internal and external, inviscid and viscous, two-dimensional, three-dimensional and axi-symmetric problems, were simulated for comparison between the explicit and implicit solution procedures. The increased efficiency and robustness of modified code due to the implicit algorithm was demonstrated. Two unsteady test cases, a plunging airfoil and a wing undergoing bending and torsion, were simulated using the implicit algorithm modified to include the terms required for a moving and/or deforming grid. Secondly, a higher than second-order spatially accurate scheme was developed and implemented into the baseline code. Third- and fourth-order spatially accurate schemes were implemented and tested. The original dissipation was modified to include higher-order terms and modified near shock waves to limit pre- and post-shock oscillations. The unsteady cases were repeated using the higher-order spatially accurate code. The new solutions were compared with those obtained using the second-order spatially accurate scheme. Finally, the increased efficiency of using an implicit solution algorithm in a production Computational Fluid Dynamics flow solver was demonstrated for steady and unsteady flows. A third- and fourth-order spatially accurate scheme has been implemented creating a basis for a state-of-the-art aerodynamic analysis tool.

Localized Hotspots Drive Continental Geography of Abnormal Amphibians on U.S. Wildlife Refuges

PubMed Central

Reeves, Mari K.; Medley, Kimberly A.; Pinkney, Alfred E.; Holyoak, Marcel; Johnson, Pieter T. J.; Lannoo, Michael J.

2013-01-01

Amphibians with missing, misshapen, and extra limbs have garnered public and scientific attention for two decades, yet the extent of the phenomenon remains poorly understood. Despite progress in identifying the causes of abnormalities in some regions, a lack of knowledge about their broader spatial distribution and temporal dynamics has hindered efforts to understand their implications for amphibian population declines and environmental quality. To address this data gap, we conducted a nationwide, 10-year assessment of 62,947 amphibians on U.S. National Wildlife Refuges. Analysis of a core dataset of 48,081 individuals revealed that consistent with expected background frequencies, an average of 2% were abnormal, but abnormalities exhibited marked spatial variation with a maximum prevalence of 40%. Variance partitioning analysis demonstrated that factors associated with space (rather than species or year sampled) captured 97% of the variation in abnormalities, and the amount of partitioned variance decreased with increasing spatial scale (from site to refuge to region). Consistent with this, abnormalities occurred in local to regional hotspots, clustering at scales of tens to hundreds of kilometers. We detected such hotspot clusters of high-abnormality sites in the Mississippi River Valley, California, and Alaska. Abnormality frequency was more variable within than outside of hotspot clusters. This is consistent with dynamic phenomena such as disturbance or natural enemies (pathogens or predators), whereas similarity of abnormality frequencies at scales of tens to hundreds of kilometers suggests involvement of factors that are spatially consistent at a regional scale. Our characterization of the spatial and temporal variation inherent in continent-wide amphibian abnormalities demonstrates the disproportionate contribution of local factors in predicting hotspots, and the episodic nature of their occurrence. PMID:24260103

Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce

PubMed Central

Aji, Ablimit; Sun, Xiling; Vo, Hoang; Liu, Qioaling; Lee, Rubao; Zhang, Xiaodong; Saltz, Joel; Wang, Fusheng

2016-01-01

The proliferation of GPS-enabled devices, and the rapid improvement of scientific instruments have resulted in massive amounts of spatial data in the last decade. Support of high performance spatial queries on large volumes data has become increasingly important in numerous fields, which requires a scalable and efficient spatial data warehousing solution as existing approaches exhibit scalability limitations and efficiency bottlenecks for large scale spatial applications. In this demonstration, we present Hadoop-GIS – a scalable and high performance spatial query system over MapReduce. Hadoop-GIS provides an efficient spatial query engine to process spatial queries, data and space based partitioning, and query pipelines that parallelize queries implicitly on MapReduce. Hadoop-GIS also provides an expressive, SQL-like spatial query language for workload specification. We will demonstrate how spatial queries are expressed in spatially extended SQL queries, and submitted through a command line/web interface for execution. Parallel to our system demonstration, we explain the system architecture and details on how queries are translated to MapReduce operators, optimized, and executed on Hadoop. In addition, we will showcase how the system can be used to support two representative real world use cases: large scale pathology analytical imaging, and geo-spatial data warehousing. PMID:27617325

Collaborative classification of hyperspectral and visible images with convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Mengmeng; Li, Wei; Du, Qian

2017-10-01

Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

Spatial variations in annual cycles of body-size spectra of planktonic ciliates and their environmental drivers in marine ecosystems.

PubMed

Xu, Henglong; Jiang, Yong; Xu, Guangjian

2016-11-15

Body-size spectra has proved to be a useful taxon-free resolution to summarize a community structure for bioassessment. The spatial variations in annual cycles of body-size spectra of planktonic ciliates and their environmental drivers were studied based on an annual dataset. Samples were biweekly collected at five stations in a bay of the Yellow Sea, northern China during a 1-year cycle. Based on a multivariate approach, the second-stage analysis, it was shown that the annual cycles of the body-size spectra were significantly different among five sampling stations. Correlation analysis demonstrated that the spatial variations in the body-size spectra were significantly related to changes of environmental conditions, especially dissolved nitrogen, alone or in combination with salinity and dissolve oxygen. Based on results, it is suggested that the nutrients may be the environmental drivers to shape the spatial variations in annual cycles of planktonic ciliates in terms of body-size spectra in marine ecosystems. Copyright © 2016 Elsevier Ltd. All rights reserved.

A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA

NASA Astrophysics Data System (ADS)

Huang, Jun; Ma, Yong; Mei, Xiaoguang; Fan, Fan

2016-11-01

The traditional noise reduction methods for 3-D infrared hyperspectral images typically operate independently in either the spatial or spectral domain, and such methods overlook the relationship between the two domains. To address this issue, we propose a hybrid spatial-spectral method in this paper to link both domains. First, principal component analysis and bivariate wavelet shrinkage are performed in the 2-D spatial domain. Second, 2-D principal component analysis transformation is conducted in the 1-D spectral domain to separate the basic components from detail ones. The energy distribution of noise is unaffected by orthogonal transformation; therefore, the signal-to-noise ratio of each component is used as a criterion to determine whether a component should be protected from over-denoising or denoised with certain 1-D denoising methods. This study implements the 1-D wavelet shrinking threshold method based on Stein's unbiased risk estimator, and the quantitative results on publicly available datasets demonstrate that our method can improve denoising performance more effectively than other state-of-the-art methods can.

Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2018-02-01

Conventional bias correction is usually applied on a grid-by-grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two-step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back-transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA-based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.

Handling Different Spatial Resolutions in Image Fusion by Multivariate Curve Resolution-Alternating Least Squares for Incomplete Image Multisets.

PubMed

Piqueras, Sara; Bedia, Carmen; Beleites, Claudia; Krafft, Christoph; Popp, Jürgen; Maeder, Marcel; Tauler, Romà; de Juan, Anna

2018-06-05

Data fusion of different imaging techniques allows a comprehensive description of chemical and biological systems. Yet, joining images acquired with different spectroscopic platforms is complex because of the different sample orientation and image spatial resolution. Whereas matching sample orientation is often solved by performing suitable affine transformations of rotation, translation, and scaling among images, the main difficulty in image fusion is preserving the spatial detail of the highest spatial resolution image during multitechnique image analysis. In this work, a special variant of the unmixing algorithm Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) for incomplete multisets is proposed to provide a solution for this kind of problem. This algorithm allows analyzing simultaneously images collected with different spectroscopic platforms without losing spatial resolution and ensuring spatial coherence among the images treated. The incomplete multiset structure concatenates images of the two platforms at the lowest spatial resolution with the image acquired with the highest spatial resolution. As a result, the constituents of the sample analyzed are defined by a single set of distribution maps, common to all platforms used and with the highest spatial resolution, and their related extended spectral signatures, covering the signals provided by each of the fused techniques. We demonstrate the potential of the new variant of MCR-ALS for multitechnique analysis on three case studies: (i) a model example of MIR and Raman images of pharmaceutical mixture, (ii) FT-IR and Raman images of palatine tonsil tissue, and (iii) mass spectrometry and Raman images of bean tissue.

Spatial controls of occurrence and spread of wildfires in the Missouri Ozark Highlands.

PubMed

Yang, Jian; He, Hong S; Shifley, Stephen R

2008-07-01

Understanding spatial controls on wildfires is important when designing adaptive fire management plans and optimizing fuel treatment locations on a forest landscape. Previous research about this topic focused primarily on spatial controls for fire origin locations alone. Fire spread and behavior were largely overlooked. This paper contrasts the relative importance of biotic, abiotic, and anthropogenic constraints on the spatial pattern of fire occurrence with that on burn probability (i.e., the probability that fire will spread to a particular location). Spatial point pattern analysis and landscape succession fire model (LANDIS) were used to create maps to show the contrast. We quantified spatial controls on both fire occurrence and fire spread in the Midwest Ozark Highlands region, USA. This area exhibits a typical anthropogenic surface fire regime. We found that (1) human accessibility and land ownership were primary limiting factors in shaping clustered fire origin locations; (2) vegetation and topography had a negligible influence on fire occurrence in this anthropogenic regime; (3) burn probability was higher in grassland and open woodland than in closed-canopy forest, even though fire occurrence density was less in these vegetation types; and (4) biotic and abiotic factors were secondary descriptive ingredients for determining the spatial patterns of burn probability. This study demonstrates how fire occurrence and spread interact with landscape patterns to affect the spatial distribution of wildfire risk. The application of spatial point pattern data analysis would also be valuable to researchers working on landscape forest fire models to integrate historical ignition location patterns in fire simulation.

Proto-Algic III: Pronouns.

ERIC Educational Resources Information Center

Proulx, Paul

An analysis of pronouns in Proto-Algic, the ancestor of Proto-Algonquian and other languages, revealed that the Proto-Algic demonstrative roots and locatives had three inflectional endings, referring to spatial or temporal distributions of entities, which evolve into the gender systems of Yurok and Algonquian. Proto-Algic had two discourse…

INTEGRATING LANDSCAPE AND HYDROLOGIC ANALYSIS FOR WATERSHED ASSESSMENT IN AN AMERICAN SEMI-ARID BIOREGION

EPA Science Inventory

The objective of this study is to demonstrate the application of operational hydrologic modeling and landscape assessment tools to investigate the temporal and spatial effects of varying levels of anthropogenic disturbance in a semi-arid catchment and examine the consequences of ...

Modeling and Statistical Analysis of the Spatio-Temporal Patterns of Seasonal Influenza in Israel

PubMed Central

Katriel, Guy; Yaari, Rami; Roll, Uri; Stone, Lewi

2012-01-01

Background Seasonal influenza outbreaks are a serious burden for public health worldwide and cause morbidity to millions of people each year. In the temperate zone influenza is predominantly seasonal, with epidemics occurring every winter, but the severity of the outbreaks vary substantially between years. In this study we used a highly detailed database, which gave us both temporal and spatial information of influenza dynamics in Israel in the years 1998–2009. We use a discrete-time stochastic epidemic SIR model to find estimates and credible confidence intervals of key epidemiological parameters. Findings Despite the biological complexity of the disease we found that a simple SIR-type model can be fitted successfully to the seasonal influenza data. This was true at both the national levels and at the scale of single cities.The effective reproductive number Re varies between the different years both nationally and among Israeli cities. However, we did not find differences in Re between different Israeli cities within a year. R e was positively correlated to the strength of the spatial synchronization in Israel. For those years in which the disease was more “infectious”, then outbreaks in different cities tended to occur with smaller time lags. Our spatial analysis demonstrates that both the timing and the strength of the outbreak within a year are highly synchronized between the Israeli cities. We extend the spatial analysis to demonstrate the existence of high synchrony between Israeli and French influenza outbreaks. Conclusions The data analysis combined with mathematical modeling provided a better understanding of the spatio-temporal and synchronization dynamics of influenza in Israel and between Israel and France. Altogether, we show that despite major differences in demography and weather conditions intra-annual influenza epidemics are tightly synchronized in both their timing and magnitude, while they may vary greatly between years. The predominance of a similar main strain of influenza, combined with population mixing serve to enhance local and global influenza synchronization within an influenza season. PMID:23056192

Demonstration of polarization-insensitive spatial light modulation using a single polarization-sensitive spatial light modulator

PubMed Central

Liu, Jun; Wang, Jian

2015-01-01

We present a simple configuration incorporating a single polarization-sensitive phase-only liquid crystal spatial light modulator (LC-SLM) to facilitate polarization-insensitive spatial light modulation. The polarization-insensitive configuration is formed by a polarization beam splitter (PBS), a polarization-sensitive phase-only LC-SLM, a half-wave plate (HWP), and a mirror in a loop structure. We experimentally demonstrate polarization-insensitive spatial light modulations for incident linearly polarized beams with different polarization states and polarization-multiplexed beams. Polarization-insensitive spatial light modulations generating orbital angular momentum (OAM) beams are demonstrated in the experiment. The designed polarization-insensitive configuration may find promising applications in spatial light modulations accommodating diverse incident polarizations. PMID:26146032

Bayesian learning for spatial filtering in an EEG-based brain-computer interface.

PubMed

Zhang, Haihong; Yang, Huijuan; Guan, Cuntai

2013-07-01

Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy principle, we introduce a gamma probability model for describing single-trial EEG power features. We then formulate and analyze the theoretical relationship between Bayes classification error and the so-called Rayleigh quotient, which is a function of spatial filters and basically measures the ratio in power features between two classes. This paper also reports our extensive study that examines the theory and its use in classification, using three publicly available EEG data sets and state-of-the-art spatial filtering techniques and various classifiers. Specifically, we validate the positive relationship between Bayes error and Rayleigh quotient in real EEG power features. Finally, we demonstrate that the Bayes error can be practically reduced by applying a new spatial filter with lower Rayleigh quotient.

Similar Processes but Different Environmental Filters for Soil Bacterial and Fungal Community Composition Turnover on a Broad Spatial Scale

PubMed Central

Chemidlin Prévost-Bouré, Nicolas; Dequiedt, Samuel; Thioulouse, Jean; Lelièvre, Mélanie; Saby, Nicolas P. A.; Jolivet, Claudy; Arrouays, Dominique; Plassart, Pierre; Lemanceau, Philippe; Ranjard, Lionel

2014-01-01

Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes

Similar processes but different environmental filters for soil bacterial and fungal community composition turnover on a broad spatial scale.

PubMed

Chemidlin Prévost-Bouré, Nicolas; Dequiedt, Samuel; Thioulouse, Jean; Lelièvre, Mélanie; Saby, Nicolas P A; Jolivet, Claudy; Arrouays, Dominique; Plassart, Pierre; Lemanceau, Philippe; Ranjard, Lionel

2014-01-01

Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes

Error analysis of filtering operations in pixel-duplicated images of diabetic retinopathy

NASA Astrophysics Data System (ADS)

Mehrubeoglu, Mehrube; McLauchlan, Lifford

2010-08-01

In this paper, diabetic retinopathy is chosen for a sample target image to demonstrate the effectiveness of image enlargement through pixel duplication in identifying regions of interest. Pixel duplication is presented as a simpler alternative to data interpolation techniques for detecting small structures in the images. A comparative analysis is performed on different image processing schemes applied to both original and pixel-duplicated images. Structures of interest are detected and and classification parameters optimized for minimum false positive detection in the original and enlarged retinal pictures. The error analysis demonstrates the advantages as well as shortcomings of pixel duplication in image enhancement when spatial averaging operations (smoothing filters) are also applied.

Bayesian spatial transformation models with applications in neuroimaging data.

PubMed

Miranda, Michelle F; Zhu, Hongtu; Ibrahim, Joseph G

2013-12-01

The aim of this article is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. The proposed STM include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov random field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. © 2013, The International Biometric Society.

Dual-domain point diffraction interferometer

DOEpatents

Naulleau, Patrick P.; Goldberg, Kenneth Alan

2000-01-01

A hybrid spatial/temporal-domain point diffraction interferometer (referred to as the dual-domain PS/PDI) that is capable of suppressing the scattered-reference-light noise that hinders the conventional PS/PDI is provided. The dual-domain PS/PDI combines the separate noise-suppression capabilities of the widely-used phase-shifting and Fourier-transform fringe pattern analysis methods. The dual-domain PS/PDI relies on both a more restrictive implementation of the image plane PS/PDI mask and a new analysis method to be applied to the interferograms generated and recorded by the modified PS/PDI. The more restrictive PS/PDI mask guarantees the elimination of spatial-frequency crosstalk between the signal and the scattered-light noise arising from scattered-reference-light interfering with the test beam. The new dual-domain analysis method is then used to eliminate scattered-light noise arising from both the scattered-reference-light interfering with the test beam and the scattered-reference-light interfering with the "true" pinhole-diffracted reference light. The dual-domain analysis method has also been demonstrated to provide performance enhancement when using the non-optimized standard PS/PDI design. The dual-domain PS/PDI is essentially a three-tiered filtering system composed of lowpass spatial-filtering the test-beam electric field using the more restrictive PS/PDI mask, bandpass spatial-filtering the individual interferogram irradiance frames making up the phase-shifting series, and bandpass temporal-filtering the phase-shifting series as a whole.

Evidence for fish dispersal from spatial analysis of stream network topology

USGS Publications Warehouse

Hitt, N.P.; Angermeier, P.L.

2008-01-01

Developing spatially explicit conservation strategies for stream fishes requires an understanding of the spatial structure of dispersal within stream networks. We explored spatial patterns of stream fish dispersal by evaluating how the size and proximity of connected streams (i.e., stream network topology) explained variation in fish assemblage structure and how this relationship varied with local stream size. We used data from the US Environmental Protection Agency's Environmental Monitoring and Assessment Program in wadeable streams of the Mid-Atlantic Highlands region (n = 308 sites). We quantified stream network topology with a continuous analysis based on the rate of downstream flow accumulation from sites and with a discrete analysis based on the presence of mainstem river confluences (i.e., basin area >250 km2) within 20 fluvial km (fkm) from sites. Continuous variation in stream network topology was related to local species richness within a distance of ???10 fkm, suggesting an influence of fish dispersal within this spatial grain. This effect was explained largely by catostomid species, cyprinid species, and riverine species, but was not explained by zoogeographic regions, ecoregions, sampling period, or spatial autocorrelation. Sites near mainstem river confluences supported greater species richness and abundance of catostomid, cyprinid, and ictalurid fishes than did sites >20 fkm from such confluences. Assemblages at sites on the smallest streams were not related to stream network topology, consistent with the hypothesis that local stream size regulates the influence of regional dispersal. These results demonstrate that the size and proximity of connected streams influence the spatial distribution of fish and suggest that these influences can be incorporated into the designs of stream bioassessments and reserves to enhance management efficacy. ?? 2008 by The North American Benthological Society.

The Emergence of Regional Immigrant Concentrations in USA and Australia: A Spatial Relatedness Approach

PubMed Central

Novotny, Josef; Hasman, Jiri

2015-01-01

This paper examines the patterns of the US and Australian immigration geography and the process of regional population diversification and the emergence of new immigrant concentrations at the regional level. It presents a new approach in the context of human migration studies, focusing on spatial relatedness between individual foreign-born groups as revealed from the analysis of their joint spatial concentrations. The approach employs a simple assumption that the more frequently the members of two population groups concentrate in the same locations the higher is the probability that these two groups can be related. Based on detailed data on the spatial distribution of foreign-born groups in US counties (2000–2010) and Australian postal areas (2006–2011) we firstly quantify the spatial relatedness between all pairs of foreign-born groups and model the aggregate patterns of US and Australian immigration systems conceptualized as the undirected networks of foreign-born groups linked by their spatial relatedness. Secondly, adopting a more dynamic perspective, we assume that immigrant groups with higher spatial relatedness to those groups already concentrated in a region are also more likely to settle in this region in future. As the ultimate goal of the paper, we examine the power of spatial relatedness measures in projecting the emergence of new immigrant concentrations in the US and Australian regions. The results corroborate that the spatial relatedness measures can serve as useful instruments in the analysis of the patterns of population structure and prediction of regional population change. More generally, this paper demonstrates that information contained in spatial patterns (relatedness in space) of population composition has yet to be fully utilized in population forecasting. PMID:25966371

Caloric vestibular stimulation and postural control in patients with spatial neglect following stroke.

PubMed

Sturt, Ruth; Punt, T David

2013-01-01

The impact of spatial neglect remains a substantial challenge to patients undergoing rehabilitation following stroke. Beyond the relatively well-described implications for visuospatial function, neglect is increasingly shown to have a negative impact on the wider aspects of sensori-motor performance with corresponding implications for activities including gait and balance. Caloric vestibular stimulation (CVS) administered to the contralesional ear has previously been shown to improve performance in patients with spatial neglect. Here, in Experiment One, we investigated the effect of CVS on clinical measures of spatial neglect and postural control in three groups of patients following stroke; left brain damaged patients (LBD, n = 6), right brain damaged patients without neglect (RBD-, n = 6), and right brain damaged patients with neglect (RBD+ , n = 6). While post-stimulation scores demonstrated an improvement for participants with spatial neglect, further analysis of postural scores indicated that improvement was selective for asymmetrical activities, with symmetrical activities remaining unchanged. We interpret these results with reference to the related problem of extinction which predicts that activities demanding synchronous bilateral activity (symmetrical activities) would cause greater difficulties for patients with neglect. In Experiment Two, we tested a further six RBD+ patients on the same measures following CVS to the ipsilesional (right) ear. There was no significant improvement in perceptual or postural scores. Our findings are supportive of previous studies that demonstrate improvement in perception and movement for patients with spatial neglect following contralesional CVS and suggest that these improvements may have clinical benefits.

An enhanced droplet-based liquid microjunction surface sampling system coupled with HPLC-ESI-MS/MS for spatially resolved analysis

DOE PAGES

Van Berkel, Gary J.; Weiskittel, Taylor M.; Kertesz, Vilmos

2014-11-07

Droplet-based liquid microjunction surface sampling coupled with high-performance liquid chromatography (HPLC)-electrospray ionization (ESI)-tandem mass spectrometry (MS/MS) for spatially resolved analysis provides the possibility of effective analysis of complex matrix samples and can provide a greater degree of chemical information from a single spot sample than is typically possible with a direct analysis of an extract. Described here is the setup and enhanced capabilities of a discrete droplet liquid microjunction surface sampling system employing a commercially available CTC PAL autosampler. The system enhancements include incorporation of a laser distance sensor enabling unattended analysis of samples and sample locations of dramatically disparatemore » height as well as reliably dispensing just 0.5 μL of extraction solvent to make the liquid junction to the surface, wherein the extraction spot size was confined to an area about 0.7 mm in diameter; software modifications improving the spatial resolution of sampling spot selection from 1.0 to 0.1 mm; use of an open bed tray system to accommodate samples as large as whole-body rat thin tissue sections; and custom sample/solvent holders that shorten sampling time to approximately 1 min per sample. Lastly, the merit of these new features was demonstrated by spatially resolved sampling, HPLC separation, and mass spectral detection of pharmaceuticals and metabolites from whole-body rat thin tissue sections and razor blade (“crude”) cut mouse tissue.« less

An enhanced droplet-based liquid microjunction surface sampling system coupled with HPLC-ESI-MS/MS for spatially resolved analysis

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

Van Berkel, Gary J.; Weiskittel, Taylor M.; Kertesz, Vilmos

Droplet-based liquid microjunction surface sampling coupled with high-performance liquid chromatography (HPLC)-electrospray ionization (ESI)-tandem mass spectrometry (MS/MS) for spatially resolved analysis provides the possibility of effective analysis of complex matrix samples and can provide a greater degree of chemical information from a single spot sample than is typically possible with a direct analysis of an extract. Described here is the setup and enhanced capabilities of a discrete droplet liquid microjunction surface sampling system employing a commercially available CTC PAL autosampler. The system enhancements include incorporation of a laser distance sensor enabling unattended analysis of samples and sample locations of dramatically disparatemore » height as well as reliably dispensing just 0.5 μL of extraction solvent to make the liquid junction to the surface, wherein the extraction spot size was confined to an area about 0.7 mm in diameter; software modifications improving the spatial resolution of sampling spot selection from 1.0 to 0.1 mm; use of an open bed tray system to accommodate samples as large as whole-body rat thin tissue sections; and custom sample/solvent holders that shorten sampling time to approximately 1 min per sample. Lastly, the merit of these new features was demonstrated by spatially resolved sampling, HPLC separation, and mass spectral detection of pharmaceuticals and metabolites from whole-body rat thin tissue sections and razor blade (“crude”) cut mouse tissue.« less

Probabilistic Common Spatial Patterns for Multichannel EEG Analysis

PubMed Central

Chen, Zhe; Gao, Xiaorong; Li, Yuanqing; Brown, Emery N.; Gao, Shangkai

2015-01-01

Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a probabilistic modeling setting. Specifically, probabilistic CSP (P-CSP) is proposed as a generic EEG spatio-temporal modeling framework that subsumes the CSP and regularized CSP algorithms. The proposed framework enables us to resolve the overfitting issue of CSP in a principled manner. We derive statistical inference algorithms that can alleviate the issue of local optima. In particular, an efficient algorithm based on eigendecomposition is developed for maximum a posteriori (MAP) estimation in the case of isotropic noise. For more general cases, a variational algorithm is developed for group-wise sparse Bayesian learning for the P-CSP model and for automatically determining the model size. The two proposed algorithms are validated on a simulated data set. Their practical efficacy is also demonstrated by successful applications to single-trial classifications of three motor imagery EEG data sets and by the spatio-temporal pattern analysis of one EEG data set recorded in a Stroop color naming task. PMID:26005228

Nonlinear Reduced-Order Analysis with Time-Varying Spatial Loading Distributions

NASA Technical Reports Server (NTRS)

Prezekop, Adam

2008-01-01

Oscillating shocks acting in combination with high-intensity acoustic loadings present a challenge to the design of resilient hypersonic flight vehicle structures. This paper addresses some features of this loading condition and certain aspects of a nonlinear reduced-order analysis with emphasis on system identification leading to formation of a robust modal basis. The nonlinear dynamic response of a composite structure subject to the simultaneous action of locally strong oscillating pressure gradients and high-intensity acoustic loadings is considered. The reduced-order analysis used in this work has been previously demonstrated to be both computationally efficient and accurate for time-invariant spatial loading distributions, provided that an appropriate modal basis is used. The challenge of the present study is to identify a suitable basis for loadings with time-varying spatial distributions. Using a proper orthogonal decomposition and modal expansion, it is shown that such a basis can be developed. The basis is made more robust by incrementally expanding it to account for changes in the location, frequency and span of the oscillating pressure gradient.

Photogrammetry and remote sensing for visualization of spatial data in a virtual reality environment

NASA Astrophysics Data System (ADS)

Bhagawati, Dwipen

2001-07-01

Researchers in many disciplines have started using the tool of Virtual Reality (VR) to gain new insights into problems in their respective disciplines. Recent advances in computer graphics, software and hardware technologies have created many opportunities for VR systems, advanced scientific and engineering applications being among them. In Geometronics, generally photogrammetry and remote sensing are used for management of spatial data inventory. VR technology can be suitably used for management of spatial data inventory. This research demonstrates usefulness of VR technology for inventory management by taking the roadside features as a case study. Management of roadside feature inventory involves positioning and visualization of the features. This research has developed a methodology to demonstrate how photogrammetric principles can be used to position the features using the video-logging images and GPS camera positioning and how image analysis can help produce appropriate texture for building the VR, which then can be visualized in a Cave Augmented Virtual Environment (CAVE). VR modeling was implemented in two stages to demonstrate the different approaches for modeling the VR scene. A simulated highway scene was implemented with the brute force approach, while modeling software was used to model the real world scene using feature positions produced in this research. The first approach demonstrates an implementation of the scene by writing C++ codes to include a multi-level wand menu for interaction with the scene that enables the user to interact with the scene. The interactions include editing the features inside the CAVE display, navigating inside the scene, and performing limited geographic analysis. The second approach demonstrates creation of a VR scene for a real roadway environment using feature positions determined in this research. The scene looks realistic with textures from the real site mapped on to the geometry of the scene. Remote sensing and digital image processing techniques were used for texturing the roadway features in this scene.

Estimation of spatial-temporal gait parameters using a low-cost ultrasonic motion analysis system.

PubMed

Qi, Yongbin; Soh, Cheong Boon; Gunawan, Erry; Low, Kay-Soon; Thomas, Rijil

2014-08-20

In this paper, a low-cost motion analysis system using a wireless ultrasonic sensor network is proposed and investigated. A methodology has been developed to extract spatial-temporal gait parameters including stride length, stride duration, stride velocity, stride cadence, and stride symmetry from 3D foot displacements estimated by the combination of spherical positioning technique and unscented Kalman filter. The performance of this system is validated against a camera-based system in the laboratory with 10 healthy volunteers. Numerical results show the feasibility of the proposed system with average error of 2.7% for all the estimated gait parameters. The influence of walking speed on the measurement accuracy of proposed system is also evaluated. Statistical analysis demonstrates its capability of being used as a gait assessment tool for some medical applications.

An Integrated Ecological Modeling System for Assessing Impacts of Multiple Stressors on Stream and Riverine Ecosystem Services Within River Basins

EPA Science Inventory

We demonstrate a novel, spatially explicit assessment of the current condition of aquatic ecosystem services, with limited sensitivity analysis for the atmospheric contaminant mercury. The Integrated Ecological Modeling System (IEMS) forecasts water quality and quantity, habitat ...

Bisphosphonates in the Prevention of Post-Traumatic Osteoarthritis

DTIC Science & Technology

David, et al . 2017 J Ortho Res. - Histological analysis of mouse joints following DMM demonstrated rapid (within 3-day of injury), and focal (spatially...Medial Meniscus in Mice (David, et al . 2018 In Preparation) - The present study has shown that repeated i. a. injection of ZA into the murine knee

Fuzzy logic knowledge bases in integrated landscape assessment: examples and possibilities.

Treesearch

Keith M. Reynolds

2001-01-01

The literature on ecosystem management has articulated the need for integration across disciplines and spatial scales, but convincing demonstrations of integrated analysis to support ecosystem management are lacking. This paper focuses on integrated ecological assessment because ecosystem management fundamentally is concerned with integrated management, which...

Segmentation-free image processing and analysis of precipitate shapes in 2D and 3D

NASA Astrophysics Data System (ADS)

Bales, Ben; Pollock, Tresa; Petzold, Linda

2017-06-01

Segmentation based image analysis techniques are routinely employed for quantitative analysis of complex microstructures containing two or more phases. The primary advantage of these approaches is that spatial information on the distribution of phases is retained, enabling subjective judgements of the quality of the segmentation and subsequent analysis process. The downside is that computing micrograph segmentations with data from morphologically complex microstructures gathered with error-prone detectors is challenging and, if no special care is taken, the artifacts of the segmentation will make any subsequent analysis and conclusions uncertain. In this paper we demonstrate, using a two phase nickel-base superalloy microstructure as a model system, a new methodology for analysis of precipitate shapes using a segmentation-free approach based on the histogram of oriented gradients feature descriptor, a classic tool in image analysis. The benefits of this methodology for analysis of microstructure in two and three-dimensions are demonstrated.

Spatial-Temporal Dynamics of Urban Fire Incidents: a Case Study of Nanjing, China

NASA Astrophysics Data System (ADS)

Yao, J.; Zhang, X.

2016-06-01

Fire and rescue service is one of the fundamental public services provided by government in order to protect people, properties and environment from fires and other disasters, and thus promote a safer living environment. Well understanding spatial-temporal dynamics of fire incidents can offer insights for potential determinants of various fire events and enable better fire risk estimation, assisting future allocation of prevention resources and strategic planning of mitigation programs. Using a 12-year (2002-2013) dataset containing the urban fire events in Nanjing, China, this research explores the spatial-temporal dynamics of urban fire incidents. A range of exploratory spatial data analysis (ESDA) approaches and tools, such as spatial kernel density and co-maps, are employed to examine the spatial, temporal and spatial-temporal variations of the fire events. Particular attention has been paid to two types of fire incidents: residential properties and local facilities, due to their relatively higher occurrence frequencies. The results demonstrated that the amount of urban fire has greatly increased in the last decade and spatial-temporal distribution of fire events vary among different incident types, which implies varying impact of potential influencing factors for further investigation.

Femtosecond laser ablation-based mass spectrometry. An ideal tool for stoichiometric analysis of thin films

DOE PAGES

LaHaye, Nicole L.; Kurian, Jose; Diwakar, Prasoon K.; ...

2015-08-19

An accurate and routinely available method for stoichiometric analysis of thin films is a desideratum of modern materials science where a material’s properties depend sensitively on elemental composition. We thoroughly investigated femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fs-LA-ICP-MS) as an analytical technique for determination of the stoichiometry of thin films down to the nanometer scale. The use of femtosecond laser ablation allows for precise removal of material with high spatial and depth resolution that can be coupled to an ICP-MS to obtain elemental and isotopic information. We used molecular beam epitaxy-grown thin films of LaPd (x)Sb 2 and T´-La 2CuOmore » 4 to demonstrate the capacity of fs-LA-ICP-MS for stoichiometric analysis and the spatial and depth resolution of the technique. Here we demonstrate that the stoichiometric information of thin films with a thickness of ~10 nm or lower can be determined. Furthermore, our results indicate that fs-LA-ICP-MS provides precise information on the thin film-substrate interface and is able to detect the interdiffusion of cations.« less

Analysis of solar receiver flux distributions for US/Russian solar dynamic system demonstration on the MIR Space Station

NASA Technical Reports Server (NTRS)

Kerslake, Thomas W.; Fincannon, James

1995-01-01

The United States and Russia have agreed to jointly develop a solar dynamic (SD) system for flight demonstration on the Russian MIR space station starting in late 1997. Two important components of this SD system are the solar concentrator and heat receiver provided by Russia and the U.S., respectively. This paper describes optical analysis of the concentrator and solar flux predictions on target receiver surfaces. The optical analysis is performed using the code CIRCE2. These analyses account for finite sun size with limb darkening, concentrator surface slope and position errors, concentrator petal thermal deformation, gaps between petals, and the shading effect of the receiver support struts. The receiver spatial flux distributions are then combined with concentrator shadowing predictions. Geometric shadowing patterns are traced from the concentrator to the target receiver surfaces. These patterns vary with time depending on the chosen MIR flight attitude and orbital mechanics of the MIR spacecraft. The resulting predictions provide spatial and temporal receiver flux distributions for any specified mission profile. The impact these flux distributions have on receiver design and control of the Brayton engine are discussed.

Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media

PubMed Central

Pu, Jiansu; Teng, Zhiyao; Gong, Rui; Wen, Changjiang; Xu, Yang

2016-01-01

Check-in records are usually available in social services, which offer us the opportunity to capture and analyze users’ spatial and temporal behaviors. Mining such behavior features is essential to social analysis and business intelligence. However, the complexity and incompleteness of check-in records bring challenges to achieve such a task. Different from the previous work on social behavior analysis, in this paper, we present a visual analytics system, Social Check-in Fingerprinting (Sci-Fin), to facilitate the analysis and visualization of social check-in data. We focus on three major components of user check-in data: location, activity, and profile. Visual fingerprints for location, activity, and profile are designed to intuitively represent the high-dimensional attributes. To visually mine and demonstrate the behavior features, we integrate WorldMapper and Voronoi Treemap into our glyph-like designs. Such visual fingerprint designs offer us the opportunity to summarize the interesting features and patterns from different check-in locations, activities and users (groups). We demonstrate the effectiveness and usability of our system by conducting extensive case studies on real check-in data collected from a popular microblogging service. Interesting findings are reported and discussed at last. PMID:27999398

Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media.

PubMed

Pu, Jiansu; Teng, Zhiyao; Gong, Rui; Wen, Changjiang; Xu, Yang

2016-12-20

Check-in records are usually available in social services, which offer us the opportunity to capture and analyze users' spatial and temporal behaviors. Mining such behavior features is essential to social analysis and business intelligence. However, the complexity and incompleteness of check-in records bring challenges to achieve such a task. Different from the previous work on social behavior analysis, in this paper, we present a visual analytics system, Social Check-in Fingerprinting (Sci-Fin), to facilitate the analysis and visualization of social check-in data. We focus on three major components of user check-in data: location, activity, and profile. Visual fingerprints for location, activity, and profile are designed to intuitively represent the high-dimensional attributes. To visually mine and demonstrate the behavior features, we integrate WorldMapper and Voronoi Treemap into our glyph-like designs. Such visual fingerprint designs offer us the opportunity to summarize the interesting features and patterns from different check-in locations, activities and users (groups). We demonstrate the effectiveness and usability of our system by conducting extensive case studies on real check-in data collected from a popular microblogging service. Interesting findings are reported and discussed at last.

Analysis of Mining Terrain Deformation Characteristics with Deformation Information System

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Milczarek, Wojciech; Grzempowski, Piotr

2014-05-01

Mapping and prediction of mining related deformations of the earth surface is an important measure for minimising threat to surface infrastructure, human population, the environment and safety of the mining operation itself arising from underground extraction of useful minerals. The number of methods and techniques used for monitoring and analysis of mining terrain deformations is wide and increasing with the development of geographical information technologies. These include for example: terrestrial geodetic measurements, global positioning systems, remote sensing, spatial interpolation, finite element method modelling, GIS based modelling, geological modelling, empirical modelling using the Knothe theory, artificial neural networks, fuzzy logic calculations and other. The aim of this paper is to introduce the concept of an integrated Deformation Information System (DIS) developed in geographic information systems environment for analysis and modelling of various spatial data related to mining activity and demonstrate its applications for mapping and visualising, as well as identifying possible mining terrain deformation areas with various spatial modelling methods. The DIS concept is based on connected modules that include: the spatial database - the core of the system, the spatial data collection module formed by: terrestrial, satellite and remote sensing measurements of the ground changes, the spatial data mining module for data discovery and extraction, the geological modelling module, the spatial data modeling module with data processing algorithms for spatio-temporal analysis and mapping of mining deformations and their characteristics (e.g. deformation parameters: tilt, curvature and horizontal strain), the multivariate spatial data classification module and the visualization module allowing two-dimensional interactive and static mapping and three-dimensional visualizations of mining ground characteristics. The Systems's functionality has been presented on the case study of a coal mining region in SW Poland where it has been applied to study characteristics and map mining induced ground deformations in a city in the last two decades of underground coal extraction and in the first decade after the end of mining. The mining subsidence area and its deformation parameters (tilt and curvature) have been calculated and the latter classified and mapped according to the Polish regulations. In addition possible areas of ground deformation have been indicated based on multivariate spatial data analysis of geological and mining operation characteristics with the geographically weighted regression method.

Deriving movement properties and the effect of the environment from the Brownian bridge movement model in monkeys and birds.

PubMed

Buchin, Kevin; Sijben, Stef; van Loon, E Emiel; Sapir, Nir; Mercier, Stéphanie; Marie Arseneau, T Jean; Willems, Erik P

2015-01-01

The Brownian bridge movement model (BBMM) provides a biologically sound approximation of the movement path of an animal based on discrete location data, and is a powerful method to quantify utilization distributions. Computing the utilization distribution based on the BBMM while calculating movement parameters directly from the location data, may result in inconsistent and misleading results. We show how the BBMM can be extended to also calculate derived movement parameters. Furthermore we demonstrate how to integrate environmental context into a BBMM-based analysis. We develop a computational framework to analyze animal movement based on the BBMM. In particular, we demonstrate how a derived movement parameter (relative speed) and its spatial distribution can be calculated in the BBMM. We show how to integrate our framework with the conceptual framework of the movement ecology paradigm in two related but acutely different ways, focusing on the influence that the environment has on animal movement. First, we demonstrate an a posteriori approach, in which the spatial distribution of average relative movement speed as obtained from a "contextually naïve" model is related to the local vegetation structure within the monthly ranging area of a group of wild vervet monkeys. Without a model like the BBMM it would not be possible to estimate such a spatial distribution of a parameter in a sound way. Second, we introduce an a priori approach in which atmospheric information is used to calculate a crucial parameter of the BBMM to investigate flight properties of migrating bee-eaters. This analysis shows significant differences in the characteristics of flight modes, which would have not been detected without using the BBMM. Our algorithm is the first of its kind to allow BBMM-based computation of movement parameters beyond the utilization distribution, and we present two case studies that demonstrate two fundamentally different ways in which our algorithm can be applied to estimate the spatial distribution of average relative movement speed, while interpreting it in a biologically meaningful manner, across a wide range of environmental scenarios and ecological contexts. Therefore movement parameters derived from the BBMM can provide a powerful method for movement ecology research.

Spatial interpolation of hourly precipitation and dew point temperature for the identification of precipitation phase and hydrologic response in a mountainous catchment

NASA Astrophysics Data System (ADS)

Garen, D. C.; Kahl, A.; Marks, D. G.; Winstral, A. H.

2012-12-01

In mountainous catchments, it is well known that meteorological inputs, such as precipitation, air temperature, humidity, etc. vary greatly with elevation, spatial location, and time. Understanding and monitoring catchment inputs is necessary in characterizing and predicting hydrologic response to these inputs. This is true all of the time, but it is the most dramatically critical during large storms, when the input to the stream system due to rain and snowmelt creates the potential for flooding. Besides such crisis events, however, proper estimation of catchment inputs and their spatial distribution is also needed in more prosaic but no less important water and related resource management activities. The first objective of this study is to apply a geostatistical spatial interpolation technique (elevationally detrended kriging) to precipitation and dew point temperature on an hourly basis and explore its characteristics, accuracy, and other issues. The second objective is to use these spatial fields to determine precipitation phase (rain or snow) during a large, dynamic winter storm. The catchment studied is the data-rich Reynolds Creek Experimental Watershed near Boise, Idaho. As part of this analysis, precipitation-elevation lapse rates are examined for spatial and temporal consistency. A clear dependence of lapse rate on precipitation amount exists. Certain stations, however, are outliers from these relationships, showing that significant local effects can be present and raising the question of whether such stations should be used for spatial interpolation. Experiments with selecting subsets of stations demonstrate the importance of elevation range and spatial placement on the interpolated fields. Hourly spatial fields of precipitation and dew point temperature are used to distinguish precipitation phase during a large rain-on-snow storm in December 2005. This application demonstrates the feasibility of producing hourly spatial fields and the importance of doing so to support an accurate determination of precipitation phase for assessing catchment hydrologic response to the storm.

Modeling spatial competition for light in plant populations with the porous medium equation.

PubMed

Beyer, Robert; Etard, Octave; Cournède, Paul-Henry; Laurent-Gengoux, Pascal

2015-02-01

We consider a plant's local leaf area index as a spatially continuous variable, subject to particular reaction-diffusion dynamics of allocation, senescence and spatial propagation. The latter notably incorporates the plant's tendency to form new leaves in bright rather than shaded locations. Applying a generalized Beer-Lambert law allows to link existing foliage to production dynamics. The approach allows for inter-individual variability and competition for light while maintaining robustness-a key weakness of comparable existing models. The analysis of the single plant case leads to a significant simplification of the system's key equation when transforming it into the well studied porous medium equation. Confronting the theoretical model to experimental data of sugar beet populations, differing in configuration density, demonstrates its accuracy.

Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems.

PubMed

Wu, B M; van Bruggen, A H; Subbarao, K V; Pennings, G G

2001-02-01

ABSTRACT The epidemiology of lettuce downy mildew has been investigated extensively in coastal California. However, the spatial patterns of the disease and the distance that Bremia lactucae spores can be transported have not been determined. During 1995 to 1998, we conducted several field- and valley-scale surveys to determine spatial patterns of this disease in the Salinas valley. Geostatistical analyses of the survey data at both scales showed that the influence range of downy mildew incidence at one location on incidence at other locations was between 80 and 3,000 m. A linear relationship was detected between semivariance and lag distance at the field scale, although no single statistical model could fit the semi-variograms at the valley scale. Spatial interpolation by the inverse distance weighting method with a power of 2 resulted in plausible estimates of incidence throughout the valley. Cluster analysis in geographic information systems on the interpolated disease incidence from different dates demonstrated that the Salinas valley could be divided into two areas, north and south of Salinas City, with high and low disease pressure, respectively. Seasonal and spatial trends along the valley suggested that the distinction between the downy mildew conducive and nonconducive areas might be determined by environmental factors.

Assessment of the spatial pattern of colorectal tumour perfusion estimated at perfusion CT using two-dimensional fractal analysis.

PubMed

Goh, Vicky; Sanghera, Bal; Wellsted, David M; Sundin, Josefin; Halligan, Steve

2009-06-01

The aim was to evaluate the feasibility of fractal analysis for assessing the spatial pattern of colorectal tumour perfusion at dynamic contrast-enhanced CT (perfusion CT). Twenty patients with colorectal adenocarcinoma underwent a 65-s perfusion CT study from which a perfusion parametric map was generated using validated commercial software. The tumour was identified by an experienced radiologist, segmented via thresholding and fractal analysis applied using in-house software: fractal dimension, abundance and lacunarity were assessed for the entire outlined tumour and for selected representative areas within the tumour of low and high perfusion. Comparison was made with ten patients with normal colons, processed in a similar manner, using two-way mixed analysis of variance with statistical significance at the 5% level. Fractal values were higher in cancer than normal colon (p < or = 0.001): mean (SD) 1.71 (0.07) versus 1.61 (0.07) for fractal dimension and 7.82 (0.62) and 6.89 (0.47) for fractal abundance. Fractal values were lower in 'high' than 'low' perfusion areas. Lacunarity curves were shifted to the right for cancer compared with normal colon. In conclusion, colorectal cancer mapped by perfusion CT demonstrates fractal properties. Fractal analysis is feasible, potentially providing a quantitative measure of the spatial pattern of tumour perfusion.

Epidemiologic evaluation of diarrhea in dogs in an animal shelter.

PubMed

Sokolow, Susanne H; Rand, Courtney; Marks, Stanley L; Drazenovich, Niki L; Kather, Elizabeth J; Foley, Janet E

2005-06-01

To determine associations among infectious pathogens and diarrheal disease in dogs in an animal shelter and demonstrate the use of geographic information systems (GISs) for tracking spatial distributions of diarrheal disease within shelters. Feces from 120 dogs. Fresh fecal specimens were screened for bacteria and bacterial toxins via bacteriologic culture and ELISA, parvovirus via ELISA, canine coronavirus via nested polymerase chain reaction assay, protozoal cysts and oocysts via a direct fluorescent antibody technique, and parasite ova and larvae via microscopic examination of direct wet mounts and zinc sulfate centrifugation flotation. Salmonella enterica and Brachyspira spp were not common, whereas other pathogens such as canine coronavirus and Helicobacter spp were common among the dogs that were surveyed. Only intestinal parasites and Campylobacterjejuni infection were significant risk factors for diarrhea by univariate odds ratio analysis. Giardia lamblia was significantly underestimated by fecal flotation, compared with a direct fluorescent antibody technique. Spatial analysis of case specimens by use of GIS indicated that diarrhea was widespread throughout the entire shelter, and spatial statistical analysis revealed no evidence of spatial clustering of case specimens. This study provided an epidemiologic overview of diarrhea and interacting diarrhea-associated pathogens in a densely housed, highly predisposed shelter population of dogs. Several of the approaches used in this study, such as use of a spatial representation of case specimens and considering multiple etiologies simultaneously, were novel and illustrate an integrated approach to epidemiologic investigations in shelter populations.

Multi-Scale and Object-Oriented Analysis for Mountain Terrain Segmentation and Geomorphological Assessment

NASA Astrophysics Data System (ADS)

Marston, B. K.; Bishop, M. P.; Shroder, J. F.

2009-12-01

Digital terrain analysis of mountain topography is widely utilized for mapping landforms, assessing the role of surface processes in landscape evolution, and estimating the spatial variation of erosion. Numerous geomorphometry techniques exist to characterize terrain surface parameters, although their utility to characterize the spatial hierarchical structure of the topography and permit an assessment of the erosion/tectonic impact on the landscape is very limited due to scale and data integration issues. To address this problem, we apply scale-dependent geomorphometric and object-oriented analyses to characterize the hierarchical spatial structure of mountain topography. Specifically, we utilized a high resolution digital elevation model to characterize complex topography in the Shimshal Valley in the Western Himalaya of Pakistan. To accomplish this, we generate terrain objects (geomorphological features and landform) including valley floors and walls, drainage basins, drainage network, ridge network, slope facets, and elemental forms based upon curvature. Object-oriented analysis was used to characterize object properties accounting for object size, shape, and morphometry. The spatial overlay and integration of terrain objects at various scales defines the nature of the hierarchical organization. Our results indicate that variations in the spatial complexity of the terrain hierarchical organization is related to the spatio-temporal influence of surface processes and landscape evolution dynamics. Terrain segmentation and the integration of multi-scale terrain information permits further assessment of process domains and erosion, tectonic impact potential, and natural hazard potential. We demonstrate this with landform mapping and geomorphological assessment examples.

Strategy Diversity Stabilizes Mutualism through Investment Cycles, Phase Polymorphism, and Spatial Bubbles

PubMed Central

Boza, Gergely; Kun, Ádám; Scheuring, István; Dieckmann, Ulf

2012-01-01

There is continuing interest in understanding factors that facilitate the evolution and stability of cooperation within and between species. Such interactions will often involve plasticity in investment behavior, in response to the interacting partner's investments. Our aim here is to investigate the evolution and stability of reciprocal investment behavior in interspecific interactions, a key phenomenon strongly supported by experimental observations. In particular, we present a comprehensive analysis of a continuous reciprocal investment game between mutualists, both in well-mixed and spatially structured populations, and we demonstrate a series of novel mechanisms for maintaining interspecific mutualism. We demonstrate that mutualistic partners invariably follow investment cycles, during which mutualism first increases, before both partners eventually reduce their investments to zero, so that these cycles always conclude with full defection. We show that the key mechanism for stabilizing mutualism is phase polymorphism along the investment cycle. Although mutualistic partners perpetually change their strategies, the community-level distribution of investment levels becomes stationary. In spatially structured populations, the maintenance of polymorphism is further facilitated by dynamic mosaic structures, in which mutualistic partners form expanding and collapsing spatial bubbles or clusters. Additionally, we reveal strategy-diversity thresholds, both for well-mixed and spatially structured mutualistic communities, and discuss factors for meeting these thresholds, and thus maintaining mutualism. Our results demonstrate that interspecific mutualism, when considered as plastic investment behavior, can be unstable, and, in agreement with empirical observations, may involve a polymorphism of investment levels, varying both in space and in time. Identifying the mechanisms maintaining such polymorphism, and hence mutualism in natural communities, provides a significant step towards understanding the coevolution and population dynamics of mutualistic interactions. PMID:23166478

Strategy diversity stabilizes mutualism through investment cycles, phase polymorphism, and spatial bubbles.

PubMed

Boza, Gergely; Kun, Adám; Scheuring, István; Dieckmann, Ulf

2012-01-01

There is continuing interest in understanding factors that facilitate the evolution and stability of cooperation within and between species. Such interactions will often involve plasticity in investment behavior, in response to the interacting partner's investments. Our aim here is to investigate the evolution and stability of reciprocal investment behavior in interspecific interactions, a key phenomenon strongly supported by experimental observations. In particular, we present a comprehensive analysis of a continuous reciprocal investment game between mutualists, both in well-mixed and spatially structured populations, and we demonstrate a series of novel mechanisms for maintaining interspecific mutualism. We demonstrate that mutualistic partners invariably follow investment cycles, during which mutualism first increases, before both partners eventually reduce their investments to zero, so that these cycles always conclude with full defection. We show that the key mechanism for stabilizing mutualism is phase polymorphism along the investment cycle. Although mutualistic partners perpetually change their strategies, the community-level distribution of investment levels becomes stationary. In spatially structured populations, the maintenance of polymorphism is further facilitated by dynamic mosaic structures, in which mutualistic partners form expanding and collapsing spatial bubbles or clusters. Additionally, we reveal strategy-diversity thresholds, both for well-mixed and spatially structured mutualistic communities, and discuss factors for meeting these thresholds, and thus maintaining mutualism. Our results demonstrate that interspecific mutualism, when considered as plastic investment behavior, can be unstable, and, in agreement with empirical observations, may involve a polymorphism of investment levels, varying both in space and in time. Identifying the mechanisms maintaining such polymorphism, and hence mutualism in natural communities, provides a significant step towards understanding the coevolution and population dynamics of mutualistic interactions.

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data.

PubMed

Wu, Jidong; Li, Ying; Li, Ning; Shi, Peijun

2018-01-01

The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top-down (or downscaling) approach to disaggregate administrative-unit level asset value to grid-cell level. To do so, finding the highly correlated "surrogate" indicators is the key. A combination of three data sets-nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc-second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time. © 2017 Society for Risk Analysis.

Spatial-temporal-covariance-based modeling, analysis, and simulation of aero-optics wavefront aberrations.

PubMed

Vogel, Curtis R; Tyler, Glenn A; Wittich, Donald J

2014-07-01

We introduce a framework for modeling, analysis, and simulation of aero-optics wavefront aberrations that is based on spatial-temporal covariance matrices extracted from wavefront sensor measurements. Within this framework, we present a quasi-homogeneous structure function to analyze nonhomogeneous, mildly anisotropic spatial random processes, and we use this structure function to show that phase aberrations arising in aero-optics are, for an important range of operating parameters, locally Kolmogorov. This strongly suggests that the d5/3 power law for adaptive optics (AO) deformable mirror fitting error, where d denotes actuator separation, holds for certain important aero-optics scenarios. This framework also allows us to compute bounds on AO servo lag error and predictive control error. In addition, it provides us with the means to accurately simulate AO systems for the mitigation of aero-effects, and it may provide insight into underlying physical processes associated with turbulent flow. The techniques introduced here are demonstrated using data obtained from the Airborne Aero-Optics Laboratory.

Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques.

PubMed

Long, Zhiying; Chen, Kewei; Wu, Xia; Reiman, Eric; Peng, Danling; Yao, Li

2009-02-01

Spatial Independent component analysis (sICA) has been widely used to analyze functional magnetic resonance imaging (fMRI) data. The well accepted implicit assumption is the spatially statistical independency of intrinsic sources identified by sICA, making the sICA applications difficult for data in which there exist interdependent sources and confounding factors. This interdependency can arise, for instance, from fMRI studies investigating two tasks in a single session. In this study, we introduced a linear projection approach and considered its utilization as a tool to separate task-related components from two-task fMRI data. The robustness and feasibility of the method are substantiated through simulation on computer data and fMRI real rest data. Both simulated and real two-task fMRI experiments demonstrated that sICA in combination with the projection method succeeded in separating spatially dependent components and had better detection power than pure model-based method when estimating activation induced by each task as well as both tasks.

Measuring the spatial resolution of an optical system in an undergraduate optics laboratory

NASA Astrophysics Data System (ADS)

Leung, Calvin; Donnelly, T. D.

2017-06-01

Two methods of quantifying the spatial resolution of a camera are described, performed, and compared, with the objective of designing an imaging-system experiment for students in an undergraduate optics laboratory. With the goal of characterizing the resolution of a typical digital single-lens reflex (DSLR) camera, we motivate, introduce, and show agreement between traditional test-target contrast measurements and the technique of using Fourier analysis to obtain the modulation transfer function (MTF). The advantages and drawbacks of each method are compared. Finally, we explore the rich optical physics at work in the camera system by calculating the MTF as a function of wavelength and f-number. For example, we find that the Canon 40D demonstrates better spatial resolution at short wavelengths, in accordance with scalar diffraction theory, but is not diffraction-limited, being significantly affected by spherical aberration. The experiment and data analysis routines described here can be built and written in an undergraduate optics lab setting.

Distortion correction and cross-talk compensation algorithm for use with an imaging spectrometer based spatially resolved diffuse reflectance system

NASA Astrophysics Data System (ADS)

Cappon, Derek J.; Farrell, Thomas J.; Fang, Qiyin; Hayward, Joseph E.

2016-12-01

Optical spectroscopy of human tissue has been widely applied within the field of biomedical optics to allow rapid, in vivo characterization and analysis of the tissue. When designing an instrument of this type, an imaging spectrometer is often employed to allow for simultaneous analysis of distinct signals. This is especially important when performing spatially resolved diffuse reflectance spectroscopy. In this article, an algorithm is presented that allows for the automated processing of 2-dimensional images acquired from an imaging spectrometer. The algorithm automatically defines distinct spectrometer tracks and adaptively compensates for distortion introduced by optical components in the imaging chain. Crosstalk resulting from the overlap of adjacent spectrometer tracks in the image is detected and subtracted from each signal. The algorithm's performance is demonstrated in the processing of spatially resolved diffuse reflectance spectra recovered from an Intralipid and ink liquid phantom and is shown to increase the range of wavelengths over which usable data can be recovered.

Exhaled Aerosol Pattern Discloses Lung Structural Abnormality: A Sensitivity Study Using Computational Modeling and Fractal Analysis

PubMed Central

Xi, Jinxiang; Si, Xiuhua A.; Kim, JongWon; Mckee, Edward; Lin, En-Bing

2014-01-01

Background Exhaled aerosol patterns, also called aerosol fingerprints, provide clues to the health of the lung and can be used to detect disease-modified airway structures. The key is how to decode the exhaled aerosol fingerprints and retrieve the lung structural information for a non-invasive identification of respiratory diseases. Objective and Methods In this study, a CFD-fractal analysis method was developed to quantify exhaled aerosol fingerprints and applied it to one benign and three malign conditions: a tracheal carina tumor, a bronchial tumor, and asthma. Respirations of tracer aerosols of 1 µm at a flow rate of 30 L/min were simulated, with exhaled distributions recorded at the mouth. Large eddy simulations and a Lagrangian tracking approach were used to simulate respiratory airflows and aerosol dynamics. Aerosol morphometric measures such as concentration disparity, spatial distributions, and fractal analysis were applied to distinguish various exhaled aerosol patterns. Findings Utilizing physiology-based modeling, we demonstrated substantial differences in exhaled aerosol distributions among normal and pathological airways, which were suggestive of the disease location and extent. With fractal analysis, we also demonstrated that exhaled aerosol patterns exhibited fractal behavior in both the entire image and selected regions of interest. Each exhaled aerosol fingerprint exhibited distinct pattern parameters such as spatial probability, fractal dimension, lacunarity, and multifractal spectrum. Furthermore, a correlation of the diseased location and exhaled aerosol spatial distribution was established for asthma. Conclusion Aerosol-fingerprint-based breath tests disclose clues about the site and severity of lung diseases and appear to be sensitive enough to be a practical tool for diagnosis and prognosis of respiratory diseases with structural abnormalities. PMID:25105680

Close-Packed Silicon Microelectrodes for Scalable Spatially Oversampled Neural Recording

PubMed Central

Scholvin, Jörg; Kinney, Justin P.; Bernstein, Jacob G.; Moore-Kochlacs, Caroline; Kopell, Nancy; Fonstad, Clifton G.; Boyden, Edward S.

2015-01-01

Objective Neural recording electrodes are important tools for understanding neural codes and brain dynamics. Neural electrodes that are close-packed, such as in tetrodes, enable spatial oversampling of neural activity, which facilitates data analysis. Here we present the design and implementation of close-packed silicon microelectrodes, to enable spatially oversampled recording of neural activity in a scalable fashion. Methods Our probes are fabricated in a hybrid lithography process, resulting in a dense array of recording sites connected to submicron dimension wiring. Results We demonstrate an implementation of a probe comprising 1000 electrode pads, each 9 × 9 μm, at a pitch of 11 μm. We introduce design automation and packaging methods that allow us to readily create a large variety of different designs. Significance Finally, we perform neural recordings with such probes in the live mammalian brain that illustrate the spatial oversampling potential of closely packed electrode sites. PMID:26699649

An Introduction to Macro- Level Spatial Nonstationarity: a Geographically Weighted Regression Analysis of Diabetes and Poverty

PubMed Central

Siordia, Carlos; Saenz, Joseph; Tom, Sarah E.

2014-01-01

Type II diabetes is a growing health problem in the United States. Understanding geographic variation in diabetes prevalence will inform where resources for management and prevention should be allocated. Investigations of the correlates of diabetes prevalence have largely ignored how spatial nonstationarity might play a role in the macro-level distribution of diabetes. This paper introduces the reader to the concept of spatial nonstationarity—variance in statistical relationships as a function of geographical location. Since spatial nonstationarity means different predictors can have varying effects on model outcomes, we make use of a geographically weighed regression to calculate correlates of diabetes as a function of geographic location. By doing so, we demonstrate an exploratory example in which the diabetes-poverty macro-level statistical relationship varies as a function of location. In particular, we provide evidence that when predicting macro-level diabetes prevalence, poverty is not always positively associated with diabetes PMID:25414731

Development and evaluation of a Hadamard transform imaging spectrometer and a Hadamard transform thermal imager

NASA Technical Reports Server (NTRS)

Harwit, M.; Swift, R.; Wattson, R.; Decker, J.; Paganetti, R.

1976-01-01

A spectrometric imager and a thermal imager, which achieve multiplexing by the use of binary optical encoding masks, were developed. The masks are based on orthogonal, pseudorandom digital codes derived from Hadamard matrices. Spatial and/or spectral data is obtained in the form of a Hadamard transform of the spatial and/or spectral scene; computer algorithms are then used to decode the data and reconstruct images of the original scene. The hardware, algorithms and processing/display facility are described. A number of spatial and spatial/spectral images are presented. The achievement of a signal-to-noise improvement due to the signal multiplexing was also demonstrated. An analysis of the results indicates both the situations for which the multiplex advantage may be gained, and the limitations of the technique. A number of potential applications of the spectrometric imager are discussed.

An Introduction to Macro- Level Spatial Nonstationarity: a Geographically Weighted Regression Analysis of Diabetes and Poverty.

PubMed

Siordia, Carlos; Saenz, Joseph; Tom, Sarah E

2012-01-01

Type II diabetes is a growing health problem in the United States. Understanding geographic variation in diabetes prevalence will inform where resources for management and prevention should be allocated. Investigations of the correlates of diabetes prevalence have largely ignored how spatial nonstationarity might play a role in the macro-level distribution of diabetes. This paper introduces the reader to the concept of spatial nonstationarity-variance in statistical relationships as a function of geographical location. Since spatial nonstationarity means different predictors can have varying effects on model outcomes, we make use of a geographically weighed regression to calculate correlates of diabetes as a function of geographic location. By doing so, we demonstrate an exploratory example in which the diabetes-poverty macro-level statistical relationship varies as a function of location. In particular, we provide evidence that when predicting macro-level diabetes prevalence, poverty is not always positively associated with diabetes.

The parietal cortex in sensemaking: the dissociation of multiple types of spatial information.

PubMed

Sun, Yanlong; Wang, Hongbin

2013-01-01

According to the data-frame theory, sensemaking is a macrocognitive process in which people try to make sense of or explain their observations by processing a number of explanatory structures called frames until the observations and frames become congruent. During the sensemaking process, the parietal cortex has been implicated in various cognitive tasks for the functions related to spatial and temporal information processing, mathematical thinking, and spatial attention. In particular, the parietal cortex plays important roles by extracting multiple representations of magnitudes at the early stages of perceptual analysis. By a series of neural network simulations, we demonstrate that the dissociation of different types of spatial information can start early with a rather similar structure (i.e., sensitivity on a common metric), but accurate representations require specific goal-directed top-down controls due to the interference in selective attention. Our results suggest that the roles of the parietal cortex rely on the hierarchical organization of multiple spatial representations and their interactions. The dissociation and interference between different types of spatial information are essentially the result of the competition at different levels of abstraction.

The Parietal Cortex in Sensemaking: The Dissociation of Multiple Types of Spatial Information

PubMed Central

Sun, Yanlong; Wang, Hongbin

2013-01-01

According to the data-frame theory, sensemaking is a macrocognitive process in which people try to make sense of or explain their observations by processing a number of explanatory structures called frames until the observations and frames become congruent. During the sensemaking process, the parietal cortex has been implicated in various cognitive tasks for the functions related to spatial and temporal information processing, mathematical thinking, and spatial attention. In particular, the parietal cortex plays important roles by extracting multiple representations of magnitudes at the early stages of perceptual analysis. By a series of neural network simulations, we demonstrate that the dissociation of different types of spatial information can start early with a rather similar structure (i.e., sensitivity on a common metric), but accurate representations require specific goal-directed top-down controls due to the interference in selective attention. Our results suggest that the roles of the parietal cortex rely on the hierarchical organization of multiple spatial representations and their interactions. The dissociation and interference between different types of spatial information are essentially the result of the competition at different levels of abstraction. PMID:23710165

Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Dwirahmadi, Febi; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Do, Cuong Manh; Nguyen, Trung Hieu; Dinh, Tuan Anh Diep

2015-05-01

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

Minimizing Spatial Variability of Healthcare Spatial Accessibility-The Case of a Dengue Fever Outbreak.

PubMed

Chu, Hone-Jay; Lin, Bo-Cheng; Yu, Ming-Run; Chan, Ta-Chien

2016-12-13

Outbreaks of infectious diseases or multi-casualty incidents have the potential to generate a large number of patients. It is a challenge for the healthcare system when demand for care suddenly surges. Traditionally, valuation of heath care spatial accessibility was based on static supply and demand information. In this study, we proposed an optimal model with the three-step floating catchment area (3SFCA) to account for the supply to minimize variability in spatial accessibility. We used empirical dengue fever outbreak data in Tainan City, Taiwan in 2015 to demonstrate the dynamic change in spatial accessibility based on the epidemic trend. The x and y coordinates of dengue-infected patients with precision loss were provided publicly by the Tainan City government, and were used as our model's demand. The spatial accessibility of heath care during the dengue outbreak from August to October 2015 was analyzed spatially and temporally by producing accessibility maps, and conducting capacity change analysis. This study also utilized the particle swarm optimization (PSO) model to decrease the spatial variation in accessibility and shortage areas of healthcare resources as the epidemic went on. The proposed method in this study can help decision makers reallocate healthcare resources spatially when the ratios of demand and supply surge too quickly and form clusters in some locations.

Preliminary Geologic/spectral Analysis of LANDSAT-4 Thematic Mapper Data, Wind River/bighorn Basin Area, Wyoming

NASA Technical Reports Server (NTRS)

Lang, H. R.; Conel, J. E.; Paylor, E. D.

1984-01-01

A LIDQA evaluation for geologic applications of a LANDSAT TM scene covering the Wind River/Bighorn Basin area, Wyoming, is examined. This involves a quantitative assessment of data quality including spatial and spectral characteristics. Analysis is concentrated on the 6 visible, near infrared, and short wavelength infrared bands. Preliminary analysis demonstrates that: (1) principal component images derived from the correlation matrix provide the most useful geologic information. To extract surface spectral reflectance, the TM radiance data must be calibrated. Scatterplots demonstrate that TM data can be calibrated and sensor response is essentially linear. Low instrumental offset and gain settings result in spectral data that do not utilize the full dynamic range of the TM system.

A high-resolution genetic signature of demographic and spatial expansion in epizootic rabies virus

PubMed Central

Biek, Roman; Henderson, J. Caroline; Waller, Lance A.; Rupprecht, Charles E.; Real, Leslie A.

2007-01-01

Emerging pathogens potentially undergo rapid evolution while expanding in population size and geographic range during the course of invasion, yet it is generally difficult to demonstrate how these processes interact. Our analysis of a 30-yr data set covering a large-scale rabies virus outbreak among North American raccoons reveals the long lasting effect of the initial infection wave in determining how viral populations are genetically structured in space. We further find that coalescent-based estimates derived from the genetic data yielded an amazingly accurate reconstruction of the known spatial and demographic dynamics of the virus over time. Our study demonstrates the combined evolutionary and population dynamic processes characterizing the spread of pathogen after its introduction into a fully susceptible host population. Furthermore, the results provide important insights regarding the spatial scale of rabies persistence and validate the use of coalescent approaches for uncovering even relatively complex population histories. Such approaches will be of increasing relevance for understanding the epidemiology of emerging zoonotic diseases in a landscape context. PMID:17470818

Water quality modeling based on landscape analysis: Importance of riparian hydrology

Treesearch

Thomas Grabs

2010-01-01

Several studies in high-latitude catchments have demonstrated the importance of near-stream riparian zones as hydrogeochemical hotspots with a substantial influence on stream chemistry. An adequate representation of the spatial variability of riparian-zone processes and characteristics is the key for modeling spatiotemporal variations of stream-water quality. This...

Dead Crow Reports and Location of Human West Nile Virus Cases, Chicago, 2002

PubMed Central

Jones, Roderick C.; Gibbs, Kevin; Paul, William

2004-01-01

During the summer and fall of 2002, an epidemic (223 cases) and epizootic of West Nile virus infections occurred in Chicago. Retrospective spatial analysis demonstrated that age-adjusted human case rates were three times higher inside geographic areas with high early-season crow deaths than outside these areas. PMID:15200837

Variation in Phytochemical Composition Reveals Distinct Divergence of Aloe vera (L.) Burm.f. From Other Aloe Species: Rationale Behind Selective Preference of Aloe vera in Nutritional and Therapeutic Use

PubMed Central

Dey, Priyankar; Dutta, Somit; Chowdhury, Anurag; Das, Abhaya Prasad; Chaudhuri, Tapas Kumar

2017-01-01

In the present study, we have phytochemically characterized 5 different abundant Aloe species, including Aloe vera (L.) Burm.f., using silylation followed by Gas Chromatography-Mass Spectrometry technique and compared the data using multivariate statistical analysis. The results demonstrated clear distinction of the overall phytochemical profile of A vera, highlighted by its divergent spatial arrangement in the component plot. Lowest correlation of the phytochemical profiles were found between A vera and A aristata Haw. (−0.626), whereas highest correlation resided between A aristata and A aspera Haw. (0.899). Among the individual phytochemicals, palmitic acid was identified in highest abundance cumulatively, and carboxylic acids were the most predominant phytochemical species in all the Aloe species. Compared to A vera, linear correlation analysis revealed highest and lowest correlation with A aspera (R 2 = 0.9162) and A aristata (R 2 = 0.6745), respectively. Therefore, A vera demonstrated distinct spatial allocation, reflecting its greater phytochemical variability. PMID:29228808

The Impact of Fluid Inertia on In Vivo Estimation of Mitral Valve Leaflet Constitutive Properties and Mechanics.

PubMed

Bark, David L; Dasi, Lakshmi P

2016-05-01

We examine the influence of the added mass effect (fluid inertia) on mitral valve leaflet stress during isovolumetric phases. To study this effect, oscillating flow is applied to a flexible membrane at various frequencies to control inertia. Resulting membrane strain is calculated through a three-dimensional reconstruction of markers from stereo images. To investigate the effect in vivo, the analysis is repeated on a published dataset for an ovine mitral valve (Journal of Biomechanics 42(16): 2697-2701). The membrane experiment demonstrates that the relationship between pressure and strain must be corrected with a fluid inertia term if the ratio of inertia to pressure differential approaches 1. In the mitral valve, this ratio reaches 0.7 during isovolumetric contraction for an acceleration of 6 m/s(2). Acceleration is reduced by 72% during isovolumetric relaxation. Fluid acceleration also varies along the leaflet during isovolumetric phases, resulting in spatial variations in stress. These results demonstrate that fluid inertia may be the source of the temporally and spatially varying stiffness measurements previously seen through inverse finite element analysis of in vivo data during isovolumetric phases. This study demonstrates that there is a need to account for added mass effects when analyzing in vivo constitutive relationships of heart valves.

Groundwater-fed irrigation impacts spatially distributed temporal scaling behavior of the natural system: a spatio-temporal framework for understanding water management impacts

NASA Astrophysics Data System (ADS)

Condon, Laura E.; Maxwell, Reed M.

2014-03-01

Regional scale water management analysis increasingly relies on integrated modeling tools. Much recent work has focused on groundwater-surface water interactions and feedbacks. However, to our knowledge, no study has explicitly considered impacts of management operations on the temporal dynamics of the natural system. Here, we simulate twenty years of hourly moisture dependent, groundwater-fed irrigation using a three-dimensional, fully integrated, hydrologic model (ParFlow-CLM). Results highlight interconnections between irrigation demand, groundwater oscillation frequency and latent heat flux variability not previously demonstrated. Additionally, the three-dimensional model used allows for novel consideration of spatial patterns in temporal dynamics. Latent heat flux and water table depth both display spatial organization in temporal scaling, an important finding given the spatial homogeneity and weak scaling observed in atmospheric forcings. Pumping and irrigation amplify high frequency (sub-annual) variability while attenuating low frequency (inter-annual) variability. Irrigation also intensifies scaling within irrigated areas, essentially increasing temporal memory in both the surface and the subsurface. These findings demonstrate management impacts that extend beyond traditional water balance considerations to the fundamental behavior of the system itself. This is an important step to better understanding groundwater’s role as a buffer for natural variability and the impact that water management has on this capacity.

Image Stability Requirements For a Geostationary Imaging Fourier Transform Spectrometer (GIFTS)

NASA Technical Reports Server (NTRS)

Bingham, G. E.; Cantwell, G.; Robinson, R. C.; Revercomb, H. E.; Smith, W. L.

2001-01-01

A Geostationary Imaging Fourier Transform Spectrometer (GIFTS) has been selected for the NASA New Millennium Program (NMP) Earth Observing-3 (EO-3) mission. Our paper will discuss one of the key GIFTS measurement requirements, Field of View (FOV) stability, and its impact on required system performance. The GIFTS NMP mission is designed to demonstrate new and emerging sensor and data processing technologies with the goal of making revolutionary improvements in meteorological observational capability and forecasting accuracy. The GIFTS payload is a versatile imaging FTS with programmable spectral resolution and spatial scene selection that allows radiometric accuracy and atmospheric sounding precision to be traded in near real time for area coverage. The GIFTS sensor combines high sensitivity with a massively parallel spatial data collection scheme to allow high spatial resolution measurement of the Earth's atmosphere and rapid broad area coverage. An objective of the GIFTS mission is to demonstrate the advantages of high spatial resolution (4 km ground sample distance - gsd) on temperature and water vapor retrieval by allowing sampling in broken cloud regions. This small gsd, combined with the relatively long scan time required (approximately 10 s) to collect high resolution spectra from geostationary (GEO) orbit, may require extremely good pointing control. This paper discusses the analysis of this requirement.

Spatial mapping and quantification of developmental branching morphogenesis.

PubMed

Short, Kieran; Hodson, Mark; Smyth, Ian

2013-01-15

Branching morphogenesis is a fundamental developmental mechanism that shapes the formation of many organs. The complex three-dimensional shapes derived by this process reflect equally complex genetic interactions between branching epithelia and their surrounding mesenchyme. Despite the importance of this process to normal adult organ function, analysis of branching has been stymied by the absence of a bespoke method to quantify accurately the complex spatial datasets that describe it. As a consequence, although many developmentally important genes are proposed to influence branching morphogenesis, we have no way of objectively assessing their individual contributions to this process. We report the development of a method for accurately quantifying many aspects of branching morphogenesis and we demonstrate its application to the study of organ development. As proof of principle we have employed this approach to analyse the developing mouse lung and kidney, describing the spatial characteristics of the branching ureteric bud and pulmonary epithelia. To demonstrate further its capacity to profile unrecognised genetic contributions to organ development, we examine Tgfb2 mutant kidneys, identifying elements of both developmental delay and specific spatial dysmorphology caused by haplo-insufficiency for this gene. This technical advance provides a crucial resource that will enable rigorous characterisation of the genetic and environmental factors that regulate this essential and evolutionarily conserved developmental mechanism.

Toward seamless hydrologic predictions across spatial scales

NASA Astrophysics Data System (ADS)

Samaniego, Luis; Kumar, Rohini; Thober, Stephan; Rakovec, Oldrich; Zink, Matthias; Wanders, Niko; Eisner, Stephanie; Müller Schmied, Hannes; Sutanudjaja, Edwin H.; Warrach-Sagi, Kirsten; Attinger, Sabine

2017-09-01

Land surface and hydrologic models (LSMs/HMs) are used at diverse spatial resolutions ranging from catchment-scale (1-10 km) to global-scale (over 50 km) applications. Applying the same model structure at different spatial scales requires that the model estimates similar fluxes independent of the chosen resolution, i.e., fulfills a flux-matching condition across scales. An analysis of state-of-the-art LSMs and HMs reveals that most do not have consistent hydrologic parameter fields. Multiple experiments with the mHM, Noah-MP, PCR-GLOBWB, and WaterGAP models demonstrate the pitfalls of deficient parameterization practices currently used in most operational models, which are insufficient to satisfy the flux-matching condition. These examples demonstrate that J. Dooge's 1982 statement on the unsolved problem of parameterization in these models remains true. Based on a review of existing parameter regionalization techniques, we postulate that the multiscale parameter regionalization (MPR) technique offers a practical and robust method that provides consistent (seamless) parameter and flux fields across scales. Herein, we develop a general model protocol to describe how MPR can be applied to a particular model and present an example application using the PCR-GLOBWB model. Finally, we discuss potential advantages and limitations of MPR in obtaining the seamless prediction of hydrological fluxes and states across spatial scales.

Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques.

PubMed

Li, Siyue; Zhang, Quanfa

2010-04-15

A data matrix (4032 observations), obtained during a 2-year monitoring period (2005-2006) from 42 sites in the upper Han River is subjected to various multivariate statistical techniques including cluster analysis, principal component analysis (PCA), factor analysis (FA), correlation analysis and analysis of variance to determine the spatial characterization of dissolved trace elements and heavy metals. Our results indicate that waters in the upper Han River are primarily polluted by Al, As, Cd, Pb, Sb and Se, and the potential pollutants include Ba, Cr, Hg, Mn and Ni. Spatial distribution of trace metals indicates the polluted sections mainly concentrate in the Danjiang, Danjiangkou Reservoir catchment and Hanzhong Plain, and the most contaminated river is in the Hanzhong Plain. Q-model clustering depends on geographical location of sampling sites and groups the 42 sampling sites into four clusters, i.e., Danjiang, Danjiangkou Reservoir region (lower catchment), upper catchment and one river in headwaters pertaining to water quality. The headwaters, Danjiang and lower catchment, and upper catchment correspond to very high polluted, moderate polluted and relatively low polluted regions, respectively. Additionally, PCA/FA and correlation analysis demonstrates that Al, Cd, Mn, Ni, Fe, Si and Sr are controlled by natural sources, whereas the other metals appear to be primarily controlled by anthropogenic origins though geogenic source contributing to them. 2009 Elsevier B.V. All rights reserved.

Aliasing Detection and Reduction Scheme on Angularly Undersampled Light Fields.

PubMed

Xiao, Zhaolin; Wang, Qing; Zhou, Guoqing; Yu, Jingyi

2017-05-01

When using plenoptic camera for digital refocusing, angular undersampling can cause severe (angular) aliasing artifacts. Previous approaches have focused on avoiding aliasing by pre-processing the acquired light field via prefiltering, demosaicing, reparameterization, and so on. In this paper, we present a different solution that first detects and then removes angular aliasing at the light field refocusing stage. Different from previous frequency domain aliasing analysis, we carry out a spatial domain analysis to reveal whether the angular aliasing would occur and uncover where in the image it would occur. The spatial analysis also facilitates easy separation of the aliasing versus non-aliasing regions and angular aliasing removal. Experiments on both synthetic scene and real light field data sets (camera array and Lytro camera) demonstrate that our approach has a number of advantages over the classical prefiltering and depth-dependent light field rendering techniques.

Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore

PubMed Central

Ibrahim, Bashar; Henze, Richard; Gruenert, Gerd; Egbert, Matthew; Huwald, Jan; Dittrich, Peter

2013-01-01

A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models. PMID:24709796

Spatio-temporal models of mental processes from fMRI.

PubMed

Janoos, Firdaus; Machiraju, Raghu; Singh, Shantanu; Morocz, Istvan Ákos

2011-07-15

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Conventional analysis methods focus on the spatial dimension of the data discarding the information about brain function contained in the temporal dimension. This paper presents a fully spatio-temporal multivariate analysis method using a state-space model (SSM) for brain function that yields not only spatial maps of activity but also its temporal structure along with spatially varying estimates of the hemodynamic response. Efficient algorithms for estimating the parameters along with quantitative validations are given. A novel low-dimensional feature-space for representing the data, based on a formal definition of functional similarity, is derived. Quantitative validation of the model and the estimation algorithms is provided with a simulation study. Using a real fMRI study for mental arithmetic, the ability of this neurophysiologically inspired model to represent the spatio-temporal information corresponding to mental processes is demonstrated. Moreover, by comparing the models across multiple subjects, natural patterns in mental processes organized according to different mental abilities are revealed. Copyright © 2011 Elsevier Inc. All rights reserved.

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

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

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized inmore » the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.« less

Improved spatial regression analysis of diffusion tensor imaging for lesion detection during longitudinal progression of multiple sclerosis in individual subjects

NASA Astrophysics Data System (ADS)

Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui

2016-03-01

Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.

Using spatial principles to optimize distributed computing for enabling the physical science discoveries

PubMed Central

Yang, Chaowei; Wu, Huayi; Huang, Qunying; Li, Zhenlong; Li, Jing

2011-01-01

Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century. PMID:21444779

Using spatial principles to optimize distributed computing for enabling the physical science discoveries.

PubMed

Yang, Chaowei; Wu, Huayi; Huang, Qunying; Li, Zhenlong; Li, Jing

2011-04-05

Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century.

OpenMSI Arrayed Analysis Toolkit: Analyzing Spatially Defined Samples Using Mass Spectrometry Imaging

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

de Raad, Markus; de Rond, Tristan; Rübel, Oliver

Mass spectrometry imaging (MSI) has primarily been applied in localizing biomolecules within biological matrices. Although well-suited, the application of MSI for comparing thousands of spatially defined spotted samples has been limited. One reason for this is a lack of suitable and accessible data processing tools for the analysis of large arrayed MSI sample sets. In this paper, the OpenMSI Arrayed Analysis Toolkit (OMAAT) is a software package that addresses the challenges of analyzing spatially defined samples in MSI data sets. OMAAT is written in Python and is integrated with OpenMSI (http://openmsi.nersc.gov), a platform for storing, sharing, and analyzing MSI data.more » By using a web-based python notebook (Jupyter), OMAAT is accessible to anyone without programming experience yet allows experienced users to leverage all features. OMAAT was evaluated by analyzing an MSI data set of a high-throughput glycoside hydrolase activity screen comprising 384 samples arrayed onto a NIMS surface at a 450 μm spacing, decreasing analysis time >100-fold while maintaining robust spot-finding. The utility of OMAAT was demonstrated for screening metabolic activities of different sized soil particles, including hydrolysis of sugars, revealing a pattern of size dependent activities. Finally, these results introduce OMAAT as an effective toolkit for analyzing spatially defined samples in MSI. OMAAT runs on all major operating systems, and the source code can be obtained from the following GitHub repository: https://github.com/biorack/omaat.« less

Coexistence of species with different dispersal across landscapes: a critical role of spatial correlation in disturbance.

PubMed

Liao, Jinbao; Ying, Zhixia; Woolnough, Daelyn A; Miller, Adam D; Li, Zhenqing; Nijs, Ivan

2016-05-11

Disturbance is key to maintaining species diversity in plant communities. Although the effects of disturbance frequency and extent on species diversity have been studied, we do not yet have a mechanistic understanding of how these aspects of disturbance interact with spatial structure of disturbance to influence species diversity. Here we derive a novel pair approximation model to explore competitive outcomes in a two-species system subject to spatially correlated disturbance. Generally, spatial correlation in disturbance favoured long-range dispersers, while distance-limited dispersers were greatly suppressed. Interestingly, high levels of spatial aggregation of disturbance promoted long-term species coexistence that is not possible in the absence of disturbance, but only when the local disperser was intrinsically competitively superior. However, spatial correlation in disturbance led to different competitive outcomes, depending on the disturbed area. Concerning ecological conservation and management, we theoretically demonstrate that introducing a spatially correlated disturbance to the system or altering an existing disturbance regime can be a useful strategy either to control species invasion or to promote species coexistence. Disturbance pattern analysis may therefore provide new insights into biodiversity conservation. © 2016 The Author(s).

TECA: A Parallel Toolkit for Extreme Climate Analysis

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

Prabhat, Mr; Ruebel, Oliver; Byna, Surendra

2012-03-12

We present TECA, a parallel toolkit for detecting extreme events in large climate datasets. Modern climate datasets expose parallelism across a number of dimensions: spatial locations, timesteps and ensemble members. We design TECA to exploit these modes of parallelism and demonstrate a prototype implementation for detecting and tracking three classes of extreme events: tropical cyclones, extra-tropical cyclones and atmospheric rivers. We process a modern TB-sized CAM5 simulation dataset with TECA, and demonstrate good runtime performance for the three case studies.

Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

NASA Astrophysics Data System (ADS)

Nikiforov, M. P.; Reukov, V. V.; Thompson, G. L.; Vertegel, A. A.; Guo, S.; Kalinin, S. V.; Jesse, S.

2009-10-01

Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

Chandra Interactive Analysis of Observations (CIAO)

NASA Technical Reports Server (NTRS)

Dobrzycki, Adam

2000-01-01

The Chandra (formerly AXAF) telescope, launched on July 23, 1999, provides X-rays data with unprecedented spatial and spectral resolution. As part of the Chandra scientific support, the Chandra X-ray Observatory Center provides a new data analysis system, CIAO ("Chandra Interactive Analysis of Observations"). We will present the main components of the system: "First Look" analysis; SHERPA: a multi-dimensional, multi-mission modeling and fitting application; Chandra Imaging and Plotting System; Detect package-source detection algorithms; and DM package generic data manipulation tools, We will set up a demonstration of the portable version of the system and show examples of Chandra Data Analysis.

Psychometric analysis of five measures of spatial ability.

PubMed

Hogan, Thomas P

2012-02-01

This study analyzed psychometric properties of five measures of spatial ability on 96 young adults, with supplementary analysis for three of the measures on another sample of 71 young adults. Two measures were taken from the widely cited Kit of Factor-Referenced Cognitive Tests and three other measures were taken from a relatively new source originally intended as laboratory demonstrations. Previous research provided limited information on the psychometric properties of the measures. All five measures yielded adequate reliability and loaded on a single factor. Three measures yielded markedly skewed distributions. Two measures showed clear sex differences with men scoring higher but this difference seemed contaminated by a speed factor; three measures did not show a sex difference. Recommendations for use of the measures in future studies are provided.

A fully-implicit high-order system thermal-hydraulics model for advanced non-LWR safety analyses

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

Hu, Rui

An advanced system analysis tool is being developed for advanced reactor safety analysis. This paper describes the underlying physics and numerical models used in the code, including the governing equations, the stabilization schemes, the high-order spatial and temporal discretization schemes, and the Jacobian Free Newton Krylov solution method. The effects of the spatial and temporal discretization schemes are investigated. Additionally, a series of verification test problems are presented to confirm the high-order schemes. Furthermore, it is demonstrated that the developed system thermal-hydraulics model can be strictly verified with the theoretical convergence rates, and that it performs very well for amore » wide range of flow problems with high accuracy, efficiency, and minimal numerical diffusions.« less

A fully-implicit high-order system thermal-hydraulics model for advanced non-LWR safety analyses

DOE PAGES

Hu, Rui

2016-11-19

An advanced system analysis tool is being developed for advanced reactor safety analysis. This paper describes the underlying physics and numerical models used in the code, including the governing equations, the stabilization schemes, the high-order spatial and temporal discretization schemes, and the Jacobian Free Newton Krylov solution method. The effects of the spatial and temporal discretization schemes are investigated. Additionally, a series of verification test problems are presented to confirm the high-order schemes. Furthermore, it is demonstrated that the developed system thermal-hydraulics model can be strictly verified with the theoretical convergence rates, and that it performs very well for amore » wide range of flow problems with high accuracy, efficiency, and minimal numerical diffusions.« less

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality

PubMed Central

Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; MacEachren, Alan M

2008-01-01

Background Kulldorff's spatial scan statistic and its software implementation – SaTScan – are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. Results We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. Conclusion The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales. Method We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit. PMID:18992163

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality.

PubMed

Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; Maceachren, Alan M

2008-11-07

Kulldorff's spatial scan statistic and its software implementation - SaTScan - are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales. We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit.

Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data

USGS Publications Warehouse

Li, Yan; Wagner, Tyler; Jiao, Yan; Lorantas, Robert M.; Murphy, Cheryl

2018-01-01

Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.

Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

NASA Astrophysics Data System (ADS)

Rasam, A. R. A.; Ghazali, R.; Noor, A. M. M.; Mohd, W. M. N. W.; Hamid, J. R. A.; Bazlan, M. J.; Ahmad, N.

2014-02-01

Cholera spatial epidemiology is the study of the spread and control of the disease spatial pattern and epidemics. Previous studies have shown that multi-factorial causation such as human behaviour, ecology and other infectious risk factors influence the disease outbreaks. Thus, understanding spatial pattern and possible interrelationship factors of the outbreaks are crucial to be explored an in-depth study. This study focuses on the integration of geographical information system (GIS) and epidemiological techniques in exploratory analyzing the cholera spatial pattern and distribution in the selected district of Sabah. Spatial Statistic and Pattern tools in ArcGIS and Microsoft Excel software were utilized to map and analyze the reported cholera cases and other data used. Meanwhile, cohort study in epidemiological technique was applied to investigate multiple outcomes of the disease exposure. The general spatial pattern of cholera was highly clustered showed the disease spread easily at a place or person to others especially 1500 meters from the infected person and locations. Although the cholera outbreaks in the districts are not critical, it could be endemic at the crowded areas, unhygienic environment, and close to contaminated water. It was also strongly believed that the coastal water of the study areas has possible relationship with the cholera transmission and phytoplankton bloom since the areas recorded higher cases. GIS demonstrates a vital spatial epidemiological technique in determining the distribution pattern and elucidating the hypotheses generating of the disease. The next research would be applying some advanced geo-analysis methods and other disease risk factors for producing a significant a local scale predictive risk model of the disease in Malaysia.

Spatial arrangement of faults and opening-mode fractures

NASA Astrophysics Data System (ADS)

Laubach, S. E.; Lamarche, J.; Gauthier, B. D. M.; Dunne, W. M.; Sanderson, David J.

2018-03-01

Spatial arrangement is a fundamental characteristic of fracture arrays. The pattern of fault and opening-mode fracture positions in space defines structural heterogeneity and anisotropy in a rock volume, governs how faults and fractures affect fluid flow, and impacts our understanding of the initiation, propagation and interactions during the formation of fracture patterns. This special issue highlights recent progress with respect to characterizing and understanding the spatial arrangements of fault and fracture patterns, providing examples over a wide range of scales and structural settings. Five papers describe new methods and improvements of existing techniques to quantify spatial arrangement. One study unravels the time evolution of opening-mode fracture spatial arrangement, which are data needed to compare natural patterns with progressive fracture growth in kinematic and mechanical models. Three papers investigate the role of evolving diagenesis in localizing fractures by mechanical stratigraphy and nine discuss opening-mode fracture spatial arrangement. Two papers show the relevance of complex cluster patterns to unconventional reservoirs through examples of fractures in tight gas sandstone horizontal wells, and a study of fracture arrangement in shale. Four papers demonstrate the roles of folds in fracture localization and the development spatial patterns. One paper models along-fault friction and fluid pressure and their effects on fault-related fracture arrangement. Contributions address deformation band patterns in carbonate rocks and fault size and arrangement above a detachment fault. Three papers describe fault and fracture arrangements in basement terrains, and three document fracture patterns in shale. This collection of papers points toward improvement in field methods, continuing improvements in computer-based data analysis and creation of synthetic fracture patterns, and opportunities for further understanding fault and fracture attributes in the subsurface through coupled spatial, size, and pattern analysis.

Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics

USGS Publications Warehouse

Nichols, J.M.; Moniz, L.; Nichols, J.D.; Pecora, L.M.; Cooch, E.

2005-01-01

A number of important questions in ecology involve the possibility of interactions or ?coupling? among potential components of ecological systems. The basic question of whether two components are coupled (exhibit dynamical interdependence) is relevant to investigations of movement of animals over space, population regulation, food webs and trophic interactions, and is also useful in the design of monitoring programs. For example, in spatially extended systems, coupling among populations in different locations implies the existence of redundant information in the system and the possibility of exploiting this redundancy in the development of spatial sampling designs. One approach to the identification of coupling involves study of the purported mechanisms linking system components. Another approach is based on time series of two potential components of the same system and, in previous ecological work, has relied on linear cross-correlation analysis. Here we present two different attractor-based approaches, continuity and mutual prediction, for determining the degree to which two population time series (e.g., at different spatial locations) are coupled. Both approaches are demonstrated on a one-dimensional predator?prey model system exhibiting complex dynamics. Of particular interest is the spatial asymmetry introduced into the model as linearly declining resource for the prey over the domain of the spatial coordinate. Results from these approaches are then compared to the more standard cross-correlation analysis. In contrast to cross-correlation, both continuity and mutual prediction are clearly able to discern the asymmetry in the flow of information through this system.

Use of Activity Space in a Tuberculosis Outbreak: Bringing Homeless Persons Into Spatial Analyses.

PubMed

Worrell, Mary Claire; Kramer, Michael; Yamin, Aliya; Ray, Susan M; Goswami, Neela D

2017-01-01

Tuberculosis (TB) causes significant morbidity and mortality in US cities, particularly in poor, transient populations. During a TB outbreak in Fulton County, Atlanta, GA, we aimed to determine whether local maps created from multiple locations of personal activity per case would differ significantly from traditional maps created from single residential address. Data were abstracted for patients with TB disease diagnosed in 2008-2014 and receiving care at the Fulton County Health Department. Clinical and activity location data were abstracted from charts. Kernel density methods, activity space analysis, and overlay with homeless shelter locations were used to characterize case spatial distribution when using single versus multiple addresses. Data were collected for 198 TB cases, with over 30% homeless US-born cases included. Greater spatial dispersion of cases was found when utilizing multiple versus single addresses per case. Activity spaces of homeless and isoniazid (INH)-resistant cases were more spatially congruent with one another than non-homeless and INH-susceptible cases ( P < .0001 and P < .0001, respectively). Innovative spatial methods allowed us to more comprehensively capture the geography of TB-infected homeless persons, who made up a large portion of the Fulton County outbreak. We demonstrate how activity space analysis, prominent in exposure science and chronic disease, supports that routine capture of multiple location TB data may facilitate spatially different public health interventions than traditional surveillance maps. © The Author 2017. Published by Oxford University Press on behalf of Infectious Diseases Society of America.

Uniformity analysis for a direct-drive laser fusion reactor

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

Lund, L.D.; Skupsky, S.; Goldman, L.M.

1983-01-01

We show the results of an analysis of the uniformity for a direct-drive reactor using 20, 32, 60, or 96 beams. Several of these options achieve less than the 1% nonuniformity that is required. These options are considered for the cases where the solid angle fraction of the beam ports is 2% and 8%. The analysis is facilitated by separating the contributions due to the geometrical effects related to the number and orientation of the beams from those due to the spatial profile of the individual beams. Emphasis is placed on the wavelength of the nonuniformities, as the shorter wavelengthmore » nonuniformities are more easily smoothed by thermal conduction within the target. The analysis demonstrates that the longer wavelengths can be minimized by suitable choices of geometry and by maintaining beam balance, whereas the shorter wavelength nonuniformities can be reduced by optimizing parameters such as the focal position and the spatial intensity profile of each beam. The tolerances required for beam-to-beam energy balance will be discussed.« less

Unsupervised seismic facies analysis with spatial constraints using regularized fuzzy c-means

NASA Astrophysics Data System (ADS)

Song, Chengyun; Liu, Zhining; Cai, Hanpeng; Wang, Yaojun; Li, Xingming; Hu, Guangmin

2017-12-01

Seismic facies analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic facies analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear facies boundaries and channel morphology, which reveals that the method is an effective seismic facies analysis tool.

Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps

PubMed Central

Goovaerts, Pierre

2006-01-01

Boundary analysis of cancer maps may highlight areas where causative exposures change through geographic space, the presence of local populations with distinct cancer incidences, or the impact of different cancer control methods. Too often, such analysis ignores the spatial pattern of incidence or mortality rates and overlooks the fact that rates computed from sparsely populated geographic entities can be very unreliable. This paper proposes a new methodology that accounts for the uncertainty and spatial correlation of rate data in the detection of significant edges between adjacent entities or polygons. Poisson kriging is first used to estimate the risk value and the associated standard error within each polygon, accounting for the population size and the risk semivariogram computed from raw rates. The boundary statistic is then defined as half the absolute difference between kriged risks. Its reference distribution, under the null hypothesis of no boundary, is derived through the generation of multiple realizations of the spatial distribution of cancer risk values. This paper presents three types of neutral models generated using methods of increasing complexity: the common random shuffle of estimated risk values, a spatial re-ordering of these risks, or p-field simulation that accounts for the population size within each polygon. The approach is illustrated using age-adjusted pancreatic cancer mortality rates for white females in 295 US counties of the Northeast (1970–1994). Simulation studies demonstrate that Poisson kriging yields more accurate estimates of the cancer risk and how its value changes between polygons (i.e. boundary statistic), relatively to the use of raw rates or local empirical Bayes smoother. When used in conjunction with spatial neutral models generated by p-field simulation, the boundary analysis based on Poisson kriging estimates minimizes the proportion of type I errors (i.e. edges wrongly declared significant) while the frequency of these errors is predicted well by the p-value of the statistical test. PMID:19023455

Advanced analysis of forest fire clustering

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Pereira, Mario; Golay, Jean

2017-04-01

Analysis of point pattern clustering is an important topic in spatial statistics and for many applications: biodiversity, epidemiology, natural hazards, geomarketing, etc. There are several fundamental approaches used to quantify spatial data clustering using topological, statistical and fractal measures. In the present research, the recently introduced multi-point Morisita index (mMI) is applied to study the spatial clustering of forest fires in Portugal. The data set consists of more than 30000 fire events covering the time period from 1975 to 2013. The distribution of forest fires is very complex and highly variable in space. mMI is a multi-point extension of the classical two-point Morisita index. In essence, mMI is estimated by covering the region under study by a grid and by computing how many times more likely it is that m points selected at random will be from the same grid cell than it would be in the case of a complete random Poisson process. By changing the number of grid cells (size of the grid cells), mMI characterizes the scaling properties of spatial clustering. From mMI, the data intrinsic dimension (fractal dimension) of the point distribution can be estimated as well. In this study, the mMI of forest fires is compared with the mMI of random patterns (RPs) generated within the validity domain defined as the forest area of Portugal. It turns out that the forest fires are highly clustered inside the validity domain in comparison with the RPs. Moreover, they demonstrate different scaling properties at different spatial scales. The results obtained from the mMI analysis are also compared with those of fractal measures of clustering - box counting and sand box counting approaches. REFERENCES Golay J., Kanevski M., Vega Orozco C., Leuenberger M., 2014: The multipoint Morisita index for the analysis of spatial patterns. Physica A, 406, 191-202. Golay J., Kanevski M. 2015: A new estimator of intrinsic dimension based on the multipoint Morisita index. Pattern Recognition, 48, 4070-4081.

Study the effects of varying interference upon the optical properties of turbid samples using NIR spatial light modulation

NASA Astrophysics Data System (ADS)

Shaul, Oren; Fanrazi-Kahana, Michal; Meitav, Omri; Pinhasi, Gad A.; Abookasis, David

2018-03-01

Optical properties of biological tissues are valuable diagnostic parameters which can provide necessary information regarding tissue state during disease pathogenesis and therapy. However, different sources of interference, such as temperature changes may modify these properties, introducing confounding factors and artifacts to data, consequently skewing their interpretation and misinforming clinical decision-making. In the current study, we apply spatial light modulation, a type of diffuse reflectance hyperspectral imaging technique, to monitor the variation in optical properties of highly scattering turbid media in the presence varying levels of the following sources of interference: scattering concentration, temperature, and pressure. Spatial near-infrared (NIR) light modulation is a wide-field, non-contact emerging optical imaging platform capable of separating the effects of tissue scattering from those of absorption, thereby accurately estimating both parameters. With this technique, periodic NIR illumination patterns at alternately low and high spatial frequencies, at six discrete wavelengths between 690 to 970 nm, were sequentially projected upon the medium while a CCD camera collects the diffusely reflected light. Data analysis based assumptions is then performed off-line to recover the medium's optical properties. We conducted a series of experiments demonstrating the changes in absorption and reduced scattering coefficients of commercially available fresh milk and chicken breast tissue under different interference conditions. In addition, information on the refractive index was study under increased pressure. This work demonstrates the utility of NIR spatial light modulation to detect varying sources of interference upon the optical properties of biological samples.

Subtypes of breast cancer show different spatial distributions of brain metastases.

PubMed

Kyeong, Sunghyon; Cha, Yoon Jin; Ahn, Sung Gwe; Suh, Sang Hyun; Son, Eun Ju; Ahn, Sung Jun

2017-01-01

The aim of our study was to test the hypothesis that the spatial distribution of breast cancer brain metastases (BM) differ according to their biological subtypes. MR images of 100 patients with BM from primary breast cancer were retrospectively reviewed. Patients were divided according to the biological subtype of the primary tumor, (triple-negative: 24, HER2 positive: 48, luminal: 28). All images marked with BMs were standardized to the human brain MRI atlas provided by the Montreal Neurological Institute 152 database. Distribution pattern of BM was evaluated with intra-group and intergroup analysis. In intra-group analysis, hot spots of metastases from triple-negative are evenly distributed in the brain, meanwhile BMs from HER2 positive and luminal type occur dominantly in occipital lobe and cerebellum. In intergroup analysis, BMs from triple-negative type occurred more often in frontal lobe, limbic region, and parietal lobe, compared with other types (P < .05). Breast cancer subtypes tend to demonstrate different spatial distributions of their BMs. These findings may have direct implications for dose modulation in prophylactic irradiation as well as for differential diagnoses. Thus, this result should be validated in future study with a larger population.

Visualizing driving forces of spatially extended systems using the recurrence plot framework

NASA Astrophysics Data System (ADS)

Riedl, Maik; Marwan, Norbert; Kurths, Jürgen

2017-12-01

The increasing availability of highly resolved spatio-temporal data leads to new opportunities as well as challenges in many scientific disciplines such as climatology, ecology or epidemiology. This allows more detailed insights into the investigated spatially extended systems. However, this development needs advanced techniques of data analysis which go beyond standard linear tools since the more precise consideration often reveals nonlinear phenomena, for example threshold effects. One of these tools is the recurrence plot approach which has been successfully applied to the description of complex systems. Using this technique's power of visualization, we propose the analysis of the local minima of the underlying distance matrix in order to display driving forces of spatially extended systems. The potential of this novel idea is demonstrated by the analysis of the chlorophyll concentration and the sea surface temperature in the Southern California Bight. We are able not only to confirm the influence of El Niño events on the phytoplankton growth in this region but also to confirm two discussed regime shifts in the California current system. This new finding underlines the power of the proposed approach and promises new insights into other complex systems.

Multivariate analysis applied to the study of spatial distributions found in drug-eluting stent coatings by confocal Raman microscopy.

PubMed

Balss, Karin M; Long, Frederick H; Veselov, Vladimir; Orana, Argjenta; Akerman-Revis, Eugena; Papandreou, George; Maryanoff, Cynthia A

2008-07-01

Multivariate data analysis was applied to confocal Raman measurements on stents coated with the polymers and drug used in the CYPHER Sirolimus-eluting Coronary Stents. Partial least-squares (PLS) regression was used to establish three independent calibration curves for the coating constituents: sirolimus, poly(n-butyl methacrylate) [PBMA], and poly(ethylene-co-vinyl acetate) [PEVA]. The PLS calibrations were based on average spectra generated from each spatial location profiled. The PLS models were tested on six unknown stent samples to assess accuracy and precision. The wt % difference between PLS predictions and laboratory assay values for sirolimus was less than 1 wt % for the composite of the six unknowns, while the polymer models were estimated to be less than 0.5 wt % difference for the combined samples. The linearity and specificity of the three PLS models were also demonstrated with the three PLS models. In contrast to earlier univariate models, the PLS models achieved mass balance with better accuracy. This analysis was extended to evaluate the spatial distribution of the three constituents. Quantitative bitmap images of drug-eluting stent coatings are presented for the first time to assess the local distribution of components.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

NASA Astrophysics Data System (ADS)

Novosad, Philip; Reader, Andrew J.

2016-06-01

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [11C]SCH23390 data, showing promising results.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions.

PubMed

Novosad, Philip; Reader, Andrew J

2016-06-21

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [(18)F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [(11)C]SCH23390 data, showing promising results.

Effect of Spatial Resolution for Characterizing Soil Properties from Imaging Spectrometer Data

NASA Astrophysics Data System (ADS)

Dutta, D.; Kumar, P.; Greenberg, J. A.

2015-12-01

The feasibility of quantifying soil constituents over large areas using airborne hyperspectral data [0.35 - 2.5 μm] in an ensemble bootstrapping lasso algorithmic framework has been demonstrated previously [1]. However the effects of coarsening the spatial resolution of hyperspectral data on the quantification of soil constituents are unknown. We use Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data collected at 7.6m resolution over Birds Point New Madrid (BPNM) floodway for up-scaling and generating multiple coarser resolution datasets including the 60m Hyperspectral Infrared Imager (HyspIRI) like data. HyspIRI is a proposed visible shortwave/thermal infrared mission, which will provide global data over a spectral range of 0.35 - 2.5μm at a spatial resolution of 60m. Our results show that the lasso method, which is based on point scale observational data, is scalable. We found consistent good model performance (R2) values (0.79 < R2 < 0.82) and correct classifications as per USDA soil texture classes at multiple spatial resolutions. The results further demonstrate that the attributes of the pdf for different soil constituents across the landscape and the within-pixel variance are well preserved across scales. Our analysis provides a methodological framework with a sufficient set of metrics for assessing the performance of scaling up analysis from point scale observational data and may be relevant for other similar remote sensing studies. [1] Dutta, D.; Goodwell, A.E.; Kumar, P.; Garvey, J.E.; Darmody, R.G.; Berretta, D.P.; Greenberg, J.A., "On the Feasibility of Characterizing Soil Properties From AVIRIS Data," Geoscience and Remote Sensing, IEEE Transactions on, vol.53, no.9, pp.5133,5147, Sept. 2015. doi: 10.1109/TGRS.2015.2417547.

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.

PubMed

Engemann, Denis A; Gramfort, Alexandre

2015-03-01

Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals. Copyright © 2015 Elsevier Inc. All rights reserved.

Locally distributed ground deformation in an area of potential phreatic eruption, Midagahara volcano, Japan, detected by single-look-based InSAR time series analysis

NASA Astrophysics Data System (ADS)

Kobayashi, Tomokazu

2018-05-01

Although it is difficult to monitor the spatial extent and temporal evolution of local and small-magnitude ground inflation, this information is vital to assess the potential for phreatic eruption. Herein, we demonstrate the detection of locally distributed ground deformation preceding the enhancement of geothermal activity in the Midagahara volcano, Japan, through the application of single-look-based interferometric synthetic aperture radar analysis. In the Jigoku-dani geothermal area, the ground deformation proceeded at a low speed of 4 cm/year at most with a spatial extent of 500 m in the east-west direction and 250 m in the north-south direction. The deformation can be recognized to progress from 2007, at the latest, to 2010, after which the geothermal activity increased, with the collapse of sulfur towers and the appearance of active fumaroles and boiling water on the ground surface. The most deformed area corresponds to the geothermal area with the highest activity observed on the ground surface. Assuming a sill opening model, the deformation source is estimated to be located at a depth of 50 m from the surface with a speed of 7 cm/year at most, which is consistent with the depth of the highly conductive medium inferred from magnetotelluric analyses. This may suggest that volcanic fluid and/or heat was injected into the fluid-rich medium from depth and caused the ground inflation. Our results demonstrate that high-spatial-resolution deformation data can be an effective tool to monitor subsurface pressure conditions with pinpoint spatial accuracy during the build-up to phreatic eruptions.

Teaching Spatial Thinking in Undergraduate Geology Courses Using Tools and Strategies from Cognitive Science Research

NASA Astrophysics Data System (ADS)

Ormand, C. J.; Shipley, T. F.; Dutrow, B. L.; Goodwin, L. B.; Hickson, T. A.; Tikoff, B.; Atit, K.; Gagnier, K. M.; Resnick, I.

2015-12-01

Spatial visualization is an essential skill in the STEM disciplines, including the geological sciences. Undergraduate students, including geoscience majors in upper-level courses, bring a wide range of spatial skill levels to the classroom. Students with weak spatial skills may struggle to understand fundamental concepts and to solve geological problems with a spatial component. However, spatial thinking skills are malleable. Using strategies that have emerged from cognitive science research, we developed a set of curricular materials that improve undergraduate geology majors' abilities to reason about 3D concepts and to solve spatially complex geological problems. Cognitive science research on spatial thinking demonstrates that predictive sketching, making visual comparisons, gesturing, and the use of analogy can be used to develop students' spatial thinking skills. We conducted a three-year study of the efficacy of these strategies in strengthening the spatial skills of students in core geology courses at three universities. Our methodology is a quasi-experimental quantitative design, utilizing pre- and post-tests of spatial thinking skills, assessments of spatial problem-solving skills, and a control group comprised of students not exposed to our new curricular materials. Students taught using the new curricular materials show improvement in spatial thinking skills. Further analysis of our data, to be completed prior to AGU, will answer additional questions about the relationship between spatial skills and academic performance, spatial skills and gender, spatial skills and confidence, and the impact of our curricular materials on students who are struggling academically. Teaching spatial thinking in the context of discipline-based exercises has the potential to transform undergraduate education in the geological sciences by removing one significant barrier to success.

A compartmental-spatial system dynamics approach to ground water modeling.

PubMed

Roach, Jesse; Tidwell, Vince

2009-01-01

High-resolution, spatially distributed ground water flow models can prove unsuitable for the rapid, interactive analysis that is increasingly demanded to support a participatory decision environment. To address this shortcoming, we extend the idea of multiple cell (Bear 1979) and compartmental (Campana and Simpson 1984) ground water models developed within the context of spatial system dynamics (Ahmad and Simonovic 2004) for rapid scenario analysis. We term this approach compartmental-spatial system dynamics (CSSD). The goal is to balance spatial aggregation necessary to achieve a real-time integrative and interactive decision environment while maintaining sufficient model complexity to yield a meaningful representation of the regional ground water system. As a test case, a 51-compartment CSSD model was built and calibrated from a 100,0001 cell MODFLOW (McDonald and Harbaugh 1988) model of the Albuquerque Basin in central New Mexico (McAda and Barroll 2002). Seventy-seven percent of historical drawdowns predicted by the MODFLOW model were within 1 m of the corresponding CSSD estimates, and in 80% of the historical model run years the CSSD model estimates of river leakage, reservoir leakage, ground water flow to agricultural drains, and riparian evapotranspiration were within 30% of the corresponding estimates from McAda and Barroll (2002), with improved model agreement during the scenario period. Comparisons of model results demonstrate both advantages and limitations of the CCSD model approach.

A new spatial multi-criteria decision support tool for site selection for implementation of managed aquifer recharge.

PubMed

Rahman, M Azizur; Rusteberg, Bernd; Gogu, R C; Lobo Ferreira, J P; Sauter, Martin

2012-05-30

This study reports the development of a new spatial multi-criteria decision analysis (SMCDA) software tool for selecting suitable sites for Managed Aquifer Recharge (MAR) systems. The new SMCDA software tool functions based on the combination of existing multi-criteria evaluation methods with modern decision analysis techniques. More specifically, non-compensatory screening, criteria standardization and weighting, and Analytical Hierarchy Process (AHP) have been combined with Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA). This SMCDA tool may be implemented with a wide range of decision maker's preferences. The tool's user-friendly interface helps guide the decision maker through the sequential steps for site selection, those steps namely being constraint mapping, criteria hierarchy, criteria standardization and weighting, and criteria overlay. The tool offers some predetermined default criteria and standard methods to increase the trade-off between ease-of-use and efficiency. Integrated into ArcGIS, the tool has the advantage of using GIS tools for spatial analysis, and herein data may be processed and displayed. The tool is non-site specific, adaptive, and comprehensive, and may be applied to any type of site-selection problem. For demonstrating the robustness of the new tool, a case study was planned and executed at Algarve Region, Portugal. The efficiency of the SMCDA tool in the decision making process for selecting suitable sites for MAR was also demonstrated. Specific aspects of the tool such as built-in default criteria, explicit decision steps, and flexibility in choosing different options were key features, which benefited the study. The new SMCDA tool can be augmented by groundwater flow and transport modeling so as to achieve a more comprehensive approach to the selection process for the best locations of the MAR infiltration basins, as well as the locations of recovery wells and areas of groundwater protection. The new spatial multicriteria analysis tool has already been implemented within the GIS based Gabardine decision support system as an innovative MAR planning tool. Copyright © 2012 Elsevier Ltd. All rights reserved.

Thermal Analysis by Structural Characterization as a Method for Assessing Heterogeneity in Complex Solid Pharmaceutical Dosage Forms.

PubMed

Alhijjaj, Muqdad; Reading, Mike; Belton, Peter; Qi, Sheng

2015-11-03

Characterizing inter- and intrasample heterogeneity of solid and semisolid pharmaceutical products is important both for rational design of dosage forms and subsequent quality control during manufacture; however, most pharmaceutical products are multicomponent formulations that are challenging in this regard. Thermal analysis, in particular differential scanning calorimetry, is commonly used to obtain structural information, such as degree of crystallinity, or identify the presence of a particular polymorph, but the results are an average over the whole sample; it cannot directly provide information about the spatial distribution of phases. This study demonstrates the use of a new thermo-optical technique, thermal analysis by structural characterization (TASC), that can provide spatially resolved information on thermal transitions by applying a novel algorithm to images acquired by hot stage microscopy. We determined that TASC can be a low cost, relatively rapid method of characterizing heterogeneity and other aspects of structure. In the examples studied, it was found that high heating rates enabled screening times of 3-5 min per sample. In addition, this study demonstrated the higher sensitivity of TASC for detecting the metastable form of polyethylene glycol (PEG) compared to conventional differential scanning calorimetry (DSC). This preliminary work suggests that TASC will be a worthwhile additional tool for characterizing a broad range of materials.

A Functional Model of the Digital Extensor Mechanism: Demonstrating Biomechanics with Hair Bands

ERIC Educational Resources Information Center

Cloud, Beth A.; Youdas, James W.; Hellyer, Nathan J.; Krause, David A.

2010-01-01

The action of muscles about joints can be explained through analysis of their spatial relationship. A functional model of these relationships can be valuable in learning and understanding the muscular action about a joint. A model can be particularly helpful when examining complex actions across multiple joints such as in the digital extensor…

Stochastic optical reconstruction microscopy-based relative localization analysis (STORM-RLA) for quantitative nanoscale assessment of spatial protein organization.

PubMed

Veeraraghavan, Rengasayee; Gourdie, Robert G

2016-11-07

The spatial association between proteins is crucial to understanding how they function in biological systems. Colocalization analysis of fluorescence microscopy images is widely used to assess this. However, colocalization analysis performed on two-dimensional images with diffraction-limited resolution merely indicates that the proteins are within 200-300 nm of each other in the xy-plane and within 500-700 nm of each other along the z-axis. Here we demonstrate a novel three-dimensional quantitative analysis applicable to single-molecule positional data: stochastic optical reconstruction microscopy-based relative localization analysis (STORM-RLA). This method offers significant advantages: 1) STORM imaging affords 20-nm resolution in the xy-plane and <50 nm along the z-axis; 2) STORM-RLA provides a quantitative assessment of the frequency and degree of overlap between clusters of colabeled proteins; and 3) STORM-RLA also calculates the precise distances between both overlapping and nonoverlapping clusters in three dimensions. Thus STORM-RLA represents a significant advance in the high-throughput quantitative assessment of the spatial organization of proteins. © 2016 Veeraraghavan and Gourdie. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Validation of Vehicle Panel/Equipment Response from Diffuse Acoustic Field Excitation Using Spatially Correlated Transfer Function Approach

NASA Technical Reports Server (NTRS)

Smith, Andrew; LaVerde, Bruce; Fulcher, Clay; Hunt, Ron

2012-01-01

An approach for predicting the vibration, strain, and force responses of a flight-like vehicle panel assembly to acoustic pressures is presented. Important validation for the approach is provided by comparison to ground test measurements in a reverberant chamber. The test article and the corresponding analytical model were assembled in several configurations to demonstrate the suitability of the approach for response predictions when the vehicle panel is integrated with equipment. Critical choices in the analysis necessary for convergence of the predicted and measured responses are illustrated through sensitivity studies. The methodology includes representation of spatial correlation of the pressure field over the panel surface. Therefore, it is possible to demonstrate the effects of hydrodynamic coincidence in the response. The sensitivity to pressure patch density clearly illustrates the onset of coincidence effects on the panel response predictions.

Scalable parallel distance field construction for large-scale applications

DOE PAGES

Yu, Hongfeng; Xie, Jinrong; Ma, Kwan -Liu; ...

2015-10-01

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. Anew distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking overtime, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate itsmore » efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. In conclusion, our work greatly extends the usability of distance fields for demanding applications.« less

Scalable Parallel Distance Field Construction for Large-Scale Applications.

PubMed

Yu, Hongfeng; Xie, Jinrong; Ma, Kwan-Liu; Kolla, Hemanth; Chen, Jacqueline H

2015-10-01

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. A new distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking over time, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. Our work greatly extends the usability of distance fields for demanding applications.

Solving Large-scale Spatial Optimization Problems in Water Resources Management through Spatial Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Wang, J.; Cai, X.

2007-12-01

A water resources system can be defined as a large-scale spatial system, within which distributed ecological system interacts with the stream network and ground water system. Water resources management, the causative factors and hence the solutions to be developed have a significant spatial dimension. This motivates a modeling analysis of water resources management within a spatial analytical framework, where data is usually geo- referenced and in the form of a map. One of the important functions of Geographic information systems (GIS) is to identify spatial patterns of environmental variables. The role of spatial patterns in water resources management has been well established in the literature particularly regarding how to design better spatial patterns for satisfying the designated objectives of water resources management. Evolutionary algorithms (EA) have been demonstrated to be successful in solving complex optimization models for water resources management due to its flexibility to incorporate complex simulation models in the optimal search procedure. The idea of combining GIS and EA motivates the development and application of spatial evolutionary algorithms (SEA). SEA assimilates spatial information into EA, and even changes the representation and operators of EA. In an EA used for water resources management, the mathematical optimization model should be modified to account the spatial patterns; however, spatial patterns are usually implicit, and it is difficult to impose appropriate patterns to spatial data. Also it is difficult to express complex spatial patterns by explicit constraints included in the EA. The GIS can help identify the spatial linkages and correlations based on the spatial knowledge of the problem. These linkages are incorporated in the fitness function for the preference of the compatible vegetation distribution. Unlike a regular GA for spatial models, the SEA employs a special hierarchical hyper-population and spatial genetic operators to represent spatial variables in a more efficient way. The hyper-population consists of a set of populations, which correspond to the spatial distributions of the individual agents (organisms). Furthermore spatial crossover and mutation operators are designed in accordance with the tree representation and then applied to both organisms and populations. This study applies the SEA to a specific problem of water resources management- maximizing the riparian vegetation coverage in accordance with the distributed groundwater system in an arid region. The vegetation coverage is impacted greatly by the nonlinear feedbacks and interactions between vegetation and groundwater and the spatial variability of groundwater. The SEA is applied to search for an optimal vegetation configuration compatible to the groundwater flow. The results from this example demonstrate the effectiveness of the SEA. Extension of the algorithm for other water resources management problems is discussed.

Spatial data analytics on heterogeneous multi- and many-core parallel architectures using python

USGS Publications Warehouse

Laura, Jason R.; Rey, Sergio J.

2017-01-01

Parallel vector spatial analysis concerns the application of parallel computational methods to facilitate vector-based spatial analysis. The history of parallel computation in spatial analysis is reviewed, and this work is placed into the broader context of high-performance computing (HPC) and parallelization research. The rise of cyber infrastructure and its manifestation in spatial analysis as CyberGIScience is seen as a main driver of renewed interest in parallel computation in the spatial sciences. Key problems in spatial analysis that have been the focus of parallel computing are covered. Chief among these are spatial optimization problems, computational geometric problems including polygonization and spatial contiguity detection, the use of Monte Carlo Markov chain simulation in spatial statistics, and parallel implementations of spatial econometric methods. Future directions for research on parallelization in computational spatial analysis are outlined.

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition.

PubMed

Gudde, Harmen B; Griffiths, Debra; Coventry, Kenny R

2018-02-19

The memory game paradigm is a behavioral procedure to explore the relationship between language, spatial memory, and object knowledge. Using two different versions of the paradigm, spatial language use and memory for object location are tested under different, experimentally manipulated conditions. This allows us to tease apart proposed models explaining the influence of object knowledge on spatial language (e.g., spatial demonstratives), and spatial memory, as well as understanding the parameters that affect demonstrative choice and spatial memory more broadly. Key to the development of the method was the need to collect data on language use (e.g., spatial demonstratives: "this/that") and spatial memory data under strictly controlled conditions, while retaining a degree of ecological validity. The language version (section 3.1) of the memory game tests how conditions affect language use. Participants refer verbally to objects placed at different locations (e.g., using spatial demonstratives: "this/that red circle"). Different parameters can be experimentally manipulated: the distance from the participant, the position of a conspecific, and for example whether the participant owns, knows, or sees the object while referring to it. The same parameters can be manipulated in the memory version of the memory game (section 3.2). This version tests the effects of the different conditions on object-location memory. Following object placement, participants get 10 seconds to memorize the object's location. After the object and location cues are removed, participants verbally direct the experimenter to move a stick to indicate where the object was. The difference between the memorized and the actual location shows the direction and strength of the memory error, allowing comparisons between the influences of the respective parameters.

Spatial analyses for nonoverlapping objects with size variations and their application to coral communities.

PubMed

Muko, Soyoka; Shimatani, Ichiro K; Nozawa, Yoko

2014-07-01

Spatial distributions of individuals are conventionally analysed by representing objects as dimensionless points, in which spatial statistics are based on centre-to-centre distances. However, if organisms expand without overlapping and show size variations, such as is the case for encrusting corals, interobject spacing is crucial for spatial associations where interactions occur. We introduced new pairwise statistics using minimum distances between objects and demonstrated their utility when examining encrusting coral community data. We also calculated the conventional point process statistics and the grid-based statistics to clarify the advantages and limitations of each spatial statistical method. For simplicity, coral colonies were approximated by disks in these demonstrations. Focusing on short-distance effects, the use of minimum distances revealed that almost all coral genera were aggregated at a scale of 1-25 cm. However, when fragmented colonies (ramets) were treated as a genet, a genet-level analysis indicated weak or no aggregation, suggesting that most corals were randomly distributed and that fragmentation was the primary cause of colony aggregations. In contrast, point process statistics showed larger aggregation scales, presumably because centre-to-centre distances included both intercolony spacing and colony sizes (radius). The grid-based statistics were able to quantify the patch (aggregation) scale of colonies, but the scale was strongly affected by the colony size. Our approach quantitatively showed repulsive effects between an aggressive genus and a competitively weak genus, while the grid-based statistics (covariance function) also showed repulsion although the spatial scale indicated from the statistics was not directly interpretable in terms of ecological meaning. The use of minimum distances together with previously proposed spatial statistics helped us to extend our understanding of the spatial patterns of nonoverlapping objects that vary in size and the associated specific scales. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

The neural bases of spatial frequency processing during scene perception

PubMed Central

Kauffmann, Louise; Ramanoël, Stephen; Peyrin, Carole

2014-01-01

Theories on visual perception agree that scenes are processed in terms of spatial frequencies. Low spatial frequencies (LSF) carry coarse information whereas high spatial frequencies (HSF) carry fine details of the scene. However, how and where spatial frequencies are processed within the brain remain unresolved questions. The present review addresses these issues and aims to identify the cerebral regions differentially involved in low and high spatial frequency processing, and to clarify their attributes during scene perception. Results from a number of behavioral and neuroimaging studies suggest that spatial frequency processing is lateralized in both hemispheres, with the right and left hemispheres predominantly involved in the categorization of LSF and HSF scenes, respectively. There is also evidence that spatial frequency processing is retinotopically mapped in the visual cortex. HSF scenes (as opposed to LSF) activate occipital areas in relation to foveal representations, while categorization of LSF scenes (as opposed to HSF) activates occipital areas in relation to more peripheral representations. Concomitantly, a number of studies have demonstrated that LSF information may reach high-order areas rapidly, allowing an initial coarse parsing of the visual scene, which could then be sent back through feedback into the occipito-temporal cortex to guide finer HSF-based analysis. Finally, the review addresses spatial frequency processing within scene-selective regions areas of the occipito-temporal cortex. PMID:24847226

Development of portable defocusing micro-scale spatially offset Raman spectroscopy.

PubMed

Realini, Marco; Botteon, Alessandra; Conti, Claudia; Colombo, Chiara; Matousek, Pavel

2016-05-10

We present, for the first time, portable defocusing micro-Spatially Offset Raman Spectroscopy (micro-SORS). Micro-SORS is a concept permitting the analysis of thin, highly turbid stratified layers beyond the reach of conventional Raman microscopy. The technique is applicable to the analysis of painted layers in cultural heritage (panels, canvases and mural paintings, painted statues and decorated objects in general) as well as in many other areas including polymer, biological and biomedical applications, catalytic and forensics sciences where highly turbid stratified layers are present and where invasive analysis is undesirable or impossible. So far the technique has been demonstrated only on benchtop Raman microscopes precluding the non-invasive analysis of larger samples and samples in situ. The new set-up is characterised conceptually on a range of artificially assembled two-layer systems demonstrating its benefits and performance across several application areas. These included stratified polymer sample, pharmaceutical tablet and layered paint samples. The same samples were also analysed by a high performance (non-portable) benchtop Raman microscope to provide benchmarking against our earlier research. The realisation of the vision of delivering portability to micro-SORS has a transformative potential spanning across multiple disciplines as it fully unlocks, for the first time, the non-invasive and non-destructive aspects of micro-SORS enabling it to be applied also to large and non-portable samples in situ without recourse to removing samples, or their fragments, for laboratory analysis on benchtop Raman microscopes.

Spatiotemporal analysis of particulate air pollution and ischemic heart disease mortality in Beijing, China.

PubMed

Xu, Meimei; Guo, Yuming; Zhang, Yajuan; Westerdahl, Dane; Mo, Yunzheng; Liang, Fengchao; Pan, Xiaochuan

2014-12-12

Few studies have used spatially resolved ambient particulate matter with an aerodynamic diameter of <10 μm (PM10) to examine the impact of PM10 on ischemic heart disease (IHD) mortality in China. The aim of our study is to evaluate the short-term effects of PM10 concentrations on IHD mortality by means of spatiotemporal analysis approach. We collected daily data on air pollution, weather conditions and IHD mortality in Beijing, China during 2008 and 2009. Ordinary kriging (OK) was used to interpolate daily PM10 concentrations at the centroid of 287 township-level areas based on 27 monitoring sites covering the whole city. A generalized additive mixed model was used to estimate quantitatively the impact of spatially resolved PM10 on the IHD mortality. The co-effects of the seasons, gender and age were studied in a stratified analysis. Generalized additive model was used to evaluate the effects of averaged PM10 concentration as well. The averaged spatially resolved PM10 concentration at 287 township-level areas was 120.3 ± 78.1 μg/m3. Ambient PM10 concentration was associated with IHD mortality in spatiotemporal analysis and the strongest effects were identified for the 2-day average. A 10 μg/m3 increase in PM10 was associated with an increase of 0.33% (95% confidence intervals: 0.13%, 0.52%) in daily IHD mortality. The effect estimates using spatially resolved PM10 were larger than that using averaged PM10. The seasonal stratification analysis showed that PM10 had the statistically stronger effects on IHD mortality in summer than that in the other seasons. Males and older people demonstrated the larger response to PM10 exposure. Our results suggest that short-term exposure to particulate air pollution is associated with increased IHD mortality. Spatial variation should be considered for assessing the impacts of particulate air pollution on mortality.

Spatial resolution of a hard x-ray CCD detector.

PubMed

Seely, John F; Pereira, Nino R; Weber, Bruce V; Schumer, Joseph W; Apruzese, John P; Hudson, Lawrence T; Szabo, Csilla I; Boyer, Craig N; Skirlo, Scott

2010-08-10

The spatial resolution of an x-ray CCD detector was determined from the widths of the tungsten x-ray lines in the spectrum formed by a crystal spectrometer in the 58 to 70 keV energy range. The detector had 20 microm pixel, 1700 by 1200 pixel format, and a CsI x-ray conversion scintillator. The spectral lines from a megavolt x-ray generator were focused on the spectrometer's Rowland circle by a curved transmission crystal. The line shapes were Lorentzian with an average width after removal of the natural and instrumental line widths of 95 microm (4.75 pixels). A high spatial frequency background, primarily resulting from scattered gamma rays, was removed from the spectral image by Fourier analysis. The spectral lines, having low spatial frequency in the direction perpendicular to the dispersion, were enhanced by partially removing the Lorentzian line shape and by fitting Lorentzian curves to broad unresolved spectral features. This demonstrates the ability to improve the spectral resolution of hard x-ray spectra that are recorded by a CCD detector with well-characterized intrinsic spatial resolution.

Spatial Lattice Modulation for MIMO Systems

NASA Astrophysics Data System (ADS)

Choi, Jiwook; Nam, Yunseo; Lee, Namyoon

2018-06-01

This paper proposes spatial lattice modulation (SLM), a spatial modulation method for multipleinput-multiple-output (MIMO) systems. The key idea of SLM is to jointly exploit spatial, in-phase, and quadrature dimensions to modulate information bits into a multi-dimensional signal set that consists oflattice points. One major finding is that SLM achieves a higher spectral efficiency than the existing spatial modulation and spatial multiplexing methods for the MIMO channel under the constraint ofM-ary pulseamplitude-modulation (PAM) input signaling per dimension. In particular, it is shown that when the SLM signal set is constructed by using dense lattices, a significant signal-to-noise-ratio (SNR) gain, i.e., a nominal coding gain, is attainable compared to the existing methods. In addition, closed-form expressions for both the average mutual information and average symbol-vector-error-probability (ASVEP) of generic SLM are derived under Rayleigh-fading environments. To reduce detection complexity, a low-complexity detection method for SLM, which is referred to as lattice sphere decoding, is developed by exploiting lattice theory. Simulation results verify the accuracy of the conducted analysis and demonstrate that the proposed SLM techniques achieve higher average mutual information and lower ASVEP than do existing methods.

Urban Spatial Ecological Performance Based on the Data of Remote Sensing of Guyuan

NASA Astrophysics Data System (ADS)

Ren, X.-J.; Chen, X.-J.; Ma, Q.

2018-04-01

The evolution analysis of urban landuse and spatial ecological performance are necessary and useful to recognizing the stage of urban development and revealing the regularity and connotation of urban spatial expansion. Moreover, it lies in the core that should be exmined in the urban sustainable development. In this paper, detailed information has been acquired from the high-resolution satellite imageries of Guyuan, China case study. With the support of GIS, the land-use mapping information and the land cover changes are analyzed, and the process of urban spatial ecological performance evolution by the hierarchical methodology is explored. Results demonstrate that in the past 11 years, the urban spatial ecological performance show an improved process with the dramatic landcover change in Guyuan. Firstly, the landuse structure of Guyuan changes significantly and shows an obvious stage characteristic. Secondly, the urban ecological performance of Guyuan continues to be optimized over the 11 years. Thirdly, the findings suggest that a dynamic monitoring mechanism of urban land use based on high-resolution remote sensing data should be established in urban development, and the rational development of urban land use should be guided by the spatial ecological performance as the basic value orientation.

Raman spectroscopy-based detection of chemical contaminants in food powders

NASA Astrophysics Data System (ADS)

Chao, Kuanglin; Dhakal, Sagar; Qin, Jianwei; Kim, Moon; Bae, Abigail

2016-05-01

Raman spectroscopy technique has proven to be a reliable method for qualitative detection of chemical contaminants in food ingredients and products. For quantitative imaging-based detection, each contaminant particle in a food sample must be detected and it is important to determine the necessary spatial resolution needed to effectively detect the contaminant particles. This study examined the effective spatial resolution required for detection of maleic acid in tapioca starch and benzoyl peroxide in wheat flour. Each chemical contaminant was mixed into its corresponding food powder at a concentration of 1% (w/w). Raman spectral images were collected for each sample, leveled across a 45 mm x 45 mm area, using different spatial resolutions. Based on analysis of these images, a spatial resolution of 0.5mm was selected as effective spatial resolution for detection of maleic acid in starch and benzoyl peroxide in flour. An experiment was then conducted using the 0.5mm spatial resolution to demonstrate Raman imaging-based quantitative detection of these contaminants for samples prepared at 0.1%, 0.3%, and 0.5% (w/w) concentrations. The results showed a linear correlation between the detected numbers of contaminant pixels and the actual concentrations of contaminant.

Hippocampus-dependent place learning enables spatial flexibility in C57BL6/N mice

PubMed Central

Kleinknecht, Karl R.; Bedenk, Benedikt T.; Kaltwasser, Sebastian F.; Grünecker, Barbara; Yen, Yi-Chun; Czisch, Michael; Wotjak, Carsten T.

2012-01-01

Spatial navigation is a fundamental capability necessary in everyday life to locate food, social partners, and shelter. It results from two very different strategies: (1) place learning which enables for flexible way finding and (2) response learning that leads to a more rigid “route following.” Despite the importance of knockout techniques that are only available in mice, little is known about mice' flexibility in spatial navigation tasks. Here we demonstrate for C57BL6/N mice in a water-cross maze (WCM) that only place learning enables spatial flexibility and relearning of a platform position, whereas response learning does not. This capability depends on an intact hippocampal formation, since hippocampus lesions by ibotenic acid (IA) disrupted relearning. In vivo manganese-enhanced magnetic resonance imaging revealed a volume loss of ≥60% of the hippocampus as a critical threshold for relearning impairments. In particular the changes in the left ventral hippocampus were indicative of relearning deficits. In summary, our findings establish the importance of hippocampus-dependent place learning for spatial flexibility and provide a first systematic analysis on spatial flexibility in mice. PMID:23293591

Network-based spatial clustering technique for exploring features in regional industry

NASA Astrophysics Data System (ADS)

Chou, Tien-Yin; Huang, Pi-Hui; Yang, Lung-Shih; Lin, Wen-Tzu

2008-10-01

In the past researches, industrial cluster mainly focused on single or particular industry and less on spatial industrial structure and mutual relations. Industrial cluster could generate three kinds of spillover effects, including knowledge, labor market pooling, and input sharing. In addition, industrial cluster indeed benefits industry development. To fully control the status and characteristics of district industrial cluster can facilitate to improve the competitive ascendancy of district industry. The related researches on industrial spatial cluster were of great significance for setting up industrial policies and promoting district economic development. In this study, an improved model, GeoSOM, that combines DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and SOM (Self-Organizing Map) was developed for analyzing industrial cluster. Different from former distance-based algorithm for industrial cluster, the proposed GeoSOM model can calculate spatial characteristics between firms based on DBSCAN algorithm and evaluate the similarity between firms based on SOM clustering analysis. The demonstrative data sets, the manufacturers around Taichung County in Taiwan, were analyzed for verifying the practicability of the proposed model. The analyzed results indicate that GeoSOM is suitable for evaluating spatial industrial cluster.

A GIS-based spatial correlation analysis for ambient air pollution and AECOPD hospitalizations in Jinan, China.

PubMed

Wang, Wenqiao; Ying, Yangyang; Wu, Quanyuan; Zhang, Haiping; Ma, Dedong; Xiao, Wei

2015-03-01

Acute exacerbations of COPD (AECOPD) are important events during disease procedure. AECOPD have negative effect on patients' quality of life, symptoms and lung function, and result in high socioeconomic costs. Though previous studies have demonstrated the significant association between outdoor air pollution and AECOPD hospitalizations, little is known about the spatial relationship utilized a spatial analyzing technique- Geographical Information System (GIS). Using GIS to investigate the spatial association between ambient air pollution and AECOPD hospitalizations in Jinan City, 2009. 414 AECOPD hospitalization cases in Jinan, 2009 were enrolled in our analysis. Monthly concentrations of five monitored air pollutants (NO2, SO2, PM10, O3, CO) during January 2009-December 2009 were provided by Environmental Protection Agency of Shandong Province. Each individual was geocoded in ArcGIS10.0 software. The spatial distribution of five pollutants and the temporal-spatial specific air pollutants exposure level for each individual was estimated by ordinary Kriging model. Spatial autocorrelation (Global Moran's I) was employed to explore the spatial association between ambient air pollutants and AECOPD hospitalizations. A generalized linear model (GLM) using a Poisson distribution with log-link function was used to construct a core model. At residence, concentrations of SO2, PM10, NO2, CO, O3 and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of SO2, PM10, CO, O3, NO2 at residence is 15.88, 13.93, 12.60, 4.02, 2.44 respectively, while at workplace, concentrations of PM10, SO2, O3, CO and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of PM10, SO2, O3, CO at workplace is 11.39, 8.07, 6.10, and 5.08 respectively. After adjusting for potential confounders in the model, only the PM10 concentrations at workplace showed statistical significance, with a 10 μg/m(3) increase of PM10 at workplace associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD. Ambient air pollution is correlated with AECOPD hospitalizations spatially. A 10 μg/m(3) increase of PM10 at workplace was associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD in Jinan, 2009. As a spatial data processing tool, GIS has novel and great potential on air pollutants exposure assessment and spatial analysis in AECOPD research. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of GIS in foreign direct investment decision support system

NASA Astrophysics Data System (ADS)

Zhou, Jianlan; Sun, Koumei

2007-06-01

It is important to make decisions on how to attract foreign direct investment (FDI) to China and know how the inequality of FDI introduction by locational different provinces. Following background descriptions on China's FDI economic environments and FDI-related policies, this paper demonstrates the uses of geographical information system (GIS) and multi-criterion decision-making (MCDM) framework in solving a spatial multi-objective problem of evaluating and ranking China's provinces for FDI introduction. It implements a foreign direct investment decision support system, which reveals the main determinants of FDI in China and gives some results of regional geographical analysis over spatial data.

Elasticity reconstruction: Beyond the assumption of local homogeneity

NASA Astrophysics Data System (ADS)

Sinkus, Ralph; Daire, Jean-Luc; Van Beers, Bernard E.; Vilgrain, Valerie

2010-07-01

Elasticity imaging is a novel domain which is currently gaining significant interest in the medical field. Most inversion techniques are based on the homogeneity assumption, i.e. the local spatial derivatives of the complex-shear modulus are ignored. This analysis presents an analytic approach in order to overcome this limitation, i.e. first order spatial derivatives of the real-part of the complex-shear modulus are taken into account. Resulting distributions in a gauged breast lesion phantom agree very well with the theoretical expectations. An in-vivo example of a cholangiocarcinoma demonstrates that the new approach provides maps of the viscoelastic properties which agree much better with expectations from anatomy.

Modeling spatially localized photonic nanojets from phase diffraction gratings

NASA Astrophysics Data System (ADS)

Geints, Yu. E.; Zemlyanov, A. A.

2016-04-01

We investigated numerically the specific spatially localized intense optical structure, a photonic nanojet (PNJ), formed in the near-field scattering of optical radiation at phase diffraction gratings. The finite-difference time-domain technique was employed to study the PNJ key parameters (length, width, focal distance, and intensity) produced by diffraction gratings with the saw-tooth, rectangle, and hemispheric line profiles. Our analysis showed that each type of diffraction gratings produces a photonic jet with unique characteristics. Based on the numerical calculations, we demonstrate that the PNJ could be manipulated in a wide range through the variation of period, duty cycle, and shape of diffraction grating rulings.

A method to estimate the effect of deformable image registration uncertainties on daily dose mapping

PubMed Central

Murphy, Martin J.; Salguero, Francisco J.; Siebers, Jeffrey V.; Staub, David; Vaman, Constantin

2012-01-01

Purpose: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. Methods: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. Results: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. Conclusions: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties. PMID:22320766

Investigation of aquifer-estuary interaction using wavelet analysis of fiber-optic temperature data

USGS Publications Warehouse

Henderson, R.D.; Day-Lewis, Frederick D.; Harvey, Charles F.

2009-01-01

Fiber-optic distributed temperature sensing (FODTS) provides sub-minute temporal and meter-scale spatial resolution over kilometer-long cables. Compared to conventional thermistor or thermocouple-based technologies, which measure temperature at discrete (and commonly sparse) locations, FODTS offers nearly continuous spatial coverage, thus providing hydrologic information at spatiotemporal scales previously impossible. Large and information-rich FODTS datasets, however, pose challenges for data exploration and analysis. To date, FODTS analyses have focused on time-series variance as the means to discriminate between hydrologic phenomena. Here, we demonstrate the continuous wavelet transform (CWT) and cross-wavelet transform (XWT) to analyze FODTS in the context of related hydrologic time series. We apply the CWT and XWT to data from Waquoit Bay, Massachusetts to identify the location and timing of tidal pumping of submarine groundwater.

Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces.

PubMed

Abu-Alqumsan, Mohammad; Peer, Angelika

2016-06-01

Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired Electroencephalographic (EEG) data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods. We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels. We found that the multivariate synchronization index method and the maximum contrast combination method are variations of the CCA method. All three methods were found to provide relatively unreliable detections in low signal-to-noise ratio (SNR) regimes. CVARS and the minimum energy combination methods were found to provide better estimates for different SNR levels. Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably.

Experimental evidence of dynamic re-organization of evolving landscapes under changing climatic forcing

NASA Astrophysics Data System (ADS)

Singh, Arvind; Tejedor, Alejandro; Zaliapin, Ilya; Reinhardt, Liam; Foufoula-Georgiou, Efi

2015-04-01

The aim of this study is to better understand the dynamic re-organization of an evolving landscape under a scenario of changing climatic forcing for improving our knowledge of geomorphic transport laws under transient conditions and developing predictive models of landscape response to external perturbations. Real landscape observations for long-term analysis are limited and to this end a high resolution controlled laboratory experiment was conducted at the St. Anthony Falls laboratory at the University of Minnesota. Elevation data were collected at temporal resolution of 5 mins and spatial resolution of 0.5 mm as the landscape approached steady state (constant uplift and precipitation rate) and in the transient state (under the same uplift and 5x precipitation). The results reveal rapid topographic re-organization under a five-fold precipitation increase with the fluvial regime expanding into the previously debris dominated regime, accelerated erosion happening at hillslope scales, and rivers shifting from an erosion-limited to a transport-limited regime. From a connectivity and clustering analysis of the erosional and depositional events, we demonstrate the strikingly different spatial patterns of landscape evolution under steady-state (SS) and transient-state (TS), even when the time under SS is "stretched" compared to that under TS such as to match the total volume and PDF of erosional and depositional amounts. We quantify the spatial coupling of hillslopes and channels and demonstrate that hillslopes lead and channels follow in re-organizing the whole landscape under such an amplified precipitation regime.

Solving phase appearance/disappearance two-phase flow problems with high resolution staggered grid and fully implicit schemes by the Jacobian-free Newton–Krylov Method

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

Zou, Ling; Zhao, Haihua; Zhang, Hongbin

2016-04-01

The phase appearance/disappearance issue presents serious numerical challenges in two-phase flow simulations. Many existing reactor safety analysis codes use different kinds of treatments for the phase appearance/disappearance problem. However, to our best knowledge, there are no fully satisfactory solutions. Additionally, the majority of the existing reactor system analysis codes were developed using low-order numerical schemes in both space and time. In many situations, it is desirable to use high-resolution spatial discretization and fully implicit time integration schemes to reduce numerical errors. In this work, we adapted a high-resolution spatial discretization scheme on staggered grid mesh and fully implicit time integrationmore » methods (such as BDF1 and BDF2) to solve the two-phase flow problems. The discretized nonlinear system was solved by the Jacobian-free Newton Krylov (JFNK) method, which does not require the derivation and implementation of analytical Jacobian matrix. These methods were tested with a few two-phase flow problems with phase appearance/disappearance phenomena considered, such as a linear advection problem, an oscillating manometer problem, and a sedimentation problem. The JFNK method demonstrated extremely robust and stable behaviors in solving the two-phase flow problems with phase appearance/disappearance. No special treatments such as water level tracking or void fraction limiting were used. High-resolution spatial discretization and second- order fully implicit method also demonstrated their capabilities in significantly reducing numerical errors.« less

Imaging of Al/Fe ratios in synthetic Al-goethite revealed by nanoscale secondary ion mass spectrometry.

PubMed

Pohl, Lydia; Kölbl, Angelika; Werner, Florian; Mueller, Carsten W; Höschen, Carmen; Häusler, Werner; Kögel-Knabner, Ingrid

2018-04-30

Aluminium (Al)-substituted goethite is ubiquitous in soils and sediments. The extent of Al-substitution affects the physicochemical properties of the mineral and influences its macroscale properties. Bulk analysis only provides total Al/Fe ratios without providing information with respect to the Al-substitution of single minerals. Here, we demonstrate that nanoscale secondary ion mass spectrometry (NanoSIMS) enables the precise determination of Al-content in single minerals, while simultaneously visualising the variation of the Al/Fe ratio. Al-substituted goethite samples were synthesized with increasing Al concentrations of 0.1, 3, and 7 % and analysed by NanoSIMS in combination with established bulk spectroscopic methods (XRD, FTIR, Mössbauer spectroscopy). The high spatial resolution (50-150 nm) of NanoSIMS is accompanied by a high number of single-point measurements. We statistically evaluated the Al/Fe ratios derived from NanoSIMS, while maintaining the spatial information and reassigning it to its original localization. XRD analyses confirmed increasing concentration of incorporated Al within the goethite structure. Mössbauer spectroscopy revealed 11 % of the goethite samples generated at high Al concentrations consisted of hematite. The NanoSIMS data show that the Al/Fe ratios are in agreement with bulk data derived from total digestion and demonstrated small spatial variability between single-point measurements. More advantageously, statistical analysis and reassignment of single-point measurements allowed us to identify distinct spots with significantly higher or lower Al/Fe ratios. NanoSIMS measurements confirmed the capacity to produce images, which indicated the uniform increase in Al-concentrations in goethite. Using a combination of statistical analysis with information from complementary spectroscopic techniques (XRD, FTIR and Mössbauer spectroscopy) we were further able to reveal spots with lower Al/Fe ratios as hematite. Copyright © 2018 John Wiley & Sons, Ltd.

Abnormal functional specialization within medial prefrontal cortex in high-functioning autism: a multi-voxel similarity analysis

PubMed Central

Meuwese, Julia D.I.; Towgood, Karren J.; Frith, Christopher D.; Burgess, Paul W.

2009-01-01

Multi-voxel pattern analyses have proved successful in ‘decoding’ mental states from fMRI data, but have not been used to examine brain differences associated with atypical populations. We investigated a group of 16 (14 males) high-functioning participants with autism spectrum disorder (ASD) and 16 non-autistic control participants (12 males) performing two tasks (spatial/verbal) previously shown to activate medial rostral prefrontal cortex (mrPFC). Each task manipulated: (i) attention towards perceptual versus self-generated information and (ii) reflection on another person's mental state (‘mentalizing'versus ‘non-mentalizing’) in a 2 × 2 design. Behavioral performance and group-level fMRI results were similar between groups. However, multi-voxel similarity analyses revealed strong differences. In control participants, the spatial distribution of activity generalized significantly between task contexts (spatial/verbal) when examining the same function (attention/mentalizing) but not when comparing different functions. This pattern was disrupted in the ASD group, indicating abnormal functional specialization within mrPFC, and demonstrating the applicability of multi-voxel pattern analysis to investigations of atypical populations. PMID:19174370

Analyzing linear spatial features in ecology.

PubMed

Buettel, Jessie C; Cole, Andrew; Dickey, John M; Brook, Barry W

2018-06-01

The spatial analysis of dimensionless points (e.g., tree locations on a plot map) is common in ecology, for instance using point-process statistics to detect and compare patterns. However, the treatment of one-dimensional linear features (fiber processes) is rarely attempted. Here we appropriate the methods of vector sums and dot products, used regularly in fields like astrophysics, to analyze a data set of mapped linear features (logs) measured in 12 × 1-ha forest plots. For this demonstrative case study, we ask two deceptively simple questions: do trees tend to fall downhill, and if so, does slope gradient matter? Despite noisy data and many potential confounders, we show clearly that topography (slope direction and steepness) of forest plots does matter to treefall. More generally, these results underscore the value of mathematical methods of physics to problems in the spatial analysis of linear features, and the opportunities that interdisciplinary collaboration provides. This work provides scope for a variety of future ecological analyzes of fiber processes in space. © 2018 by the Ecological Society of America.

[Teenage pregnancy rates and socioeconomic characteristics of municipalities in São Paulo State, Southeast Brazil: a spatial analysis].

PubMed

Martinez, Edson Zangiacomi; Roza, Daiane Leite da; Caccia-Bava, Maria do Carmo Gullaci Guimarães; Achcar, Jorge Alberto; Dal-Fabbro, Amaury Lelis

2011-05-01

Teenage pregnancy is a common public health problem worldwide. The objective of this ecological study was to investigate the spatial association between teenage pregnancy rates and socioeconomic characteristics of municipalities in São Paulo State, Southeast Brazil. We used a Bayesian model with a spatial distribution following a conditional autoregressive (CAR) form based on Markov Chain Monte Carlo algorithm. We used data from the Live Birth Information System (SINASC) and the Brazilian Institute of Geography and Statistics (IBGE). Early pregnancy was more frequent in municipalities with lower per capital gross domestic product (GDP), higher poverty rate, smaller population, lower human development index (HDI), and a higher percentage of individuals with State social vulnerability index of 5 or 6 (more vulnerable). The study demonstrates a significant association between teenage pregnancy and socioeconomic indicators.

Spatial aliasing for efficient direction-of-arrival estimation based on steering vector reconstruction

NASA Astrophysics Data System (ADS)

Yan, Feng-Gang; Cao, Bin; Rong, Jia-Jia; Shen, Yi; Jin, Ming

2016-12-01

A new technique is proposed to reduce the computational complexity of the multiple signal classification (MUSIC) algorithm for direction-of-arrival (DOA) estimate using a uniform linear array (ULA). The steering vector of the ULA is reconstructed as the Kronecker product of two other steering vectors, and a new cost function with spatial aliasing at hand is derived. Thanks to the estimation ambiguity of this spatial aliasing, mirror angles mathematically relating to the true DOAs are generated, based on which the full spectral search involved in the MUSIC algorithm is highly compressed into a limited angular sector accordingly. Further complexity analysis and performance studies are conducted by computer simulations, which demonstrate that the proposed estimator requires an extremely reduced computational burden while it shows a similar accuracy to the standard MUSIC.

Boundary control for a flexible manipulator based on infinite dimensional disturbance observer

NASA Astrophysics Data System (ADS)

Jiang, Tingting; Liu, Jinkun; He, Wei

2015-07-01

This paper focuses on disturbance observer and boundary control design for the flexible manipulator in presence of both boundary disturbance and spatially distributed disturbance. Taking the infinite-dimensionality of the flexural dynamics into account, this study proposes a partial differential equation (PDE) model. Since the spatially distributed disturbance is infinite dimensional, it cannot be compensated by the typical disturbance observer, which is designed by finite dimensional approach. To estimate the spatially distributed disturbance, we propose a novel infinite dimensional disturbance observer (IDDO). Applying the IDDO as a feedforward compensator, a boundary control scheme is designed to regulate the joint position and eliminate the elastic vibration simultaneously. Theoretical analysis validates the stability of both the proposed disturbance observer and the boundary controller. The performance of the closed-loop system is demonstrated by numerical simulations.

Impact Induced Delamination Detection and Quantification With Guided Wavefield Analysis

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Leckey, Cara A. C.; Yu, Lingyu; Seebo, Jeffrey P.

2015-01-01

This paper studies impact induced delamination detection and quantification by using guided wavefield data and spatial wavenumber imaging. The complex geometry impact-like delamination is created through a quasi-static indentation on a CFRP plate. To detect and quantify the impact delamination in the CFRP plate, PZT-SLDV sensing and spatial wavenumber imaging are performed. In the PZT-SLDV sensing, the guided waves are generated from the PZT, and the high spatial resolution guided wavefields are measured by the SLDV. The guided wavefield data acquired from the PZT-SLDV sensing represent guided wave propagation in the composite laminate and include guided wave interaction with the delamination damage. The measured guided wavefields are analyzed through the spatial wavenumber imaging method, which generates an image containing the dominant local wavenumber at each spatial location. The spatial wavenumber imaging result for the simple single layer Teflon insert delamination provided quantitative information on delamination damage size and location. The location of delamination damage is indicated by the area with larger wavenumbers in the spatial wavenumber image. The impact-like delamination results only partially agreed with the damage size and shape. The results also demonstrated the dependence on excitation frequency. Future work will further investigate the accuracy of the wavenumber imaging method for real composite damage and the dependence on frequency of excitation.

Spatial distributions of Pseudomonas fluorescens colony variants in mixed-culture biofilms.

PubMed

Workentine, Matthew L; Wang, Siyuan; Ceri, Howard; Turner, Raymond J

2013-07-28

The emergence of colony morphology variants in structured environments is being recognized as important to both niche specialization and stress tolerance. Pseudomonas fluorescens demonstrates diversity in both its natural environment, the rhizosphere, and in laboratory grown biofilms. Sub-populations of these variants within a biofilm have been suggested as important contributors to antimicrobial stress tolerance given their altered susceptibility to various agents. As such it is of interest to determine how these variants might be distributed in the biofilm environment. Here we present an analysis of the spatial distribution of Pseudomonas fluorescens colony morphology variants in mixed-culture biofilms with the wildtype phenotype. These findings reveal that two variant colony morphotypes demonstrate a significant growth advantage over the wildtype morphotype in the biofilm environment. The two variant morphotypes out-grew the wildtype across the entire biofilm and this occurred within 24 h and was maintained through to 96 h. This competitive advantage was not observed in homogeneous broth culture. The significant advantage that the variants demonstrate in biofilm colonization over the wildtype denotes the importance of this phenotype in structured environments.

Short-Term Memory for Space and Time Flexibly Recruit Complementary Sensory-Biased Frontal Lobe Attention Networks.

PubMed

Michalka, Samantha W; Kong, Lingqiang; Rosen, Maya L; Shinn-Cunningham, Barbara G; Somers, David C

2015-08-19

The frontal lobes control wide-ranging cognitive functions; however, functional subdivisions of human frontal cortex are only coarsely mapped. Here, functional magnetic resonance imaging reveals two distinct visual-biased attention regions in lateral frontal cortex, superior precentral sulcus (sPCS) and inferior precentral sulcus (iPCS), anatomically interdigitated with two auditory-biased attention regions, transverse gyrus intersecting precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS). Intrinsic functional connectivity analysis demonstrates that sPCS and iPCS fall within a broad visual-attention network, while tgPCS and cIFS fall within a broad auditory-attention network. Interestingly, we observe that spatial and temporal short-term memory (STM), respectively, recruit visual and auditory attention networks in the frontal lobe, independent of sensory modality. These findings not only demonstrate that both sensory modality and information domain influence frontal lobe functional organization, they also demonstrate that spatial processing co-localizes with visual processing and that temporal processing co-localizes with auditory processing in lateral frontal cortex. Copyright © 2015 Elsevier Inc. All rights reserved.

Novel Multidimensional Cross-Correlation Data Comparison Techniques for Spectroscopic Discernment in a Volumetrically Sensitive, Moderating Type Neutron Spectrometer

NASA Astrophysics Data System (ADS)

Hoshor, Cory; Young, Stephan; Rogers, Brent; Currie, James; Oakes, Thomas; Scott, Paul; Miller, William; Caruso, Anthony

2014-03-01

A novel application of the Pearson Cross-Correlation to neutron spectral discernment in a moderating type neutron spectrometer is introduced. This cross-correlation analysis will be applied to spectral response data collected through both MCNP simulation and empirical measurement by the volumetrically sensitive spectrometer for comparison in 1, 2, and 3 spatial dimensions. The spectroscopic analysis methods discussed will be demonstrated to discern various common spectral and monoenergetic neutron sources.

An efficient solution procedure for the thermoelastic analysis of truss space structures

NASA Technical Reports Server (NTRS)

Givoli, D.; Rand, O.

1992-01-01

A solution procedure is proposed for the thermal and thermoelastic analysis of truss space structures in periodic motion. In this method, the spatial domain is first descretized using a consistent finite element formulation. Then the resulting semi-discrete equations in time are solved analytically by using Fourier decomposition. Geometrical symmetry is taken advantage of completely. An algorithm is presented for the calculation of heat flux distribution. The method is demonstrated via a numerical example of a cylindrically shaped space structure.

Contaminant transport in wetland flows with bulk degradation and bed absorption

NASA Astrophysics Data System (ADS)

Wang, Ping; Chen, G. Q.

2017-09-01

Ecological degradation and absorption are ubiquitous and exert considerable influence on the contaminant transport in natural and constructed wetland flows. It creates an increased demand on models to accurately characterize the spatial concentration distribution of the transport process. This work extends a method of spatial concentration moments by considering the non-uniform longitudinal solute displacements along the vertical direction, and analytically determines the spatial concentration distribution in the very initial stage since source release with effects of bulk degradation and bed absorption. The present method is demonstrated to bear a more accurate prediction especially in the initial stage through convergence analysis of Hermite polynomials. Results reveal that contaminant cloud shows to be more contracted and reformed by bed absorption with increasing damping factor of wetland flows. Tremendous vertical concentration variation especially in the downstream of the contaminant cloud remains great even at asymptotic large times. Spatial concentration evolution by the extended method other than the mean by previous studies is potential for various implements associated with contaminant transport with strict environmental standards.

Drivers of metacommunity structure diverge for common and rare Amazonian tree species.

PubMed

Bispo, Polyanna da Conceição; Balzter, Heiko; Malhi, Yadvinder; Slik, J W Ferry; Dos Santos, João Roberto; Rennó, Camilo Daleles; Espírito-Santo, Fernando D; Aragão, Luiz E O C; Ximenes, Arimatéa C; Bispo, Pitágoras da Conceição

2017-01-01

We analysed the flora of 46 forest inventory plots (25 m x 100 m) in old growth forests from the Amazonian region to identify the role of environmental (topographic) and spatial variables (obtained using PCNM, Principal Coordinates of Neighbourhood Matrix analysis) for common and rare species. For the analyses, we used multiple partial regression to partition the specific effects of the topographic and spatial variables on the univariate data (standardised richness, total abundance and total biomass) and partial RDA (Redundancy Analysis) to partition these effects on composition (multivariate data) based on incidence, abundance and biomass. The different attributes (richness, abundance, biomass and composition based on incidence, abundance and biomass) used to study this metacommunity responded differently to environmental and spatial processes. Considering standardised richness, total abundance (univariate) and composition based on biomass, the results for common species differed from those obtained for all species. On the other hand, for total biomass (univariate) and for compositions based on incidence and abundance, there was a correspondence between the data obtained for the total community and for common species. Our data also show that in general, environmental and/or spatial components are important to explain the variability in tree communities for total and common species. However, with the exception of the total abundance, the environmental and spatial variables measured were insufficient to explain the attributes of the communities of rare species. These results indicate that predicting the attributes of rare tree species communities based on environmental and spatial variables is a substantial challenge. As the spatial component was relevant for several community attributes, our results demonstrate the importance of using a metacommunities approach when attempting to understand the main ecological processes underlying the diversity of tropical forest communities.

Visualizing excipient composition and homogeneity of Compound Liquorice Tablets by near-infrared chemical imaging

NASA Astrophysics Data System (ADS)

Wu, Zhisheng; Tao, Ou; Cheng, Wei; Yu, Lu; Shi, Xinyuan; Qiao, Yanjiang

2012-02-01

This study demonstrated that near-infrared chemical imaging (NIR-CI) was a promising technology for visualizing the spatial distribution and homogeneity of Compound Liquorice Tablets. The starch distribution (indirectly, plant extraction) could be spatially determined using basic analysis of correlation between analytes (BACRA) method. The correlation coefficients between starch spectrum and spectrum of each sample were greater than 0.95. Depending on the accurate determination of starch distribution, a method to determine homogeneous distribution was proposed by histogram graph. The result demonstrated that starch distribution in sample 3 was relatively heterogeneous according to four statistical parameters. Furthermore, the agglomerates domain in each tablet was detected using score image layers of principal component analysis (PCA) method. Finally, a novel method named Standard Deviation of Macropixel Texture (SDMT) was introduced to detect agglomerates and heterogeneity based on binary image. Every binary image was divided into different sizes length of macropixel and the number of zero values in each macropixel was counted to calculate standard deviation. Additionally, a curve fitting graph was plotted on the relationship between standard deviation and the size length of macropixel. The result demonstrated the inter-tablet heterogeneity of both starch and total compounds distribution, simultaneously, the similarity of starch distribution and the inconsistency of total compounds distribution among intra-tablet were signified according to the value of slope and intercept parameters in the curve.

Spatial uncertainty analysis: Propagation of interpolation errors in spatially distributed models

USGS Publications Warehouse

Phillips, D.L.; Marks, D.G.

1996-01-01

In simulation modelling, it is desirable to quantify model uncertainties and provide not only point estimates for output variables but confidence intervals as well. Spatially distributed physical and ecological process models are becoming widely used, with runs being made over a grid of points that represent the landscape. This requires input values at each grid point, which often have to be interpolated from irregularly scattered measurement sites, e.g., weather stations. Interpolation introduces spatially varying errors which propagate through the model We extended established uncertainty analysis methods to a spatial domain for quantifying spatial patterns of input variable interpolation errors and how they propagate through a model to affect the uncertainty of the model output. We applied this to a model of potential evapotranspiration (PET) as a demonstration. We modelled PET for three time periods in 1990 as a function of temperature, humidity, and wind on a 10-km grid across the U.S. portion of the Columbia River Basin. Temperature, humidity, and wind speed were interpolated using kriging from 700- 1000 supporting data points. Kriging standard deviations (SD) were used to quantify the spatially varying interpolation uncertainties. For each of 5693 grid points, 100 Monte Carlo simulations were done, using the kriged values of temperature, humidity, and wind, plus random error terms determined by the kriging SDs and the correlations of interpolation errors among the three variables. For the spring season example, kriging SDs averaged 2.6??C for temperature, 8.7% for relative humidity, and 0.38 m s-1 for wind. The resultant PET estimates had coefficients of variation (CVs) ranging from 14% to 27% for the 10-km grid cells. Maps of PET means and CVs showed the spatial patterns of PET with a measure of its uncertainty due to interpolation of the input variables. This methodology should be applicable to a variety of spatially distributed models using interpolated inputs.

Farmers Extension Program Effects on Yield Gap in North China Plain

NASA Astrophysics Data System (ADS)

Sum, N.; Zhao, Y.

2015-12-01

Improving crop yield of the lowest yielding smallholder farmers in developing countries is essential to both food security of the country and the farmers' livelihood. Although wheat and maize production in most developed countries have reached 80% or greater of yield potential determined by simulated models, yield gap remains high in the developing world. One of these cases is the yield gap of maize in the North China Plain (NCP), where the average farmer's yield is 41% of his or her potential yield. This large yield gap indicates opportunity to raise yields substantially by improving agronomy, especially in nutrition management, irrigation facility, and mechanization issues such as technical services. Farmers' agronomic knowledge is essential to yield performance. In order to propagate such knowledge to farmers, agricultural extension programs, especially in-the-field guidance with training programs at targeted demonstration fields, have become prevalent in China. Although traditional analyses of the effects of the extension program are done through surveys, they are limited to only one to two years and to a small area. However, the spatial analysis tool Google Earth Engine (GEE) and its extensive satellite imagery data allow for unprecedented spatial temporal analysis of yield variation. We used GEE to analyze maize yield in Quzhou county in the North China Plain from 2007 to 2013. We based our analysis on the distance from a demonstration farm plot, the source of the farmers' agronomic knowledge. Our hypothesis was that the farther the farmers' fields were from the demonstration plot, the less access they would have to the knowledge, and the less increase in yield over time. Testing this hypothesis using GEE helps us determine the effectiveness of the demonstration plot in disseminating optimal agronomic practices in addition to evaluating yield performance of the demonstration field itself. Furthermore, we can easily extend this methodology to analyze the whole NCP and any other parts of the world for any type of crop.

Brazilian Road Traffic Fatalities: A Spatial and Environmental Analysis

PubMed Central

de Andrade, Luciano; Vissoci, João Ricardo Nickenig; Rodrigues, Clarissa Garcia; Finato, Karen; Carvalho, Elias; Pietrobon, Ricardo; de Souza, Eniuce Menezes; Nihei, Oscar Kenji; Lynch, Catherine; de Barros Carvalho, Maria Dalva

2014-01-01

Background Road traffic injuries (RTI) are a major public health epidemic killing thousands of people daily. Low and middle-income countries, such as Brazil, have the highest annual rates of road traffic fatalities. In order to improve road safety, this study mapped road traffic fatalities on a Brazilian highway to determine the main environmental factors affecting road traffic fatalities. Methods and Findings Four techniques were utilized to identify and analyze RTI hotspots. We used spatial analysis by points by applying kernel density estimator, and wavelet analysis to identify the main hot regions. Additionally, built environment analysis, and principal component analysis were conducted to verify patterns contributing to crash occurrence in the hotspots. Between 2007 and 2009, 379 crashes were notified, with 466 fatalities on BR277. Higher incidence of crashes occurred on sections of highway with double lanes (ratio 2∶1). The hotspot analysis demonstrated that both the eastern and western regions had higher incidences of crashes when compared to the central region. Through the built environment analysis, we have identified five different patterns, demonstrating that specific environmental characteristics are associated with different types of fatal crashes. Patterns 2 and 4 are constituted mainly by predominantly urban characteristics and have frequent fatal pedestrian crashes. Patterns 1, 3 and 5 display mainly rural characteristics and have higher prevalence of vehicular collisions. In the built environment analysis, the variables length of road in urban area, limited lighting, double lanes roadways, and less auxiliary lanes were associated with a higher incidence of fatal crashes. Conclusions By combining different techniques of analyses, we have identified numerous hotspots and environmental characteristics, which governmental or regulatory agencies could make use to plan strategies to reduce RTI and support life-saving policies. PMID:24498051

Correlation analysis of fracture arrangement in space

NASA Astrophysics Data System (ADS)

Marrett, Randall; Gale, Julia F. W.; Gómez, Leonel A.; Laubach, Stephen E.

2018-03-01

We present new techniques that overcome limitations of standard approaches to documenting spatial arrangement. The new techniques directly quantify spatial arrangement by normalizing to expected values for randomly arranged fractures. The techniques differ in terms of computational intensity, robustness of results, ability to detect anti-correlation, and use of fracture size data. Variation of spatial arrangement across a broad range of length scales facilitates distinguishing clustered and periodic arrangements-opposite forms of organization-from random arrangements. Moreover, self-organized arrangements can be distinguished from arrangements due to extrinsic organization. Traditional techniques for analysis of fracture spacing are hamstrung because they account neither for the sequence of fracture spacings nor for possible coordination between fracture size and position, attributes accounted for by our methods. All of the new techniques reveal fractal clustering in a test case of veins, or cement-filled opening-mode fractures, in Pennsylvanian Marble Falls Limestone. The observed arrangement is readily distinguishable from random and periodic arrangements. Comparison of results that account for fracture size with results that ignore fracture size demonstrates that spatial arrangement is dominated by the sequence of fracture spacings, rather than coordination of fracture size with position. Fracture size and position are not completely independent in this example, however, because large fractures are more clustered than small fractures. Both spatial and size organization of veins here probably emerged from fracture interaction during growth. The new approaches described here, along with freely available software to implement the techniques, can be applied with effect to a wide range of structures, or indeed many other phenomena such as drilling response, where spatial heterogeneity is an issue.

Agent Based Modeling: Fine-Scale Spatio-Temporal Analysis of Pertussis

NASA Astrophysics Data System (ADS)

Mills, D. A.

2017-10-01

In epidemiology, spatial and temporal variables are used to compute vaccination efficacy and effectiveness. The chosen resolution and scale of a spatial or spatio-temporal analysis will affect the results. When calculating vaccination efficacy, for example, a simple environment that offers various ideal outcomes is often modeled using coarse scale data aggregated on an annual basis. In contrast to the inadequacy of this aggregated method, this research uses agent based modeling of fine-scale neighborhood data centered around the interactions of infants in daycare and their families to demonstrate an accurate reflection of vaccination capabilities. Despite being able to prevent major symptoms, recent studies suggest that acellular Pertussis does not prevent the colonization and transmission of Bordetella Pertussis bacteria. After vaccination, a treated individual becomes a potential asymptomatic carrier of the Pertussis bacteria, rather than an immune individual. Agent based modeling enables the measurable depiction of asymptomatic carriers that are otherwise unaccounted for when calculating vaccination efficacy and effectiveness. Using empirical data from a Florida Pertussis outbreak case study, the results of this model demonstrate that asymptomatic carriers bias the calculated vaccination efficacy and reveal a need for reconsidering current methods that are widely used for calculating vaccination efficacy and effectiveness.

Increased Testosterone Decreases Medial Cortical Volume and Neurogenesis in Territorial Side-Blotched Lizards (Uta stansburiana)

PubMed Central

LaDage, Lara D.; Roth, Timothy C.; Downs, Cynthia J.; Sinervo, Barry; Pravosudov, Vladimir V.

2017-01-01

Variation in an animal's spatial environment can induce variation in the hippocampus, an area of the brain involved in spatial cognitive processing. Specifically, increased spatial area use is correlated with increased hippocampal attributes, such as volume and neurogenesis. In the side-blotched lizard (Uta stansburiana), males demonstrate alternative reproductive tactics and are either territorial—defending large, clearly defined spatial boundaries—or non-territorial—traversing home ranges that are smaller than the territorial males' territories. Our previous work demonstrated cortical volume (reptilian hippocampal homolog) correlates with these spatial niches. We found that territorial holders have larger medial cortices than non-territory holders, yet these differences in the neural architecture demonstrated some degree of plasticity as well. Although we have demonstrated a link among territoriality, spatial use, and brain plasticity, the mechanisms that underlie this relationship are unclear. Previous studies found that higher testosterone levels can induce increased use of the spatial area and can cause an upregulation in hippocampal attributes. Thus, testosterone may be the mechanistic link between spatial area use and the brain. What remains unclear, however, is if testosterone can affect the cortices independent of spatial experiences and whether testosterone differentially interacts with territorial status to produce the resultant cortical phenotype. In this study, we compared neurogenesis as measured by the total number of doublecortin-positive cells and cortical volume between territorial and non-territorial males supplemented with testosterone. We found no significant differences in the number of doublecortin-positive cells or cortical volume among control territorial, control non-territorial, and testosterone-supplemented non-territorial males, while testosterone-supplemented territorial males had smaller medial cortices containing fewer doublecortin-positive cells. These results demonstrate that testosterone can modulate medial cortical attributes outside of differential spatial processing experiences but that territorial males appear to be more sensitive to alterations in testosterone levels compared with non-territorial males. PMID:28298883

Spatially-Resolved Proteomics: Rapid Quantitative Analysis of Laser Capture Microdissected Alveolar Tissue Samples

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

Clair, Geremy; Piehowski, Paul D.; Nicola, Teodora

Global proteomics approaches allow characterization of whole tissue lysates to an impressive depth. However, it is now increasingly recognized that to better understand the complexity of multicellular organisms, global protein profiling of specific spatially defined regions/substructures of tissues (i.e. spatially-resolved proteomics) is essential. Laser capture microdissection (LCM) enables microscopic isolation of defined regions of tissues preserving crucial spatial information. However, current proteomics workflows entail several manual sample preparation steps and are challenged by the microscopic mass-limited samples generated by LCM, and that impact measurement robustness, quantification, and throughput. Here, we coupled LCM with a fully automated sample preparation workflow thatmore » with a single manual step allows: protein extraction, tryptic digestion, peptide cleanup and LC-MS/MS analysis of proteomes from microdissected tissues. Benchmarking against the current state of the art in ultrasensitive global proteomic analysis, our approach demonstrated significant improvements in quantification and throughput. Using our LCM-SNaPP proteomics approach, we characterized to a depth of more than 3,400 proteins, the ontogeny of protein changes during normal lung development in laser capture microdissected alveolar tissue containing ~4,000 cells per sample. Importantly, the data revealed quantitative changes for 350 low abundance transcription factors and signaling molecules, confirming earlier transcript-level observations and defining seven modules of coordinated transcription factor/signaling molecule expression patterns, suggesting that a complex network of temporal regulatory control directs normal lung development with epigenetic regulation fine-tuning pre-natal developmental processes. Our LCM-proteomics approach facilitates efficient, spatially-resolved, ultrasensitive global proteomics analyses in high-throughput that will be enabling for several clinical and biological applications.« less

Local regression type methods applied to the study of geophysics and high frequency financial data

NASA Astrophysics Data System (ADS)

Mariani, M. C.; Basu, K.

2014-09-01

In this work we applied locally weighted scatterplot smoothing techniques (Lowess/Loess) to Geophysical and high frequency financial data. We first analyze and apply this technique to the California earthquake geological data. A spatial analysis was performed to show that the estimation of the earthquake magnitude at a fixed location is very accurate up to the relative error of 0.01%. We also applied the same method to a high frequency data set arising in the financial sector and obtained similar satisfactory results. The application of this approach to the two different data sets demonstrates that the overall method is accurate and efficient, and the Lowess approach is much more desirable than the Loess method. The previous works studied the time series analysis; in this paper our local regression models perform a spatial analysis for the geophysics data providing different information. For the high frequency data, our models estimate the curve of best fit where data are dependent on time.

Separation of distinct photoexcitation species in femtosecond transient absorption microscopy

DOE PAGES

Xiao, Kai; Ma, Ying -Zhong; Simpson, Mary Jane; ...

2016-02-03

Femtosecond transient absorption microscopy is a novel chemical imaging capability with simultaneous high spatial and temporal resolution. Although several powerful data analysis approaches have been developed and successfully applied to separate distinct chemical species in such images, the application of such analysis to distinguish different photoexcited species is rare. In this paper, we demonstrate a combined approach based on phasor and linear decomposition analysis on a microscopic level that allows us to separate the contributions of both the excitons and free charge carriers in the observed transient absorption response of a composite organometallic lead halide perovskite film. We found spatialmore » regions where the transient absorption response was predominately a result of excitons and others where it was predominately due to charge carriers, and regions consisting of signals from both contributors. Lastly, quantitative decomposition of the transient absorption response curves further enabled us to reveal the relative contribution of each photoexcitation to the measured response at spatially resolved locations in the film.« less

Exploratory and spatial data analysis (EDA-SDA) for determining regional background levels and anomalies of potentially toxic elements in soils from Catorce-Matehuala, Mexico

USGS Publications Warehouse

Chiprés, J.A.; Castro-Larragoitia, J.; Monroy, M.G.

2009-01-01

The threshold between geochemical background and anomalies can be influenced by the methodology selected for its estimation. Environmental evaluations, particularly those conducted in mineralized areas, must consider this when trying to determinate the natural geochemical status of a study area, quantifying human impacts, or establishing soil restoration values for contaminated sites. Some methods in environmental geochemistry incorporate the premise that anomalies (natural or anthropogenic) and background data are characterized by their own probabilistic distributions. One of these methods uses exploratory data analysis (EDA) on regional geochemical data sets coupled with a geographic information system (GIS) to spatially understand the processes that influence the geochemical landscape in a technique that can be called a spatial data analysis (SDA). This EDA-SDA methodology was used to establish the regional background range from the area of Catorce-Matehuala in north-central Mexico. Probability plots of the data, particularly for those areas affected by human activities, show that the regional geochemical background population is composed of smaller subpopulations associated with factors such as soil type and parent material. This paper demonstrates that the EDA-SDA method offers more certainty in defining thresholds between geochemical background and anomaly than a numeric technique, making it a useful tool for regional geochemical landscape analysis and environmental geochemistry studies.

Is the spatial distribution of brain lesions associated with closed-head injury predictive of subsequent development of attention-deficit/hyperactivity disorder? Analysis with brain-image database

NASA Technical Reports Server (NTRS)

Herskovits, E. H.; Megalooikonomou, V.; Davatzikos, C.; Chen, A.; Bryan, R. N.; Gerring, J. P.

1999-01-01

PURPOSE: To determine whether there is an association between the spatial distribution of lesions detected at magnetic resonance (MR) imaging of the brain in children after closed-head injury and the development of secondary attention-deficit/hyperactivity disorder (ADHD). MATERIALS AND METHODS: Data obtained from 76 children without prior history of ADHD were analyzed. MR images were obtained 3 months after closed-head injury. After manual delineation of lesions, images were registered to the Talairach coordinate system. For each subject, registered images and secondary ADHD status were integrated into a brain-image database, which contains depiction (visualization) and statistical analysis software. Using this database, we assessed visually the spatial distributions of lesions and performed statistical analysis of image and clinical variables. RESULTS: Of the 76 children, 15 developed secondary ADHD. Depiction of the data suggested that children who developed secondary ADHD had more lesions in the right putamen than children who did not develop secondary ADHD; this impression was confirmed statistically. After Bonferroni correction, we could not demonstrate significant differences between secondary ADHD status and lesion burdens for the right caudate nucleus or the right globus pallidus. CONCLUSION: Closed-head injury-induced lesions in the right putamen in children are associated with subsequent development of secondary ADHD. Depiction software is useful in guiding statistical analysis of image data.

A computed tomography-based spatial normalization for the analysis of [18F] fluorodeoxyglucose positron emission tomography of the brain.

PubMed

Cho, Hanna; Kim, Jin Su; Choi, Jae Yong; Ryu, Young Hoon; Lyoo, Chul Hyoung

2014-01-01

We developed a new computed tomography (CT)-based spatial normalization method and CT template to demonstrate its usefulness in spatial normalization of positron emission tomography (PET) images with [(18)F] fluorodeoxyglucose (FDG) PET studies in healthy controls. Seventy healthy controls underwent brain CT scan (120 KeV, 180 mAs, and 3 mm of thickness) and [(18)F] FDG PET scans using a PET/CT scanner. T1-weighted magnetic resonance (MR) images were acquired for all subjects. By averaging skull-stripped and spatially-normalized MR and CT images, we created skull-stripped MR and CT templates for spatial normalization. The skull-stripped MR and CT images were spatially normalized to each structural template. PET images were spatially normalized by applying spatial transformation parameters to normalize skull-stripped MR and CT images. A conventional perfusion PET template was used for PET-based spatial normalization. Regional standardized uptake values (SUV) measured by overlaying the template volume of interest (VOI) were compared to those measured with FreeSurfer-generated VOI (FSVOI). All three spatial normalization methods underestimated regional SUV values by 0.3-20% compared to those measured with FSVOI. The CT-based method showed slightly greater underestimation bias. Regional SUV values derived from all three spatial normalization methods were correlated significantly (p < 0.0001) with those measured with FSVOI. CT-based spatial normalization may be an alternative method for structure-based spatial normalization of [(18)F] FDG PET when MR imaging is unavailable. Therefore, it is useful for PET/CT studies with various radiotracers whose uptake is expected to be limited to specific brain regions or highly variable within study population.

150-μm Spatial Resolution Using Photon-Counting Detector Computed Tomography Technology: Technical Performance and First Patient Images.

PubMed

Leng, Shuai; Rajendran, Kishore; Gong, Hao; Zhou, Wei; Halaweish, Ahmed F; Henning, Andre; Kappler, Steffen; Baer, Matthias; Fletcher, Joel G; McCollough, Cynthia H

2018-05-28

The aims of this study were to quantitatively assess two new scan modes on a photon-counting detector computed tomography system, each designed to maximize spatial resolution, and to qualitatively demonstrate potential clinical impact using patient data. This Health Insurance Portability Act-compliant study was approved by our institutional review board. Two high-spatial-resolution scan modes (Sharp and UHR) were evaluated using phantoms to quantify spatial resolution and image noise, and results were compared with the standard mode (Macro). Patients were scanned using a conventional energy-integrating detector scanner and the photon-counting detector scanner using the same radiation dose. In first patient images, anatomic details were qualitatively evaluated to demonstrate potential clinical impact. Sharp and UHR modes had a 69% and 87% improvement in in-plane spatial resolution, respectively, compared with Macro mode (10% modulation-translation-function values of 16.05, 17.69, and 9.48 lp/cm, respectively). The cutoff spatial frequency of the UHR mode (32.4 lp/cm) corresponded to a limiting spatial resolution of 150 μm. The full-width-at-half-maximum values of the section sensitivity profiles were 0.41, 0.44, and 0.67 mm for the thinnest image thickness for each mode (0.25, 0.25, and 0.5 mm, respectively). At the same in-plane spatial resolution, Sharp and UHR images had up to 15% lower noise than Macro images. Patient images acquired in Sharp mode demonstrated better delineation of fine anatomic structures compared with Macro mode images. Phantom studies demonstrated superior resolution and noise properties for the Sharp and UHR modes relative to the standard Macro mode and patient images demonstrated the potential benefit of these scan modes for clinical practice.

Large-Scale Overlays and Trends: Visually Mining, Panning and Zooming the Observable Universe.

PubMed

Luciani, Timothy Basil; Cherinka, Brian; Oliphant, Daniel; Myers, Sean; Wood-Vasey, W Michael; Labrinidis, Alexandros; Marai, G Elisabeta

2014-07-01

We introduce a web-based computing infrastructure to assist the visual integration, mining and interactive navigation of large-scale astronomy observations. Following an analysis of the application domain, we design a client-server architecture to fetch distributed image data and to partition local data into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with hardware-accelerated pixel-based overlays and an online cross-registration pipeline, this approach allows the fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. To further facilitate the integration and mining of spatial and non-spatial data, we introduce interactive trend images-compact visual representations for identifying outlier objects and for studying trends within large collections of spatial objects of a given class. In a demonstration, images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. Trend images are interactively generated from catalog data and used to visually mine astronomy observations of similar type. The front-end of the infrastructure uses the web technologies WebGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables it to serve the needs of the astronomy community. Evaluation on three case studies, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field; and its potential benefits to large scale geospatial visualization in general.

Pseudoneglect in line bisection judgement is associated with a modulation of right hemispheric spatial attention dominance in right-handers.

PubMed

Zago, Laure; Petit, Laurent; Jobard, Gael; Hay, Julien; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie; Karnath, Hans-Otto; Mellet, Emmanuel

2017-01-08

The objective of this study was to validate a line bisection judgement (LBJ) task for use in investigating the lateralized cerebral bases of spatial attention in a sample of 51 right-handed healthy participants. Using functional magnetic resonance imaging (fMRI), the participants performed a LBJ task that was compared to a visuomotor control task during which the participants made similar saccadic and motoric responses. Cerebral lateralization was determined using a voxel-based functional asymmetry analysis and a hemispheric functional lateralization index (HFLI) computed from fMRI contrast images. Behavioural attentional deviation biases were assessed during the LBJ task and a "paper and pencil" symbol cancellation task (SCT). Individual visuospatial skills were also evaluated. The results showed that both the LBJ and SCT tasks elicited leftward spatial biases in healthy subjects, although the biases were not correlated, which indicated their independence. Neuroimaging results showed that the LBJ task elicited a right hemispheric lateralization, with rightward asymmetries found in a large posterior occipito-parietal area, the posterior calcarine sulcus (V1p) and the temporo-occipital junction (TOJ) and in the inferior frontal gyrus, the anterior insula and the superior medial frontal gyrus. The comparison of the LBJ asymmetry map to the lesion map of neglect patients who suffer line bisection deviation demonstrated maximum overlap in a network that included the middle occipital gyrus (MOG), the TOJ, the anterior insula and the inferior frontal region, likely subtending spatial LBJ bias. Finally, the LBJ task-related cerebral lateralization was specifically correlated with the LBJ spatial bias but not with the SCT bias or with the visuospatial skills of the participants. Taken together, these results demonstrated that the LBJ task is adequate for investigating spatial lateralization in healthy subjects and is suitable for determining the factors underlying the variability of spatial cerebral lateralization. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatial and temporal analysis of postural control in dyslexic children.

PubMed

Gouleme, Nathalie; Gerard, Christophe Loic; Bui-Quoc, Emmanuel; Bucci, Maria Pia

2015-07-01

The aim of this study is to examine postural control of dyslexic children using both spatial and temporal analysis. Thirty dyslexic (mean age 9.7±0.3years) and thirty non-dyslexic age-matched children participated in the study. Postural stability was evaluated using Multitest Equilibre from Framiral®. Posture was recorded in the following conditions: eyes open fixating a target (EO) and eyes closed (EC) on stable (-S-) and unstable (-U-) platforms. The findings of this study showed poor postural stability in dyslexic children with respect to the non-dyslexic children group, as demonstrated by both spatial and temporal analysis. In both groups of children postural control depends on the condition, and improves when the eyes are open on a stable platform. Dyslexic children have spectral power indices that are higher than in non-dyslexic children and they showed a shorter cancelling time. Poor postural control in dyslexic children could be due to a deficit in using sensory information most likely caused by impairment in cerebellar activity. The reliability of brain activation patterns, namely in using sensory input and cerebellar activity may explain the deficit in postural control in dyslexic children. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Spatially Explicit Landscape-Level Ecological Risks Induced by Land Use and Land Cover Change in a National Ecologically Representative Region in China.

PubMed

Gong, Jian; Yang, Jianxin; Tang, Wenwu

2015-11-09

Land use and land cover change is driven by multiple influential factors from environmental and social dimensions in a land system. Land use practices of human decision-makers modify the landscape of the land system, possibly leading to landscape fragmentation, biodiversity loss, or environmental pollution-severe environmental or ecological impacts. While landscape-level ecological risk assessment supports the evaluation of these impacts, investigations on how these ecological risks induced by land use practices change over space and time in response to alternative policy intervention remain inadequate. In this article, we conducted spatially explicit landscape ecological risk analysis in Ezhou City, China. Our study area is a national ecologically representative region experiencing drastic land use and land cover change, and is regulated by multiple policies represented by farmland protection, ecological conservation, and urban development. We employed landscape metrics to consider the influence of potential landscape-level disturbance for the evaluation of landscape ecological risks. Using spatiotemporal simulation, we designed scenarios to examine spatiotemporal patterns in landscape ecological risks in response to policy intervention. Our study demonstrated that spatially explicit landscape ecological risk analysis combined with simulation-driven scenario analysis is of particular importance for guiding the sustainable development of ecologically vulnerable land systems.

Cost-effectiveness of dryland forest restoration evaluated by spatial analysis of ecosystem services

PubMed Central

Birch, Jennifer C.; Newton, Adrian C.; Aquino, Claudia Alvarez; Cantarello, Elena; Echeverría, Cristian; Kitzberger, Thomas; Schiappacasse, Ignacio; Garavito, Natalia Tejedor

2010-01-01

Although ecological restoration is widely used to combat environmental degradation, very few studies have evaluated the cost-effectiveness of this approach. We examine the potential impact of forest restoration on the value of multiple ecosystem services across four dryland areas in Latin America, by estimating the net value of ecosystem service benefits under different reforestation scenarios. The values of selected ecosystem services were mapped under each scenario, supported by the use of a spatially explicit model of forest dynamics. We explored the economic potential of a change in land use from livestock grazing to restored native forest using different discount rates and performed a cost–benefit analysis of three restoration scenarios. Results show that passive restoration is cost-effective for all study areas on the basis of the services analyzed, whereas the benefits from active restoration are generally outweighed by the relatively high costs involved. These findings were found to be relatively insensitive to discount rate but were sensitive to the market value of carbon. Substantial variation in values was recorded between study areas, demonstrating that ecosystem service values are strongly context specific. However, spatial analysis enabled localized areas of net benefits to be identified, indicating the value of this approach for identifying the relative costs and benefits of restoration interventions across a landscape. PMID:21106761

Cost-effectiveness of dryland forest restoration evaluated by spatial analysis of ecosystem services.

PubMed

Birch, Jennifer C; Newton, Adrian C; Aquino, Claudia Alvarez; Cantarello, Elena; Echeverría, Cristian; Kitzberger, Thomas; Schiappacasse, Ignacio; Garavito, Natalia Tejedor

2010-12-14

Although ecological restoration is widely used to combat environmental degradation, very few studies have evaluated the cost-effectiveness of this approach. We examine the potential impact of forest restoration on the value of multiple ecosystem services across four dryland areas in Latin America, by estimating the net value of ecosystem service benefits under different reforestation scenarios. The values of selected ecosystem services were mapped under each scenario, supported by the use of a spatially explicit model of forest dynamics. We explored the economic potential of a change in land use from livestock grazing to restored native forest using different discount rates and performed a cost-benefit analysis of three restoration scenarios. Results show that passive restoration is cost-effective for all study areas on the basis of the services analyzed, whereas the benefits from active restoration are generally outweighed by the relatively high costs involved. These findings were found to be relatively insensitive to discount rate but were sensitive to the market value of carbon. Substantial variation in values was recorded between study areas, demonstrating that ecosystem service values are strongly context specific. However, spatial analysis enabled localized areas of net benefits to be identified, indicating the value of this approach for identifying the relative costs and benefits of restoration interventions across a landscape.

Mid-infrared thermal imaging for an effective mapping of surface materials and sub-surface detachments in mural paintings: integration of thermography and thermal quasi-reflectography

NASA Astrophysics Data System (ADS)

Daffara, C.; Parisotto, S.; Mariotti, P. I.

2015-06-01

Cultural Heritage is discovering how precious is thermal analysis as a tool to improve the restoration, thanks to its ability to inspect hidden details. In this work a novel dual mode imaging approach, based on the integration of thermography and thermal quasi-reflectography (TQR) in the mid-IR is demonstrated for an effective mapping of surface materials and of sub-surface detachments in mural painting. The tool was validated through a unique application: the "Monocromo" by Leonardo da Vinci in Italy. The dual mode acquisition provided two spatially aligned dataset: the TQR image and the thermal sequence. Main steps of the workflow included: 1) TQR analysis to map surface features and 2) to estimate the emissivity; 3) projection of the TQR frame on reference orthophoto and TQR mosaicking; 4) thermography analysis to map detachments; 5) use TQR to solve spatial referencing and mosaicking for the thermal-processed frames. Referencing of thermal images in the visible is a difficult aspect of the thermography technique that the dual mode approach allows to solve in effective way. We finally obtained the TQR and the thermal maps spatially referenced to the mural painting, thus providing the restorer a valuable tool for the restoration of the detachments.

Non-Destructive Study of Bulk Crystallinity and Elemental Composition of Natural Gold Single Crystal Samples by Energy-Resolved Neutron Imaging

PubMed Central

Tremsin, Anton S.; Rakovan, John; Shinohara, Takenao; Kockelmann, Winfried; Losko, Adrian S.; Vogel, Sven C.

2017-01-01

Energy-resolved neutron imaging enables non-destructive analyses of bulk structure and elemental composition, which can be resolved with high spatial resolution at bright pulsed spallation neutron sources due to recent developments and improvements of neutron counting detectors. This technique, suitable for many applications, is demonstrated here with a specific study of ~5–10 mm thick natural gold samples. Through the analysis of neutron absorption resonances the spatial distribution of palladium (with average elemental concentration of ~0.4 atom% and ~5 atom%) is mapped within the gold samples. At the same time, the analysis of coherent neutron scattering in the thermal and cold energy regimes reveals which samples have a single-crystalline bulk structure through the entire sample volume. A spatially resolved analysis is possible because neutron transmission spectra are measured simultaneously on each detector pixel in the epithermal, thermal and cold energy ranges. With a pixel size of 55 μm and a detector-area of 512 by 512 pixels, a total of 262,144 neutron transmission spectra are measured concurrently. The results of our experiments indicate that high resolution energy-resolved neutron imaging is a very attractive analytical technique in cases where other conventional non-destructive methods are ineffective due to sample opacity. PMID:28102285

Spatially Explicit Landscape-Level Ecological Risks Induced by Land Use and Land Cover Change in a National Ecologically Representative Region in China

PubMed Central

Gong, Jian; Yang, Jianxin; Tang, Wenwu

2015-01-01

Land use and land cover change is driven by multiple influential factors from environmental and social dimensions in a land system. Land use practices of human decision-makers modify the landscape of the land system, possibly leading to landscape fragmentation, biodiversity loss, or environmental pollution—severe environmental or ecological impacts. While landscape-level ecological risk assessment supports the evaluation of these impacts, investigations on how these ecological risks induced by land use practices change over space and time in response to alternative policy intervention remain inadequate. In this article, we conducted spatially explicit landscape ecological risk analysis in Ezhou City, China. Our study area is a national ecologically representative region experiencing drastic land use and land cover change, and is regulated by multiple policies represented by farmland protection, ecological conservation, and urban development. We employed landscape metrics to consider the influence of potential landscape-level disturbance for the evaluation of landscape ecological risks. Using spatiotemporal simulation, we designed scenarios to examine spatiotemporal patterns in landscape ecological risks in response to policy intervention. Our study demonstrated that spatially explicit landscape ecological risk analysis combined with simulation-driven scenario analysis is of particular importance for guiding the sustainable development of ecologically vulnerable land systems. PMID:26569270

Spatial-mode storage in a gradient-echo memory

NASA Astrophysics Data System (ADS)

Higginbottom, D. B.; Sparkes, B. M.; Rancic, M.; Pinel, O.; Hosseini, M.; Lam, P. K.; Buchler, B. C.

2012-08-01

Three-level atomic gradient echo memory (Λ-GEM) is a proposed candidate for efficient quantum storage and for linear optical quantum computation with time-bin multiplexing [Hosseini , Nature (London)NATUAS0028-083610.1038/nature08325 461, 241 (2009)]. In this paper we investigate the spatial multimode properties of a Λ-GEM system. Using a high-speed triggered CCD, we demonstrate the storage of complex spatial modes and images. We also present an in-principle demonstration of spatial multiplexing by showing selective recall of spatial elements of a stored spin wave. Using our measurements, we consider the effect of diffusion within the atomic vapor and investigate its role in spatial decoherence. Our measurements allow us to quantify the spatial distortion due to both diffusion and inhomogeneous control field scattering and compare these to theoretical models.

A New Methodology of Spatial Cross-Correlation Analysis

PubMed Central

Chen, Yanguang

2015-01-01

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran’s index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson’s correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China’s urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes. PMID:25993120

A new methodology of spatial cross-correlation analysis.

PubMed

Chen, Yanguang

2015-01-01

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran's index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson's correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China's urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes.

Remote sensing data with the conditional latin hypercube sampling and geostatistical approach to delineate landscape changes induced by large chronological physical disturbances.

PubMed

Lin, Yu-Pin; Chu, Hone-Jay; Wang, Cheng-Long; Yu, Hsiao-Hsuan; Wang, Yung-Chieh

2009-01-01

This study applies variogram analyses of normalized difference vegetation index (NDVI) images derived from SPOT HRV images obtained before and after the ChiChi earthquake in the Chenyulan watershed, Taiwan, as well as images after four large typhoons, to delineate the spatial patterns, spatial structures and spatial variability of landscapes caused by these large disturbances. The conditional Latin hypercube sampling approach was applied to select samples from multiple NDVI images. Kriging and sequential Gaussian simulation with sufficient samples were then used to generate maps of NDVI images. The variography of NDVI image results demonstrate that spatial patterns of disturbed landscapes were successfully delineated by variogram analysis in study areas. The high-magnitude Chi-Chi earthquake created spatial landscape variations in the study area. After the earthquake, the cumulative impacts of typhoons on landscape patterns depended on the magnitudes and paths of typhoons, but were not always evident in the spatiotemporal variability of landscapes in the study area. The statistics and spatial structures of multiple NDVI images were captured by 3,000 samples from 62,500 grids in the NDVI images. Kriging and sequential Gaussian simulation with the 3,000 samples effectively reproduced spatial patterns of NDVI images. However, the proposed approach, which integrates the conditional Latin hypercube sampling approach, variogram, kriging and sequential Gaussian simulation in remotely sensed images, efficiently monitors, samples and maps the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial variability and heterogeneity.

Language within your reach: near-far perceptual space and spatial demonstratives.

PubMed

Coventry, Kenny R; Valdés, Berenice; Castillo, Alejandro; Guijarro-Fuentes, Pedro

2008-09-01

Spatial demonstratives (this/that) play a crucial role when indicating object locations using language. However, the relationship between the use of these proximal and distal linguistic descriptors and the near (peri-personal) versus far (extra-personal) perceptual space distinction is a source of controversy [Kemmerer, D. (1999). "Near" and "far" in language and perception. Cognition 73, 35-63], and has been hitherto under investigated. Two experiments examined the influence of object distance from speaker, tool use (participants pointed at objects with their finger/arm or with a stick), and interaction with objects (whether or not participants placed objects themselves) on spatial demonstrative use (e.g. this/that red triangle) in English (this/that) and Spanish (este/ese/aquel). The results show that the use of demonstratives across two languages is affected by distance from speaker and by both tool use and interaction with objects. These results support the view that spatial demonstrative use corresponds with a basic distinction between near and far perceptual space.

Soil moisture observations using L-, C-, and X-band microwave radiometers

NASA Astrophysics Data System (ADS)

Bolten, John Dennis

The purpose of this thesis is to further the current understanding of soil moisture remote sensing under varying conditions using L-, C-, and X-band. Aircraft and satellite instruments are used to investigate the effects of frequency and spatial resolution on soil moisture sensitivity. The specific objectives of the research are to examine multi-scale observed and modeled microwave radiobrightness, evaluate new EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperature and soil moisture retrievals, and examine future satellite-based technologies for soil moisture sensing. The cycling of Earth's water, energy and carbon is vital to understanding global climate. Over land, these processes are largely dependent on the amount of moisture within the top few centimeters of the soil. However, there are currently no methods available that can accurately characterize Earth's soil moisture layer at the spatial scales or temporal resolutions appropriate for climate modeling. The current work uses ground truth, satellite and aircraft remote sensing data from three large-scale field experiments having different land surface, topographic and climate conditions. A physically-based radiative transfer model is used to simulate the observed aircraft and satellite measurements using spatially and temporally co-located surface parameters. A robust analysis of surface heterogeneity and scaling is possible due to the combination of multiple datasets from a range of microwave frequencies and field conditions. Accurate characterization of spatial and temporal variability of soil moisture during the three field experiments is achieved through sensor calibration and algorithm validation. Comparisons of satellite observations and resampled aircraft observations are made using soil moisture from a Numerical Weather Prediction (NWP) model in order to further demonstrate a soil moisture correlation where point data was unavailable. The influence of vegetation, spatial scaling, and surface heterogeneity on multi-scale soil moisture prediction is presented. This work demonstrates that derived soil moisture using remote sensing provides a better coverage of soil moisture spatial variability than traditional in-situ sensors. Effects of spatial scale were shown to be less significant than frequency on soil moisture sensitivity. Retrievals of soil moisture using the current methods proved inadequate under some conditions; however, this study demonstrates the need for concurrent spaceborne frequencies including L-, C, and X-band.

Misunderstood markets: The case of California gasoline

NASA Astrophysics Data System (ADS)

Thompson, Jennifer Ruth

In 1996, the California Air Resources Board (CARB) implemented a new benchmark for cleaner burning gasoline that is unique to California. Since then, government officials have often expressed concern that the uniqueness of petroleum products in California segregates the industry, allowing for gasoline prices in the State that are too high and too volatile. The growing concern about the segmentation of the California markets lends itself to analysis of spatial pricing. Spatial price spreads of wholesale gasoline within the state exhibit some characteristics that seem, on the surface, inconsistent with spatial price theory. Particularly, some spatial price spreads of wholesale gasoline appear larger than accepted transportation rates and other spreads are negative, giving a price signal for transportation against the physical flow of product. Both characteristics suggest some limitation in the arbitrage process. Proprietary data, consisting of daily product prices for the years 2000 through 2002, disaggregated by company, product, grade, and location is used to examine more closely spatial price patterns. My discussion of institutional and physical infrastructure outlines two features of the industry that limit, but do not prohibit, arbitrage. First, a look into branding and wholesale contracting shows that contract terms, specifically branding agreements, reduces the price-responsiveness of would-be arbitrageurs. Second, review of maps and documents illustrating the layout of physical infrastructure, namely petroleum pipelines, confirms the existence of some connections among markets. My analysis of the day-of-the-week effects on wholesale prices demonstrates how the logistics of the use of transportation infrastructure affect market prices. Further examination of spatial price relationships shows that diesel prices follow closely the Augmented Law of One Price (ALOP), and that branding agreements cause gasoline prices to deviate substantially ALOP. Without branding, the gasoline prices follow as closely as diesel prices to ALOP. Finally, system-wide causality analysis finds linkages among markets. In summary, both physical and statistical linkages exist among the study markets. Arbitrage among these markets is limited by the logistics of transportation infrastructure and by branding agreements in wholesale contracting.

Highly Accurate Quantitative Analysis Of Enantiomeric Mixtures from Spatially Frequency Encoded 1H NMR Spectra.

PubMed

Plainchont, Bertrand; Pitoux, Daisy; Cyrille, Mathieu; Giraud, Nicolas

2018-02-06

We propose an original concept to measure accurately enantiomeric excesses on proton NMR spectra, which combines high-resolution techniques based on a spatial encoding of the sample, with the use of optically active weakly orienting solvents. We show that it is possible to simulate accurately dipolar edited spectra of enantiomers dissolved in a chiral liquid crystalline phase, and to use these simulations to calibrate integrations that can be measured on experimental data, in order to perform a quantitative chiral analysis. This approach is demonstrated on a chemical intermediate for which optical purity is an essential criterion. We find that there is a very good correlation between the experimental and calculated integration ratios extracted from G-SERF spectra, which paves the way to a general method of determination of enantiomeric excesses based on the observation of 1 H nuclei.

Accounting for imperfect detection and survey bias in statistical analysis of presence-only data

USGS Publications Warehouse

Dorazio, Robert M.

2014-01-01

Using mathematical proof and simulation-based comparisons, I demonstrate that biases induced by errors in detection or biased selection of survey locations can be reduced or eliminated by using the hierarchical model to analyse presence-only data in conjunction with counts observed in planned surveys. I show that a relatively small number of high-quality data (from planned surveys) can be used to leverage the information in presence-only observations, which usually have broad spatial coverage but may not be informative of both occurrence and detectability of individuals. Because a variety of sampling protocols can be used in planned surveys, this approach to the analysis of presence-only data is widely applicable. In addition, since the point-process model is formulated at the level of an individual, it can be extended to account for biological interactions between individuals and temporal changes in their spatial distributions.

Resolving fluorophores by unmixing multispectral fluorescence tomography with independent component analysis

NASA Astrophysics Data System (ADS)

Pu, Huangsheng; Zhang, Guanglei; He, Wei; Liu, Fei; Guang, Huizhi; Zhang, Yue; Bai, Jing; Luo, Jianwen

2014-09-01

It is a challenging problem to resolve and identify drug (or non-specific fluorophore) distribution throughout the whole body of small animals in vivo. In this article, an algorithm of unmixing multispectral fluorescence tomography (MFT) images based on independent component analysis (ICA) is proposed to solve this problem. ICA is used to unmix the data matrix assembled by the reconstruction results from MFT. Then the independent components (ICs) that represent spatial structures and the corresponding spectrum courses (SCs) which are associated with spectral variations can be obtained. By combining the ICs with SCs, the recovered MFT images can be generated and fluorophore concentration can be calculated. Simulation studies, phantom experiments and animal experiments with different concentration contrasts and spectrum combinations are performed to test the performance of the proposed algorithm. Results demonstrate that the proposed algorithm can not only provide the spatial information of fluorophores, but also recover the actual reconstruction of MFT images.

Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification

NASA Astrophysics Data System (ADS)

Xu, Yadong; Serre, Marc L.; Reyes, Jeanette M.; Vizuete, William

2017-10-01

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error.

Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance.

PubMed

Li, Ke; Garrett, John; Ge, Yongshuai; Chen, Guang-Hong

2014-07-01

Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks. The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDIvol =4, 8, 12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIR (Veo(®), GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d'. (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than FBP (and vice versa); the value of this transitional contrast highly depended on the dose level. (3) The PSFs of MBIR could be approximated as Gaussian functions with reasonably good accuracy. (4) Thez resolution of MBIR showed similar contrast and dose dependence. (5) Noise standard deviation assessed on the edges of objects demonstrated a trade-off with spatial resolution in MBIR. (5) When both spatial resolution and image noise were considered using the CHO analysis, MBIR led to significant improvement in the overall CT image quality for both high and low contrast detection tasks at both standard and low dose levels. Due to the intrinsic nonlinearity of the MBIR method, many well-known CT spatial resolution and noise properties have been modified. In particular, dose dependence and contrast dependence have been introduced to the spatial resolution of CT images by MBIR. The method has also introduced some novel noise-resolution trade-off not seen in traditional CT images. While the benefits of MBIR regarding the overall image quality, as demonstrated in this work, are significant, the optimal use of this method in clinical practice demands a thorough understanding of its unique physical characteristics.

Microfluidic Devices for Analysis of Spatial Orientation Behaviors in Semi-Restrained Caenorhabditis elegans

PubMed Central

McCormick, Kathryn E.; Gaertner, Bryn E.; Sottile, Matthew; Phillips, Patrick C.; Lockery, Shawn R.

2011-01-01

This article describes the fabrication and use of microfluidic devices for investigating spatial orientation behaviors in nematode worms (Caenorhabditis elegans). Until now, spatial orientation has been studied in freely moving nematodes in which the frequency and nature of encounters with the gradient are uncontrolled experimental variables. In the new devices, the nematode is held in place by a restraint that aligns the longitudinal axis of the body with the border between two laminar fluid streams, leaving the animal's head and tail free to move. The content of the fluid streams can be manipulated to deliver step gradients in space or time. We demonstrate the utility of the device by identifying previously uncharacterized aspects of the behavioral mechanisms underlying chemotaxis, osmotic avoidance, and thermotaxis in this organism. The new devices are readily adaptable to behavioral and imaging studies involving fluid borne stimuli in a wide range of sensory modalities. PMID:22022437

Influence of orographically steered winds on Mutsu Bay surface currents

NASA Astrophysics Data System (ADS)

Yamaguchi, Satoshi; Kawamura, Hiroshi

2005-09-01

Effects of spatially dependent sea surface wind field on currents in Mutsu Bay, which is located at the northern end of Japanese Honshu Island, are investigated using winds derived from synthetic aperture radar (SAR) images and a numerical model. A characteristic wind pattern over the bay was evidenced from analysis of 118 SAR images and coincided with in situ observations. Wind is topographically steered with easterly winds entering the bay through the terrestrial gap and stronger wind blowing over the central water toward its mouth. Nearshore winds are weaker due to terrestrial blockages. Using the Princeton Ocean Model, we investigated currents forced by the observed spatially dependent wind field. The predicted current pattern agrees well with available observations. For a uniform wind field of equal magnitude and average direction, the circulation pattern departs from observations demonstrating that vorticity input due to spatially dependent wind stress is essential in generation of the wind-driven current in Mutsu Bay.

Spatially organizing biochemistry: choosing a strategy to translate synthetic biology to the factory.

PubMed

Jakobson, Christopher M; Tullman-Ercek, Danielle; Mangan, Niall M

2018-05-29

Natural biochemical systems are ubiquitously organized both in space and time. Engineering the spatial organization of biochemistry has emerged as a key theme of synthetic biology, with numerous technologies promising improved biosynthetic pathway performance. One strategy, however, may produce disparate results for different biosynthetic pathways. We use a spatially resolved kinetic model to explore this fundamental design choice in systems and synthetic biology. We predict that two example biosynthetic pathways have distinct optimal organization strategies that vary based on pathway-dependent and cell-extrinsic factors. Moreover, we demonstrate that the optimal design varies as a function of kinetic and biophysical properties, as well as culture conditions. Our results suggest that organizing biosynthesis has the potential to substantially improve performance, but that choosing the appropriate strategy is key. The flexible design-space analysis we propose can be adapted to diverse biosynthetic pathways, and lays a foundation to rationally choose organization strategies for biosynthesis.

Real time quantitative imaging for semiconductor crystal growth, control and characterization

NASA Technical Reports Server (NTRS)

Wargo, Michael J.

1991-01-01

A quantitative real time image processing system has been developed which can be software-reconfigured for semiconductor processing and characterization tasks. In thermal imager mode, 2D temperature distributions of semiconductor melt surfaces (900-1600 C) can be obtained with temperature and spatial resolutions better than 0.5 C and 0.5 mm, respectively, as demonstrated by analysis of melt surface thermal distributions. Temporal and spatial image processing techniques and multitasking computational capabilities convert such thermal imaging into a multimode sensor for crystal growth control. A second configuration of the image processing engine in conjunction with bright and dark field transmission optics is used to nonintrusively determine the microdistribution of free charge carriers and submicron sized crystalline defects in semiconductors. The IR absorption characteristics of wafers are determined with 10-micron spatial resolution and, after calibration, are converted into charge carrier density.

Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process.

PubMed

Ding, Mingtao; He, Lihan; Dunson, David; Carin, Lawrence

2012-12-01

A nonparametric Bayesian model is proposed for segmenting time-evolving multivariate spatial point process data. An inhomogeneous Poisson process is assumed, with a logistic stick-breaking process (LSBP) used to encourage piecewise-constant spatial Poisson intensities. The LSBP explicitly favors spatially contiguous segments, and infers the number of segments based on the observed data. The temporal dynamics of the segmentation and of the Poisson intensities are modeled with exponential correlation in time, implemented in the form of a first-order autoregressive model for uniformly sampled discrete data, and via a Gaussian process with an exponential kernel for general temporal sampling. We consider and compare two different inference techniques: a Markov chain Monte Carlo sampler, which has relatively high computational complexity; and an approximate and efficient variational Bayesian analysis. The model is demonstrated with a simulated example and a real example of space-time crime events in Cincinnati, Ohio, USA.

Spatially Characterizing Effective Timber Supply

NASA Technical Reports Server (NTRS)

Berry, J. K.; Sailor, J.

1982-01-01

The structure of a computer-oriented cartographic model for assessing roundwood supply for generation of base load electricity is discussed. The model provides an analytical procedure for coupling spatial information of harvesting economics and owner willingness to sell stumpages. Supply is characterized in terms of standing timber; of accessibility considering various harvesting and hauling factors; and of availability as affected by ownership and residential patterns. Factors governing accessibility to timber include effective harvesting distance to haulic roads as modified by barriers and slopes. Haul distance is expressed in units that take into account the relative ease of travel along various road types to a central processing facility. Areas of accessible timber are grouped into spatial units, termed 'timbersheds', of common access to particular haul road segments that belong to unique 'transport zones'. Timber availability considerations include size of ownership parcels, housing density and excluded areas. The analysis techniques are demonstrated for a cartographic data base in western Massachusetts.

Investigation of the interpolation method to improve the distributed strain measurement accuracy in optical frequency domain reflectometry systems.

PubMed

Cui, Jiwen; Zhao, Shiyuan; Yang, Di; Ding, Zhenyang

2018-02-20

We use a spectrum interpolation technique to improve the distributed strain measurement accuracy in a Rayleigh-scatter-based optical frequency domain reflectometry sensing system. We demonstrate that strain accuracy is not limited by the "uncertainty principle" that exists in the time-frequency analysis. Different interpolation methods are investigated and used to improve the accuracy of peak position of the cross-correlation and, therefore, improve the accuracy of the strain. Interpolation implemented by padding zeros on one side of the windowed data in the spatial domain, before the inverse fast Fourier transform, is found to have the best accuracy. Using this method, the strain accuracy and resolution are both improved without decreasing the spatial resolution. The strain of 3 μϵ within the spatial resolution of 1 cm at the position of 21.4 m is distinguished, and the measurement uncertainty is 3.3 μϵ.

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

PubMed

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

Measurement of subcellular texture by optical Gabor-like filtering with a digital micromirror device

PubMed Central

Pasternack, Robert M.; Qian, Zhen; Zheng, Jing-Yi; Metaxas, Dimitris N.; White, Eileen; Boustany, Nada N.

2010-01-01

We demonstrate an optical Fourier processing method to quantify object texture arising from subcellular feature orientation within unstained living cells. Using a digital micromirror device as a Fourier spatial filter, we measured cellular responses to two-dimensional optical Gabor-like filters optimized to sense orientation of nonspherical particles, such as mitochondria, with a width around 0.45 μm. Our method showed significantly rounder structures within apoptosis-defective cells lacking the proapoptotic mitochondrial effectors Bax and Bak, when compared with Bax/Bak expressing cells functional for apoptosis, consistent with reported differences in mitochondrial shape in these cells. By decoupling spatial frequency resolution from image resolution, this method enables rapid analysis of nonspherical submicrometer scatterers in an under-sampled large field of view and yields spatially localized morphometric parameters that improve the quantitative assessment of biological function. PMID:18830354

Managing Spatial Selections With Contextual Snapshots

PubMed Central

Mindek, P; Gröller, M E; Bruckner, S

2014-01-01

Spatial selections are a ubiquitous concept in visualization. By localizing particular features, they can be analysed and compared in different views. However, the semantics of such selections often depend on specific parameter settings and it can be difficult to reconstruct them without additional information. In this paper, we present the concept of contextual snapshots as an effective means for managing spatial selections in visualized data. The selections are automatically associated with the context in which they have been created. Contextual snapshots can also be used as the basis for interactive integrated and linked views, which enable in-place investigation and comparison of multiple visual representations of data. Our approach is implemented as a flexible toolkit with well-defined interfaces for integration into existing systems. We demonstrate the power and generality of our techniques by applying them to several distinct scenarios such as the visualization of simulation data, the analysis of historical documents and the display of anatomical data. PMID:25821284

Tethys: A Platform for Water Resources Modeling and Decision Support Apps

NASA Astrophysics Data System (ADS)

Swain, N. R.; Christensen, S. D.; Jones, N.; Nelson, E. J.

2014-12-01

Cloud-based applications or apps are a promising medium through which water resources models and data can be conveyed in a user-friendly environment—making them more accessible to decision-makers and stakeholders. In the context of this work, a water resources web app is a web application that exposes limited modeling functionality for a scenario exploration activity in a structured workflow (e.g.: land use change runoff analysis, snowmelt runoff prediction, and flood potential analysis). The technical expertise required to develop water resources web apps can be a barrier to many potential developers of water resources apps. One challenge that developers face is in providing spatial storage, analysis, and visualization for the spatial data that is inherent to water resources models. The software projects that provide this functionality are non-standard to web development and there are a large number of free and open source software (FOSS) projects to choose from. In addition, it is often required to synthesize several software projects to provide all of the needed functionality. Another challenge for the developer will be orchestrating the use of several software components. Consequently, the initial software development investment required to deploy an effective water resources cloud-based application can be substantial. The Tethys Platform has been developed to lower the technical barrier and minimize the initial development investment that prohibits many scientists and engineers from making use of the web app medium. Tethys synthesizes several software projects including PostGIS for spatial storage, 52°North WPS for spatial analysis, GeoServer for spatial publishing, Google Earth™, Google Maps™ and OpenLayers for spatial visualization, and Highcharts for plotting tabular data. The software selection came after a literature review of software projects being used to create existing earth sciences web apps. All of the software is linked via a Python-powered software development kit (SDK). Tethys developers use the SDK to build their apps and incorporate the needed functionality from the software suite. The presentation will include several apps that have been developed using Tethys to demonstrate its capabilities. Based upon work supported by the National Science Foundation under Grant No. 1135483.

Social stressors and air pollution across New York City communities: a spatial approach for assessing correlations among multiple exposures.

PubMed

Shmool, Jessie L C; Kubzansky, Laura D; Newman, Ogonnaya Dotson; Spengler, John; Shepard, Peggy; Clougherty, Jane E

2014-11-06

Recent toxicological and epidemiological evidence suggests that chronic psychosocial stress may modify pollution effects on health. Thus, there is increasing interest in refined methods for assessing and incorporating non-chemical exposures, including social stressors, into environmental health research, towards identifying whether and how psychosocial stress interacts with chemical exposures to influence health and health disparities. We present a flexible, GIS-based approach for examining spatial patterns within and among a range of social stressors, and their spatial relationships with air pollution, across New York City, towards understanding their combined effects on health. We identified a wide suite of administrative indicators of community-level social stressors (2008-2010), and applied simultaneous autoregressive models and factor analysis to characterize spatial correlations among social stressors, and between social stressors and air pollutants, using New York City Community Air Survey (NYCCAS) data (2008-2009). Finally, we provide an exploratory ecologic analysis evaluating possible modification of the relationship between nitrogen dioxide (NO2) and childhood asthma Emergency Department (ED) visit rates by social stressors, to demonstrate how the methods used to assess stressor exposure (and/or consequent psychosocial stress) may alter model results. Administrative indicators of a range of social stressors (e.g., high crime rate, residential crowding rate) were not consistently correlated (rho = - 0.44 to 0.89), nor were they consistently correlated with indicators of socioeconomic position (rho = - 0.54 to 0.89). Factor analysis using 26 stressor indicators suggested geographically distinct patterns of social stressors, characterized by three factors: violent crime and physical disorder, crowding and poor access to resources, and noise disruption and property crimes. In an exploratory ecologic analysis, these factors were differentially associated with area-average NO2 and childhood asthma ED visits. For example, only the 'violent crime and disorder' factor was significantly associated with asthma ED visits, and only the 'crowding and resource access' factor modified the association between area-level NO2 and asthma ED visits. This spatial approach enabled quantification of complex spatial patterning and confounding between chemical and non-chemical exposures, and can inform study design for epidemiological studies of separate and combined effects of multiple urban exposures.

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

NASA Astrophysics Data System (ADS)

Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin

2016-08-01

Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain-computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI inefficiency to one-fourth in comparison to previous non-adaptive paradigms.

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation.

PubMed

Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin

2016-08-01

In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain-computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI inefficiency to one-fourth in comparison to previous non-adaptive paradigms.

[Cognitive disorders in patients with chronic mercury intoxication].

PubMed

Katamanova, E V; Shevchenko, O I; Lakhman, O L; Denisova, I A

2014-01-01

To assess severity of cognitive disorders in chronic mercury intoxication, the authors performed claster and discrimination analysis of neuropsychologic and neurophysiologic research data from workers exposed to mercury during long length of service, from patients with early and marked stages of chronic mercurial intoxication. Cognitive disorders in chronic mercurial intoxication have three severity degrees, in the light degree disorders patients demonstrate lower amplitude of cognitive evoked potentials, poor long-term memory and associative thinking. Moderate cognitive disorders are characterized by decreased visual, long-term memory, concentration of attention, poor optic and spatial gnosis. Marked cognitive disorders with chronic mercurial intoxication present with more decreased long-term, short-term, picturesque memory, poor intellect, optic and spatial gnosis and associative thinking.

Air-coupled laser vibrometry: analysis and applications.

PubMed

Solodov, Igor; Döring, Daniel; Busse, Gerd

2009-03-01

Acousto-optic interaction between a narrow laser beam and acoustic waves in air is analyzed theoretically. The photoelastic relation in air is used to derive the phase modulation of laser light in air-coupled reflection vibrometry induced by angular spatial spectral components comprising the acoustic beam. Maximum interaction was found for the zero spatial acoustic component propagating normal to the laser beam. The angular dependence of the imaging efficiency is determined for the axial and nonaxial acoustic components with the regard for the laser beam steering in the scanning mode. The sensitivity of air-coupled vibrometry is compared with conventional "Doppler" reflection vibrometry. Applications of the methodology for visualization of linear and nonlinear air-coupled fields are demonstrated.

Spatially dispersive finite-difference time-domain analysis of sub-wavelength imaging by the wire medium slabs

NASA Astrophysics Data System (ADS)

Zhao, Yan; Belov, Pavel A.; Hao, Yang

2006-06-01

In this paper, a spatially dispersive finite-difference time-domain (FDTD) method to model wire media is developed and validated. Sub-wavelength imaging properties of the finite wire medium slabs are examined. It is demonstrated that the slab with its thickness equal to an integer number of half-wavelengths is capable of transporting images with sub-wavelength resolution from one interface of the slab to another. It is also shown that the operation of such transmission devices is not sensitive to their transverse dimensions, which can be made even comparable to the wavelength. In this case, the edge diffractions are negligible and do not disturb the image formation.

Modeling spatially localized photonic nanojets from phase diffraction gratings

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

Geints, Yu. E., E-mail: ygeints@iao.ru; Tomsk State University, 36, Lenina Avenue, Tomsk 634050; Zemlyanov, A. A.

2016-04-21

We investigated numerically the specific spatially localized intense optical structure, a photonic nanojet (PNJ), formed in the near-field scattering of optical radiation at phase diffraction gratings. The finite-difference time-domain technique was employed to study the PNJ key parameters (length, width, focal distance, and intensity) produced by diffraction gratings with the saw-tooth, rectangle, and hemispheric line profiles. Our analysis showed that each type of diffraction gratings produces a photonic jet with unique characteristics. Based on the numerical calculations, we demonstrate that the PNJ could be manipulated in a wide range through the variation of period, duty cycle, and shape of diffractionmore » grating rulings.« less

Uncertainty quantification in flux balance analysis of spatially lumped and distributed models of neuron-astrocyte metabolism.

PubMed

Calvetti, Daniela; Cheng, Yougan; Somersalo, Erkki

2016-12-01

Identifying feasible steady state solutions of a brain energy metabolism model is an inverse problem that allows infinitely many solutions. The characterization of the non-uniqueness, or the uncertainty quantification of the flux balance analysis, is tantamount to identifying the degrees of freedom of the solution. The degrees of freedom of multi-compartment mathematical models for energy metabolism of a neuron-astrocyte complex may offer a key to understand the different ways in which the energetic needs of the brain are met. In this paper we study the uncertainty in the solution, using techniques of linear algebra to identify the degrees of freedom in a lumped model, and Markov chain Monte Carlo methods in its extension to a spatially distributed case. The interpretation of the degrees of freedom in metabolic terms, more specifically, glucose and oxygen partitioning, is then leveraged to derive constraints on the free parameters to guarantee that the model is energetically feasible. We demonstrate how the model can be used to estimate the stoichiometric energy needs of the cells as well as the household energy based on the measured oxidative cerebral metabolic rate of glucose and glutamate cycling. Moreover, our analysis shows that in the lumped model the net direction of lactate dehydrogenase (LDH) in the cells can be deduced from the glucose partitioning between the compartments. The extension of the lumped model to a spatially distributed multi-compartment setting that includes diffusion fluxes from capillary to tissue increases the number of degrees of freedom, requiring the use of statistical sampling techniques. The analysis of the distributed model reveals that some of the conclusions valid for the spatially lumped model, e.g., concerning the LDH activity and glucose partitioning, may no longer hold.

Development of Imaging Fourier-Transform Spectroscopy for the Characterization of Turbulent Jet Flames

DTIC Science & Technology

2014-09-18

Spatially resolved infrared spectra of jet exhaust from an F109 turbofan engine...Appendix E contains a conference proceeding in its entirety [32]. This proceeding summarizes analysis of a turbofan engine exhaust via the Hyper-Cam and...demonstrated in a separate experiment. Recently, exhaust from an F109 turbofan engine was imaged with the IFTS[32]. Examination of the time-averaged

Redrawing the baseline: a method for adjusting biased historical forest estimates using a spatial and temporally representative plot network

Treesearch

Sara A. Goeking; Paul L. Patterson

2015-01-01

Users of Forest Inventory and Analysis (FIA) data sometimes compare historic and current forest inventory estimates, despite warnings that such comparisons may be tenuous. The purpose of this study was to demonstrate a method for obtaining a more accurate and representative reference dataset using data collected at co-located plots (i.e., plots that were measured...

Use of Forest Inventory and Analysis information in wildlife habitat modeling: a process for linking multiple scales

Treesearch

Thomas C. Edwards; Gretchen G. Moisen; Tracey S. Frescino; Joshua L. Lawler

2002-01-01

We describe our collective efforts to develop and apply methods for using FIA data to model forest resources and wildlife habitat. Our work demonstrates how flexible regression techniques, such as generalized additive models, can be linked with spatially explicit environmental information for the mapping of forest type and structure. We illustrate how these maps of...

[Spatial distribution characteristics of the physical and chemical properties of water in the Kunes River after the supply of snowmelt during spring].

PubMed

Liu, Xiang; Guo, Ling-Peng; Zhang, Fei-Yun; Ma, Jie; Mu, Shu-Yong; Zhao, Xin; Li, Lan-Hai

2015-02-01

Eight physical and chemical indicators related to water quality were monitored from nineteen sampling sites along the Kunes River at the end of snowmelt season in spring. To investigate the spatial distribution characteristics of water physical and chemical properties, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) are employed. The result of cluster analysis showed that the Kunes River could be divided into three reaches according to the similarities of water physical and chemical properties among sampling sites, representing the upstream, midstream and downstream of the river, respectively; The result of discriminant analysis demonstrated that the reliability of such a classification was high, and DO, Cl- and BOD5 were the significant indexes leading to this classification; Three principal components were extracted on the basis of the principal component analysis, in which accumulative variance contribution could reach 86.90%. The result of principal component analysis also indicated that water physical and chemical properties were mostly affected by EC, ORP, NO3(-) -N, NH4(+) -N, Cl- and BOD5. The sorted results of principal component scores in each sampling sites showed that the water quality was mainly influenced by DO in upstream, by pH in midstream, and by the rest of indicators in downstream. The order of comprehensive scores for principal components revealed that the water quality degraded from the upstream to downstream, i.e., the upstream had the best water quality, followed by the midstream, while the water quality at downstream was the worst. This result corresponded exactly to the three reaches classified using cluster analysis. Anthropogenic activity and the accumulation of pollutants along the river were probably the main reasons leading to this spatial difference.

Application of the automated spatial surveillance program to birth defects surveillance data.

PubMed

Gardner, Bennett R; Strickland, Matthew J; Correa, Adolfo

2007-07-01

Although many birth defects surveillance programs incorporate georeferenced records into their databases, practical methods for routine spatial surveillance are lacking. We present a macroprogram written for the software package R designed for routine exploratory spatial analysis of birth defects data, the Automated Spatial Surveillance Program (ASSP), and present an application of this program using spina bifida prevalence data for metropolitan Atlanta. Birth defects surveillance data were collected by the Metropolitan Atlanta Congenital Defects Program. We generated ASSP maps for two groups of years that correspond roughly to the periods before (1994-1998) and after (1999-2002) folic acid fortification of flour. ASSP maps display census tract-specific spina bifida prevalence, smoothed prevalence contours, and locations of statistically elevated prevalence. We used these maps to identify areas of elevated prevalence for spina bifida. We identified a large area of potential concern in the years following fortification of grains and cereals with folic acid. This area overlapped census tracts containing large numbers of Hispanic residents. The potential utility of ASSP for spatial disease monitoring was demonstrated by the identification of areas of high prevalence of spina bifida and may warrant further study and monitoring. We intend to further develop ASSP so that it becomes practical for routine spatial monitoring of birth defects. (c) 2007 Wiley-Liss, Inc.

Overlapping MALDI-Mass Spectrometry Imaging for In-Parallel MS and MS/MS Data Acquisition without Sacrificing Spatial Resolution

NASA Astrophysics Data System (ADS)

Hansen, Rebecca L.; Lee, Young Jin

2017-09-01

Metabolomics experiments require chemical identifications, often through MS/MS analysis. In mass spectrometry imaging (MSI), this necessitates running several serial tissue sections or using a multiplex data acquisition method. We have previously developed a multiplex MSI method to obtain MS and MS/MS data in a single experiment to acquire more chemical information in less data acquisition time. In this method, each raster step is composed of several spiral steps and each spiral step is used for a separate scan event (e.g., MS or MS/MS). One main limitation of this method is the loss of spatial resolution as the number of spiral steps increases, limiting its applicability for high-spatial resolution MSI. In this work, we demonstrate multiplex MS imaging is possible without sacrificing spatial resolution by the use of overlapping spiral steps, instead of spatially separated spiral steps as used in the previous work. Significant amounts of matrix and analytes are still left after multiple spectral acquisitions, especially with nanoparticle matrices, so that high quality MS and MS/MS data can be obtained on virtually the same tissue spot. This method was then applied to visualize metabolites and acquire their MS/MS spectra in maize leaf cross-sections at 10 μm spatial resolution. [Figure not available: see fulltext.

Kinematic patterns underlying disguised movements: Spatial and temporal dissimilarity compared to genuine movement patterns.

PubMed

Helm, Fabian; Munzert, Jörn; Troje, Nikolaus F

2017-08-01

This study examined the kinematic characteristics of disguised movements by applying linear discriminant (LDA) and dissimilarity analyses to the motion data from 788 disguised and 792 non-disguised 7-m penalty throws performed by novice and expert handball field players. Results of the LDA showed that discrimination between type of throws (disguised vs. non-disguised) was more error-prone when throws were performed by experts (spatial: 4.6%; temporal: 29.6%) compared to novices (spatial: 1.0%; temporal: 20.2%). The dissimilarity analysis revealed significantly smaller spatial dissimilarities and variations between type of throws in experts compared to novices (p<0.001), but also showed that these spatial dissimilarities and variations increased significantly in both groups the closer the throws came to the moment of (predicted) ball release. In contrast, temporal dissimilarities did not differ significantly between groups. Thus, our data clearly demonstrate that expertise in disguising one's own action intentions results in an ability to perform disguised penalty throws that are highly similar to genuine throws. We suggest that this expertise depends mainly on keeping spatial dissimilarities small. However, the attempt to disguise becomes a challenge the closer one gets to the action outcome (i.e., ball release) becoming visible. Copyright © 2017 Elsevier B.V. All rights reserved.

Creation of Excitons Excited by Light with a Spatial Mode

NASA Astrophysics Data System (ADS)

Syouji, Atsushi; Saito, Shingo; Otomo, Akira

2017-12-01

When light is absorbed into matter, its degrees of freedom (i.e., energy, polarization, and phase) are transferred to the matter and conserved. In this study, we demonstrate that elementary excitations in matter, which are one-photon-forbidden transition states, become allowed states because of the phase conservation across the entire cross section of excitation light. In particular, when 1S orthoexcitons of the yellow series in the semiconductor cuprous oxide (Cu2O) were resonantly excited by light with a spatial mode, an increase in the Γ 3 - -phonon-emission peak intensity of the excitons was detected depending on the spatial mode. Using group-theory-based analysis, we show that the irreducible representation of a one-photon-forbidden exciton, which is one of the orthoexcitons, can be transmuted to an allowed state by taking the direct product with the polar vector produced from the spatial mode of the light. Although the transition process of the exciton is locally characterized by the usual quadrupole interaction, the phase conservation at each position at which the sample is irradiated causes the exciton to be in the same spatial-mode state. That causes a change in the transition selection rule. The selection rule relaxation due to the spatial mode of the light was also applied for paraexciton creation.

Toward optimal spatial and spectral quality in widefield infrared spectromicroscopy of IR labelled single cells.

PubMed

Mattson, Eric C; Unger, Miriam; Clède, Sylvain; Lambert, François; Policar, Clotilde; Imtiaz, Asher; D'Souza, Roshan; Hirschmugl, Carol J

2013-10-07

Advancements in widefield infrared spectromicroscopy have recently been demonstrated following the commissioning of IRENI (InfraRed ENvironmental Imaging), a Fourier Transform infrared (FTIR) chemical imaging beamline at the Synchrotron Radiation Center. The present study demonstrates the effects of magnification, spatial oversampling, spectral pre-processing and deconvolution, focusing on the intracellular detection and distribution of an exogenous metal tris-carbonyl derivative 1 in a single MDA-MB-231 breast cancer cell. We demonstrate here that spatial oversampling for synchrotron-based infrared imaging is critical to obtain accurate diffraction-limited images at all wavelengths simultaneously. Resolution criteria and results from raw and deconvoluted images for two Schwarzschild objectives (36×, NA 0.5 and 74×, NA 0.65) are compared to each other and to prior reports for raster-scanned, confocal microscopes. The resolution of the imaging data can be improved by deconvolving the instrumental broadening that is determined with the measured PSFs, which is implemented with GPU programming architecture for fast hyperspectral processing. High definition, rapidly acquired, FTIR chemical images of respective spectral signatures of the cell 1 and shows that 1 is localized next to the phosphate- and Amide-rich regions, in agreement with previous infrared and luminescence studies. The infrared image contrast, localization and definition are improved after applying proven spectral pre-processing (principal component analysis based noise reduction and RMie scattering correction algorithms) to individual pixel spectra in the hyperspectral cube.

Spatial distribution and cluster analysis of retail drug shop characteristics and antimalarial behaviors as reported by private medicine retailers in western Kenya: informing future interventions.

PubMed

Rusk, Andria; Highfield, Linda; Wilkerson, J Michael; Harrell, Melissa; Obala, Andrew; Amick, Benjamin

2016-02-19

Efforts to improve malaria case management in sub-Saharan Africa have shifted focus to private antimalarial retailers to increase access to appropriate treatment. Demands to decrease intervention cost while increasing efficacy requires interventions tailored to geographic regions with demonstrated need. Cluster analysis presents an opportunity to meet this demand, but has not been applied to the retail sector or antimalarial retailer behaviors. This research conducted cluster analysis on medicine retailer behaviors in Kenya, to improve malaria case management and inform future interventions. Ninety-seven surveys were collected from medicine retailers working in the Webuye Health and Demographic Surveillance Site. Survey items included retailer training, education, antimalarial drug knowledge, recommending behavior, sales, and shop characteristics, and were analyzed using Kulldorff's spatial scan statistic. The Bernoulli purely spatial model for binomial data was used, comparing cases to controls. Statistical significance of found clusters was tested with a likelihood ratio test, using the null hypothesis of no clustering, and a p value based on 999 Monte Carlo simulations. The null hypothesis was rejected with p values of 0.05 or less. A statistically significant cluster of fewer than expected pharmacy-trained retailers was found (RR = .09, p = .001) when compared to the expected random distribution. Drug recommending behavior also yielded a statistically significant cluster, with fewer than expected retailers recommending the correct antimalarial medication to adults (RR = .018, p = .01), and fewer than expected shops selling that medication more often than outdated antimalarials when compared to random distribution (RR = 0.23, p = .007). All three of these clusters were co-located, overlapping in the northwest of the study area. Spatial clustering was found in the data. A concerning amount of correlation was found in one specific region in the study area where multiple behaviors converged in space, highlighting a prime target for interventions. These results also demonstrate the utility of applying geospatial methods in the study of medicine retailer behaviors, making the case for expanding this approach to other regions.

Evaluating metrics of local topographic position for multiscale geomorphometric analysis

NASA Astrophysics Data System (ADS)

Newman, D. R.; Lindsay, J. B.; Cockburn, J. M. H.

2018-07-01

The field of geomorphometry has increasingly moved towards the use of multiscale analytical techniques, due to the availability of fine-resolution digital elevation models (DEMs) and the inherent scale-dependency of many DEM-derived attributes such as local topographic position (LTP). LTP is useful for landform and soils mapping and numerous other environmental applications. Multiple LTP metrics have been proposed and applied in the literature; however, elevation percentile (EP) is notable for its robustness to elevation error and applicability to non-Gaussian local elevation distributions, both of which are common characteristics of DEM data sets. Multiscale LTP analysis involves the estimation of spatial patterns using a range of neighborhood sizes, traditionally achieved by applying spatial filtering techniques with varying kernel sizes. While EP can be demonstrated to provide accurate estimates of LTP, the computationally intensive method of its calculation makes it unsuited to multiscale LTP analysis, particularly at large neighborhood sizes or with fine-resolution DEMs. This research assessed the suitability of three LTP metrics for multiscale terrain characterization by quantifying their computational efficiency and by comparing their ability to approximate EP spatial patterns under varying topographic conditions. The tested LTP metrics included: deviation from mean elevation (DEV), percent elevation range (PER), and the novel relative topographic position (RTP) index. The results demonstrated that DEV, calculated using the integral image technique, offers fast and scale-invariant computation. DEV spatial patterns were strongly correlated with EP (r2 range of 0.699 to 0.967) under all tested topographic conditions. RTP was also a strong predictor of EP (r2 range of 0.594 to 0.917). PER was the weakest predictor of EP (r2 range of 0.031 to 0.801) without offering a substantial improvement in computational efficiency over RTP. PER was therefore determined to be unsuitable for most multiscale applications. It was concluded that the scale-invariant property offered by the integral image used by the DEV method counters the minor losses in robustness compared to EP, making DEV the optimal LTP metric for multiscale applications.

Local indicators of geocoding accuracy (LIGA): theory and application

PubMed Central

Jacquez, Geoffrey M; Rommel, Robert

2009-01-01

Background Although sources of positional error in geographic locations (e.g. geocoding error) used for describing and modeling spatial patterns are widely acknowledged, research on how such error impacts the statistical results has been limited. In this paper we explore techniques for quantifying the perturbability of spatial weights to different specifications of positional error. Results We find that a family of curves describes the relationship between perturbability and positional error, and use these curves to evaluate sensitivity of alternative spatial weight specifications to positional error both globally (when all locations are considered simultaneously) and locally (to identify those locations that would benefit most from increased geocoding accuracy). We evaluate the approach in simulation studies, and demonstrate it using a case-control study of bladder cancer in south-eastern Michigan. Conclusion Three results are significant. First, the shape of the probability distributions of positional error (e.g. circular, elliptical, cross) has little impact on the perturbability of spatial weights, which instead depends on the mean positional error. Second, our methodology allows researchers to evaluate the sensitivity of spatial statistics to positional accuracy for specific geographies. This has substantial practical implications since it makes possible routine sensitivity analysis of spatial statistics to positional error arising in geocoded street addresses, global positioning systems, LIDAR and other geographic data. Third, those locations with high perturbability (most sensitive to positional error) and high leverage (that contribute the most to the spatial weight being considered) will benefit the most from increased positional accuracy. These are rapidly identified using a new visualization tool we call the LIGA scatterplot. Herein lies a paradox for spatial analysis: For a given level of positional error increasing sample density to more accurately follow the underlying population distribution increases perturbability and introduces error into the spatial weights matrix. In some studies positional error may not impact the statistical results, and in others it might invalidate the results. We therefore must understand the relationships between positional accuracy and the perturbability of the spatial weights in order to have confidence in a study's results. PMID:19863795

Application of spatially modulated near-infrared structured light to study changes in optical properties of mouse brain tissue during heatstress.

PubMed

Shaul, Oren; Fanrazi-Kahana, Michal; Meitav, Omri; Pinhasi, Gad A; Abookasis, David

2017-11-10

Heat stress (HS) is a medical emergency defined by abnormally elevated body temperature that causes biochemical, physiological, and hematological changes. The goal of the present research was to detect variations in optical properties (absorption, reduced scattering, and refractive index coefficients) of mouse brain tissue during HS by using near-infrared (NIR) spatial light modulation. NIR spatial patterns with different spatial phases were used to differentiate the effects of tissue scattering from those of absorption. Decoupling optical scattering from absorption enabled the quantification of a tissue's chemical constituents (related to light absorption) and structural properties (related to light scattering). Technically, structured light patterns at low and high spatial frequencies of six wavelengths ranging between 690 and 970 nm were projected onto the mouse scalp surface while diffuse reflected light was recorded by a CCD camera positioned perpendicular to the mouse scalp. Concurrently to pattern projection, brain temperature was measured with a thermal camera positioned slightly off angle from the mouse head while core body temperature was monitored by thermocouple probe. Data analysis demonstrated variations from baseline measurements in a battery of intrinsic brain properties following HS.

Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

NASA Astrophysics Data System (ADS)

Gruber, A.; Crow, W. T.; Dorigo, W. A.

2018-02-01

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

A scoping review of spatial cluster analysis techniques for point-event data.

PubMed

Fritz, Charles E; Schuurman, Nadine; Robertson, Colin; Lear, Scott

2013-05-01

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

Nanoscale Characterization of Carrier Dynamic and Surface Passivation in InGaN/GaN Multiple Quantum Wells on GaN Nanorods.

PubMed

Chen, Weijian; Wen, Xiaoming; Latzel, Michael; Heilmann, Martin; Yang, Jianfeng; Dai, Xi; Huang, Shujuan; Shrestha, Santosh; Patterson, Robert; Christiansen, Silke; Conibeer, Gavin

2016-11-23

Using advanced two-photon excitation confocal microscopy, associated with time-resolved spectroscopy, we characterize InGaN/GaN multiple quantum wells on nanorod heterostructures and demonstrate the passivation effect of a KOH treatment. High-quality InGaN/GaN nanorods were fabricated using nanosphere lithography as a candidate material for light-emitting diode devices. The depth- and time-resolved characterization at the nanoscale provides detailed carrier dynamic analysis helpful for understanding the optical properties. The nanoscale spatially resolved images of InGaN quantum well and defects were acquired simultaneously. We demonstrate that nanorod etching improves light extraction efficiency, and a proper KOH treatment has been found to reduce the surface defects efficiently and enhance the luminescence. The optical characterization techniques provide depth-resolved and time-resolved carrier dynamics with nanoscale spatially resolved mapping, which is crucial for a comprehensive and thorough understanding of nanostructured materials and provides novel insight into the improvement of materials fabrication and applications.

Diffraction based Hanbury Brown and Twiss interferometry at a hard x-ray free-electron laser

DOE PAGES

Gorobtsov, O. Yu.; Mukharamova, N.; Lazarev, S.; ...

2018-02-02

X-ray free-electron lasers (XFELs) provide extremely bright and highly spatially coherent x-ray radiation with femtosecond pulse duration. Currently, they are widely used in biology and material science. Knowledge of the XFEL statistical properties during an experiment may be vitally important for the accurate interpretation of the results. Here, for the first time, we demonstrate Hanbury Brown and Twiss (HBT) interferometry performed in diffraction mode at an XFEL source. It allowed us to determine the XFEL statistical properties directly from the Bragg peaks originating from colloidal crystals. This approach is different from the traditional one when HBT interferometry is performed inmore » the direct beam without a sample. Our analysis has demonstrated nearly full (80%) global spatial coherence of the XFEL pulses and an average pulse duration on the order of ten femtoseconds for the monochromatized beam, which is significantly shorter than expected from the electron bunch measurements.« less

Diffraction based Hanbury Brown and Twiss interferometry at a hard x-ray free-electron laser

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

Gorobtsov, O. Yu.; Mukharamova, N.; Lazarev, S.

X-ray free-electron lasers (XFELs) provide extremely bright and highly spatially coherent x-ray radiation with femtosecond pulse duration. Currently, they are widely used in biology and material science. Knowledge of the XFEL statistical properties during an experiment may be vitally important for the accurate interpretation of the results. Here, for the first time, we demonstrate Hanbury Brown and Twiss (HBT) interferometry performed in diffraction mode at an XFEL source. It allowed us to determine the XFEL statistical properties directly from the Bragg peaks originating from colloidal crystals. This approach is different from the traditional one when HBT interferometry is performed inmore » the direct beam without a sample. Our analysis has demonstrated nearly full (80%) global spatial coherence of the XFEL pulses and an average pulse duration on the order of ten femtoseconds for the monochromatized beam, which is significantly shorter than expected from the electron bunch measurements.« less

Frequency-domain nonlinear optics in two-dimensionally patterned quasi-phase-matching media.

PubMed

Phillips, C R; Mayer, B W; Gallmann, L; Keller, U

2016-07-11

Advances in the amplification and manipulation of ultrashort laser pulses have led to revolutions in several areas. Examples include chirped pulse amplification for generating high peak-power lasers, power-scalable amplification techniques, pulse shaping via modulation of spatially-dispersed laser pulses, and efficient frequency-mixing in quasi-phase-matched nonlinear crystals to access new spectral regions. In this work, we introduce and demonstrate a new platform for nonlinear optics which has the potential to combine these separate functionalities (pulse amplification, frequency transfer, and pulse shaping) into a single monolithic device that is bandwidth- and power-scalable. The approach is based on two-dimensional (2D) patterning of quasi-phase-matching (QPM) gratings combined with optical parametric interactions involving spatially dispersed laser pulses. Our proof of principle experiment demonstrates this technique via mid-infrared optical parametric chirped pulse amplification of few-cycle pulses. Additionally, we present a detailed theoretical and numerical analysis of such 2D-QPM devices and how they can be designed.

TEMPLATES: Targeting Extremely Magnified Panchromatic Lensed Arcs and Their Extended Star Formation

NASA Astrophysics Data System (ADS)

Rigby, Jane; Vieira, Joaquin; Bayliss, M.; Fischer, T.; Florian, M.; Gladders, M.; Gonzalez, A.; Law, D.; Marrone, D.; Phadke, K.; Sharon, K.; Spilker, J.

2017-11-01

We propose high signal-to-noise NIRSpec and MIRI IFU spectroscopy, with accompanying imaging, for 4 gravitationally lensed galaxies at 1

Two-mode elliptical-core weighted fiber sensors for vibration analysis

NASA Technical Reports Server (NTRS)

Vengsarkar, Ashish M.; Murphy, Kent A.; Fogg, Brian R.; Miller, William V.; Greene, Jonathan A.; Claus, Richard O.

1992-01-01

Two-mode, elliptical-core optical fibers are demonstrated in weighted, distributed and selective vibration-mode-filtering applications. We show how appropriate placement of optical fibers on a vibrating structure can lead to vibration mode filtering. Selective vibration-mode suppression on the order of 10 dB has been obtained using tapered two-mode, circular-core fibers with tapering functions that match the second derivatives of the modes of vibration to be enhanced. We also demonstrate the use of chirped, two-mode gratings in fibers as spatial modal sensors that are equivalents of shaped piezoelectric sensors.

Multi-criteria decision analysis in conservation planning: Designing conservation area networks in San Diego County

NASA Astrophysics Data System (ADS)

MacDonald, Garrick Richard

To limit biodiversity loss caused by human activity, conservation planning must protect biodiversity while considering socio-economic cost criteria. This research aimed to determine the effects of socio-economic criteria and spatial configurations on the development of CANs for three species with different distribution patterns, while simultaneously attempting to address the uncertainty and sensitivity of CANs produced by ConsNet. The socio-economic factors and spatial criteria included the cost of land, population density, agricultural output value, area, average cluster area, number of clusters, shape, and perimeter. Three sensitive mammal species with different distribution patterns were selected and included the Bobcat, Ringtail, and a custom created mammal distribution. Forty problems and the corresponding number of CANs were formulated and computed by running each predicted presence species model with and without the four different socioeconomic threshold groups at two different resolutions. Thirty-two percent less area was conserved after considering multiple socio-economic constraints and spatial configurations in comparison to CANs that did not consider multiple socio-economic constraints and spatial configurations. Without including socio-economic costs, ConsNet's ALL_CELLS heuristic solution was the highest ranking CAN. After considering multiple socio-economic costs, the number one ranking CAN was no longer the ALL_CELLS heuristic solution, but a spatially different meta-heuristic solution. The effects of multiple constraints and objectives on the design of CANs with different distribution patterns did not vary significantly across the criteria. The CANs produced by ConsNet appeared to demonstrate some uncertainty surrounding particular criteria, but did not demonstrate substantial uncertainty across all criteria used to rank the CANs. Similarly, the range of socio-economic criteria thresholds did not have a substantial impact. ConsNet was very applicable to the research project, however, it did exhibit a few limitations. Both the advantages and disadvantages of ConsNet should be considered before using ConsNet for future conservation planning projects. The research project is an example of a large data scenario undertaken with a multiple criteria decision analysis (MCDA) approach.

Spatial distribution and influence factors of interprovincial terrestrial physical geographical names in China

NASA Astrophysics Data System (ADS)

Zhang, S.; Wang, Y.; Ju, H.

2017-12-01

The interprovincial terrestrial physical geographical entities are the key areas of regional integrated management. Based on toponomy dictionaries and different thematic maps, the attributes and the spatial extent of the interprovincial terrestrial physical geographical names (ITPGN, including terrain ITPGN and water ITPGN) were extracted. The coefficient of variation and Moran's I were combined together to measure the spatial variation and spatial association of ITPGN. The influencing factors of the distribution of ITPGN and the implications for the regional management were further discussed. The results showed that 11325 ITPGN were extracted, including 7082 terrain ITPGN and 4243 water ITPGN. Hunan Province had the largest number of ITPGN in China, and Shanghai had the smallest number. The spatial variance of the terrain ITPGN was larger than that of the water ITPGN, and the ITPGN showed a significant agglomeration phenomenon in the southern part of China. Further analysis showed that the number of ITPGN was positively related with the relative elevation and the population where the relative elevation was lower than 2000m and the population was less than 50 million. But the number of ITPGN showed a negative relationship with the two factors when their values became larger, indicating a large number of unnamed entities existed in complex terrain areas and a decreasing number of terrestrial physical geographical entities in densely populated area. Based on these analysis, we suggest the government take the ITPGN as management units to realize a balance development between different parts of the entities and strengthen the geographical names census and the nomination of unnamed interprovincial physical geographical entities. This study also demonstrated that the methods of literature survey, coefficient of variation and Moran's I can be combined to enhance the understanding of the spatial pattern of ITPGN.

Processing and statistical analysis of soil-root images

NASA Astrophysics Data System (ADS)

Razavi, Bahar S.; Hoang, Duyen; Kuzyakov, Yakov

2016-04-01

Importance of the hotspots such as rhizosphere, the small soil volume that surrounds and is influenced by plant roots, calls for spatially explicit methods to visualize distribution of microbial activities in this active site (Kuzyakov and Blagodatskaya, 2015). Zymography technique has previously been adapted to visualize the spatial dynamics of enzyme activities in rhizosphere (Spohn and Kuzyakov, 2014). Following further developing of soil zymography -to obtain a higher resolution of enzyme activities - we aimed to 1) quantify the images, 2) determine whether the pattern (e.g. distribution of hotspots in space) is clumped (aggregated) or regular (dispersed). To this end, we incubated soil-filled rhizoboxes with maize Zea mays L. and without maize (control box) for two weeks. In situ soil zymography was applied to visualize enzymatic activity of β-glucosidase and phosphatase at soil-root interface. Spatial resolution of fluorescent images was improved by direct application of a substrate saturated membrane to the soil-root system. Furthermore, we applied "spatial point pattern analysis" to determine whether the pattern (e.g. distribution of hotspots in space) is clumped (aggregated) or regular (dispersed). Our results demonstrated that distribution of hotspots at rhizosphere is clumped (aggregated) compare to control box without plant which showed regular (dispersed) pattern. These patterns were similar in all three replicates and for both enzymes. We conclude that improved zymography is promising in situ technique to identify, analyze, visualize and quantify spatial distribution of enzyme activities in the rhizosphere. Moreover, such different patterns should be considered in assessments and modeling of rhizosphere extension and the corresponding effects on soil properties and functions. Key words: rhizosphere, spatial point pattern, enzyme activity, zymography, maize.

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

NASA Astrophysics Data System (ADS)

Yi, Yonghong; Kimball, John S.; Chen, Richard H.; Moghaddam, Mahta; Reichle, Rolf H.; Mishra, Umakant; Zona, Donatella; Oechel, Walter C.

2018-01-01

An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modeling and analysis framework combining field observations, local-scale ( ˜ 50 m resolution) active layer thickness (ALT) and soil moisture maps derived from low-frequency (L + P-band) airborne radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modeled ALT results show good correspondence with in situ measurements in higher-permafrost-probability (PP ≥ 70 %) areas (n = 33; R = 0.60; mean bias = 1.58 cm; RMSE = 20.32 cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend = 0.32±1.18 cm yr-1) and much larger increases (> 3 cm yr-1) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R = 0.60 ± 0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modeling framework across a larger domain.

GIS, geostatistics, metadata banking, and tree-based models for data analysis and mapping in environmental monitoring and epidemiology.

PubMed

Schröder, Winfried

2006-05-01

By the example of environmental monitoring, some applications of geographic information systems (GIS), geostatistics, metadata banking, and Classification and Regression Trees (CART) are presented. These tools are recommended for mapping statistically estimated hot spots of vectors and pathogens. GIS were introduced as tools for spatially modelling the real world. The modelling can be done by mapping objects according to the spatial information content of data. Additionally, this can be supported by geostatistical and multivariate statistical modelling. This is demonstrated by the example of modelling marine habitats of benthic communities and of terrestrial ecoregions. Such ecoregionalisations may be used to predict phenomena based on the statistical relation between measurements of an interesting phenomenon such as, e.g., the incidence of medically relevant species and correlated characteristics of the ecoregions. The combination of meteorological data and data on plant phenology can enhance the spatial resolution of the information on climate change. To this end, meteorological and phenological data have to be correlated. To enable this, both data sets which are from disparate monitoring networks have to be spatially connected by means of geostatistical estimation. This is demonstrated by the example of transformation of site-specific data on plant phenology into surface data. The analysis allows for spatial comparison of the phenology during the two periods 1961-1990 and 1991-2002 covering whole Germany. The changes in both plant phenology and air temperature were proved to be statistically significant. Thus, they can be combined by GIS overlay technique to enhance the spatial resolution of the information on the climate change and use them for the prediction of vector incidences at the regional scale. The localisation of such risk hot spots can be done by geometrically merging surface data on promoting factors. This is demonstrated by the example of the transfer of heavy metals through soils. The predicted hot spots of heavy metal transfer can be validated empirically by measurement data which can be inquired by a metadata base linked with a geographic information system. A corresponding strategy for the detection of vector hot spots in medical epidemiology is recommended. Data on incidences and habitats of the Anophelinae in the marsh regions of Lower Saxony (Germany) were used to calculate a habitat model by CART, which together with climate data and data on ecoregions can be further used for the prediction of habitats of medically relevant vector species. In the future, this approach should be supported by an internet-based information system consisting of three components: metadata questionnaire, metadata base, and GIS to link metadata, surface data, and measurement data on incidences and habitats of medically relevant species and related data on climate, phenology, and ecoregional characteristic conditions.

Design and implementation of spatial knowledge grid for integrated spatial analysis

NASA Astrophysics Data System (ADS)

Liu, Xiangnan; Guan, Li; Wang, Ping

2006-10-01

Supported by spatial information grid(SIG), the spatial knowledge grid (SKG) for integrated spatial analysis utilizes the middleware technology in constructing the spatial information grid computation environment and spatial information service system, develops spatial entity oriented spatial data organization technology, carries out the profound computation of the spatial structure and spatial process pattern on the basis of Grid GIS infrastructure, spatial data grid and spatial information grid (specialized definition). At the same time, it realizes the complex spatial pattern expression and the spatial function process simulation by taking the spatial intelligent agent as the core to establish space initiative computation. Moreover through the establishment of virtual geographical environment with man-machine interactivity and blending, complex spatial modeling, network cooperation work and spatial community decision knowledge driven are achieved. The framework of SKG is discussed systematically in this paper. Its implement flow and the key technology with examples of overlay analysis are proposed as well.

Spatial analysis on future housing markets: economic development and housing implications.

PubMed

Liu, Xin; Wang, Lizhe

2014-01-01

A coupled projection method combining formal modelling and other statistical techniques was developed to delineate the relationship between economic and social drivers for net new housing allocations. Using the example of employment growth in Tyne and Wear, UK, until 2016, the empirical analysis yields housing projections at the macro- and microspatial levels (e.g., region to subregion to elected ward levels). The results have important implications for the strategic planning of locations for housing and employment, demonstrating both intuitively and quantitatively how local economic developments affect housing demand.

Dilational symmetry-breaking in thermodynamics

NASA Astrophysics Data System (ADS)

Lin, Chris L.; Ordóñez, Carlos R.

2017-04-01

Using thermodynamic relations and dimensional analysis we derive a general formula for the thermodynamical trace 2{ E}-DP for nonrelativistic systems and { E}-DP for relativistic systems, where D is the number of spatial dimensions, in terms of the microscopic scales of the system within the grand canonical ensemble. We demonstrate the formula for several cases, including anomalous systems which develop scales through dimensional transmutation. Using this relation, we make explicit the connection between dimensional analysis and the virial theorem. This paper is focused mainly on the non-relativistic aspects of this relation.

Signature of phase singularities in diffusive regimes in disordered waveguide lattices: interplay and qualitative analysis

NASA Astrophysics Data System (ADS)

Ghosh, Somnath

2018-05-01

Co-existence and interplay between mesoscopic light dynamics with singular optics in spatially random but temporally coherent disordered waveguide lattices is reported. Two CW light beams of 1.55 micron operating wavelength are launched as inputs to 1D waveguide lattices with controllable weak disorder in refractive index profile. Direct observation of phase singularities in the speckle pattern along the length is numerically demonstrated. Quantitative analysis of onset of such singular behavior and diffusive wave propagation is analyzed for the first time.

Spatial Analysis on Future Housing Markets: Economic Development and Housing Implications

PubMed Central

Liu, Xin; Wang, Lizhe

2014-01-01

A coupled projection method combining formal modelling and other statistical techniques was developed to delineate the relationship between economic and social drivers for net new housing allocations. Using the example of employment growth in Tyne and Wear, UK, until 2016, the empirical analysis yields housing projections at the macro- and microspatial levels (e.g., region to subregion to elected ward levels). The results have important implications for the strategic planning of locations for housing and employment, demonstrating both intuitively and quantitatively how local economic developments affect housing demand. PMID:24892097

In vivo characterization of a reporter gene system for imaging hypoxia-induced gene expression.

PubMed

Carlin, Sean; Pugachev, Andrei; Sun, Xiaorong; Burke, Sean; Claus, Filip; O'Donoghue, Joseph; Ling, C Clifton; Humm, John L

2009-10-01

To characterize a tumor model containing a hypoxia-inducible reporter gene and to demonstrate utility by comparison of reporter gene expression to the uptake and distribution of the hypoxia tracer (18)F-fluoromisonidazole ((18)F-FMISO). Three tumors derived from the rat prostate cancer cell line R3327-AT were grown in each of two rats as follows: (1) parental R3327-AT, (2) positive control R3327-AT/PC in which the HSV1-tkeGFP fusion reporter gene was expressed constitutively, (3) R3327-AT/HRE in which the reporter gene was placed under the control of a hypoxia-inducible factor-responsive promoter sequence (HRE). Animals were coadministered a hypoxia-specific marker (pimonidazole) and the reporter gene probe (124)I-2'-fluoro-2'-deoxy-1-beta-d-arabinofuranosyl-5-iodouracil ((124)I-FIAU) 3 h prior to sacrifice. Statistical analysis of the spatial association between (124)I-FIAU uptake and pimonidazole fluorescent staining intensity was then performed on a pixel-by-pixel basis. Utility of this system was demonstrated by assessment of reporter gene expression versus the exogenous hypoxia probe (18)F-FMISO. Two rats, each bearing a single R3327-AT/HRE tumor, were injected with (124)I-FIAU (3 h before sacrifice) and (18)F-FMISO (2 h before sacrifice). Statistical analysis of the spatial association between (18)F-FMISO and (124)I-FIAU on a pixel-by-pixel basis was performed. Correlation coefficients between (124)I-FIAU uptake and pimonidazole staining intensity were: 0.11 in R3327-AT tumors, -0.66 in R3327-AT/PC and 0.76 in R3327-AT/HRE, confirming that only in the R3327-AT/HRE tumor was HSV1-tkeGFP gene expression associated with hypoxia. Correlation coefficients between (18)F-FMISO and (124)I-FIAU uptakes in R3327-AT/HRE tumors were r=0.56, demonstrating good spatial correspondence between the two tracers. We have confirmed hypoxia-specific expression of the HSV1-tkeGFP fusion gene in the R3327-AT/HRE tumor model and demonstrated the utility of this model for the evaluation of radiolabeled hypoxia tracers.

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

Li, Ke; Chen, Guang-Hong, E-mail: gchen7@wisc.edu; Garrett, John

Purpose: Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks. Methods: The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDI{sub vol} =4, 8, 12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIRmore » (Veo{sup ®}, GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d′. Results: (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than FBP (and vice versa); the value of this transitional contrast highly depended on the dose level. (3) The PSFs of MBIR could be approximated as Gaussian functions with reasonably good accuracy. (4) Thez resolution of MBIR showed similar contrast and dose dependence. (5) Noise standard deviation assessed on the edges of objects demonstrated a trade-off with spatial resolution in MBIR. (5) When both spatial resolution and image noise were considered using the CHO analysis, MBIR led to significant improvement in the overall CT image quality for both high and low contrast detection tasks at both standard and low dose levels. Conclusions: Due to the intrinsic nonlinearity of the MBIR method, many well-known CT spatial resolution and noise properties have been modified. In particular, dose dependence and contrast dependence have been introduced to the spatial resolution of CT images by MBIR. The method has also introduced some novel noise-resolution trade-off not seen in traditional CT images. While the benefits of MBIR regarding the overall image quality, as demonstrated in this work, are significant, the optimal use of this method in clinical practice demands a thorough understanding of its unique physical characteristics.« less

Structure-preserving interpolation of temporal and spatial image sequences using an optical flow-based method.

PubMed

Ehrhardt, J; Säring, D; Handels, H

2007-01-01

Modern tomographic imaging devices enable the acquisition of spatial and temporal image sequences. But, the spatial and temporal resolution of such devices is limited and therefore image interpolation techniques are needed to represent images at a desired level of discretization. This paper presents a method for structure-preserving interpolation between neighboring slices in temporal or spatial image sequences. In a first step, the spatiotemporal velocity field between image slices is determined using an optical flow-based registration method in order to establish spatial correspondence between adjacent slices. An iterative algorithm is applied using the spatial and temporal image derivatives and a spatiotemporal smoothing step. Afterwards, the calculated velocity field is used to generate an interpolated image at the desired time by averaging intensities between corresponding points. Three quantitative measures are defined to evaluate the performance of the interpolation method. The behavior and capability of the algorithm is demonstrated by synthetic images. A population of 17 temporal and spatial image sequences are utilized to compare the optical flow-based interpolation method to linear and shape-based interpolation. The quantitative results show that the optical flow-based method outperforms the linear and shape-based interpolation statistically significantly. The interpolation method presented is able to generate image sequences with appropriate spatial or temporal resolution needed for image comparison, analysis or visualization tasks. Quantitative and qualitative measures extracted from synthetic phantoms and medical image data show that the new method definitely has advantages over linear and shape-based interpolation.

Demonstration of Wavelet Techniques in the Spectral Analysis of Bypass Transition Data

NASA Technical Reports Server (NTRS)

Lewalle, Jacques; Ashpis, David E.; Sohn, Ki-Hyeon

1997-01-01

A number of wavelet-based techniques for the analysis of experimental data are developed and illustrated. A multiscale analysis based on the Mexican hat wavelet is demonstrated as a tool for acquiring physical and quantitative information not obtainable by standard signal analysis methods. Experimental data for the analysis came from simultaneous hot-wire velocity traces in a bypass transition of the boundary layer on a heated flat plate. A pair of traces (two components of velocity) at one location was excerpted. A number of ensemble and conditional statistics related to dominant time scales for energy and momentum transport were calculated. The analysis revealed a lack of energy-dominant time scales inside turbulent spots but identified transport-dominant scales inside spots that account for the largest part of the Reynolds stress. Momentum transport was much more intermittent than were energetic fluctuations. This work is the first step in a continuing study of the spatial evolution of these scale-related statistics, the goal being to apply the multiscale analysis results to improve the modeling of transitional and turbulent industrial flows.

A hierarchical approach to forest landscape pattern characterization.

PubMed

Wang, Jialing; Yang, Xiaojun

2012-01-01

Landscape spatial patterns have increasingly been considered to be essential for environmental planning and resources management. In this study, we proposed a hierarchical approach for landscape classification and evaluation by characterizing landscape spatial patterns across different hierarchical levels. The case study site is the Red Hills region of northern Florida and southwestern Georgia, well known for its biodiversity, historic resources, and scenic beauty. We used one Landsat Enhanced Thematic Mapper image to extract land-use/-cover information. Then, we employed principal-component analysis to help identify key class-level landscape metrics for forests at different hierarchical levels, namely, open pine, upland pine, and forest as a whole. We found that the key class-level landscape metrics varied across different hierarchical levels. Compared with forest as a whole, open pine forest is much more fragmented. The landscape metric, such as CONTIG_MN, which measures whether pine patches are contiguous or not, is more important to characterize the spatial pattern of pine forest than to forest as a whole. This suggests that different metric sets should be used to characterize landscape patterns at different hierarchical levels. We further used these key metrics, along with the total class area, to classify and evaluate subwatersheds through cluster analysis. This study demonstrates a promising approach that can be used to integrate spatial patterns and processes for hierarchical forest landscape planning and management.

Real-time 2D spatially selective MRI experiments: Comparative analysis of optimal control design methods

NASA Astrophysics Data System (ADS)

Maximov, Ivan I.; Vinding, Mads S.; Tse, Desmond H. Y.; Nielsen, Niels Chr.; Shah, N. Jon

2015-05-01

There is an increasing need for development of advanced radio-frequency (RF) pulse techniques in modern magnetic resonance imaging (MRI) systems driven by recent advancements in ultra-high magnetic field systems, new parallel transmit/receive coil designs, and accessible powerful computational facilities. 2D spatially selective RF pulses are an example of advanced pulses that have many applications of clinical relevance, e.g., reduced field of view imaging, and MR spectroscopy. The 2D spatially selective RF pulses are mostly generated and optimised with numerical methods that can handle vast controls and multiple constraints. With this study we aim at demonstrating that numerical, optimal control (OC) algorithms are efficient for the design of 2D spatially selective MRI experiments, when robustness towards e.g. field inhomogeneity is in focus. We have chosen three popular OC algorithms; two which are gradient-based, concurrent methods using first- and second-order derivatives, respectively; and a third that belongs to the sequential, monotonically convergent family. We used two experimental models: a water phantom, and an in vivo human head. Taking into consideration the challenging experimental setup, our analysis suggests the use of the sequential, monotonic approach and the second-order gradient-based approach as computational speed, experimental robustness, and image quality is key. All algorithms used in this work were implemented in the MATLAB environment and are freely available to the MRI community.

PDNAsite: Identification of DNA-binding Site from Protein Sequence by Incorporating Spatial and Sequence Context

PubMed Central

Zhou, Jiyun; Xu, Ruifeng; He, Yulan; Lu, Qin; Wang, Hongpeng; Kong, Bing

2016-01-01

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community. PMID:27282833

Statistical analysis of mesoscale rainfall: Dependence of a random cascade generator on large-scale forcing

NASA Technical Reports Server (NTRS)

Over, Thomas, M.; Gupta, Vijay K.

1994-01-01

Under the theory of independent and identically distributed random cascades, the probability distribution of the cascade generator determines the spatial and the ensemble properties of spatial rainfall. Three sets of radar-derived rainfall data in space and time are analyzed to estimate the probability distribution of the generator. A detailed comparison between instantaneous scans of spatial rainfall and simulated cascades using the scaling properties of the marginal moments is carried out. This comparison highlights important similarities and differences between the data and the random cascade theory. Differences are quantified and measured for the three datasets. Evidence is presented to show that the scaling properties of the rainfall can be captured to the first order by a random cascade with a single parameter. The dependence of this parameter on forcing by the large-scale meteorological conditions, as measured by the large-scale spatial average rain rate, is investigated for these three datasets. The data show that this dependence can be captured by a one-to-one function. Since the large-scale average rain rate can be diagnosed from the large-scale dynamics, this relationship demonstrates an important linkage between the large-scale atmospheric dynamics and the statistical cascade theory of mesoscale rainfall. Potential application of this research to parameterization of runoff from the land surface and regional flood frequency analysis is briefly discussed, and open problems for further research are presented.

Examining Enjoyment of Casual Videogames.

PubMed

Shafer, Daniel M; Carbonara, Corey P

2015-12-01

This study investigated the processes leading to enjoyment of casual videogames on both mobile devices and console systems. Building upon a foundation in mental models theory and the psychology of play, the study focuses on how performance and experience-based variables impact enjoyment of casual videogames played on mobile devices and console devices. The grounding assumption of this research is that playing videogames produces enjoyment that contributes to mental health in the form of a brief distraction from the stress of daily life, social connections with family and friends through casual gameplay, and, in some cases, a compelling reason to engage in physical activity. A student sample of players (n=363) played a variety of casual games on mobile (iPad(®) or iPod(®) Touch(®) [Apple, Cupertino, CA]) or console (Wii™ [Nintendo, Japan], Xbox(®) 360 Kinect(®) [Microsoft, Redmond, WA], or PS3™ Move [Sony, Tokyo, Japan]) platforms. They then answered a questionnaire assessing their evaluations of the game's interactivity level, their sense of spatial presence in the game, their perception of the game's realism, and, most importantly, their enjoyment. Path analysis demonstrated the interrelationships among these variables. The results show that spatial presence is a powerful predictor of videogame enjoyment for both console- and mobile-based casual games. Patterns of prediction for games on each platform, as demonstrated using path analysis, were similar and aligned with predictions based on mental models and the psychology of play. The psychological theory of play and the mental models perspective offer firm theoretical grounds for understanding how enjoyment is wrought in the process of playing casual games. The relationships among interactivity, spatial presence, perceived reality, and enjoyment hold for games played on handheld or console devices. Furthermore, this study is one of the first to demonstrate these relationships and test them simultaneously, breaking new ground in research on game enjoyment.

Implicit representations of space after bilateral parietal lobe damage.

PubMed

Kim, M S; Robertson, L C

2001-11-15

There is substantial evidence that the primate cortex is grossly divided into two functional streams, an occipital-parietal-frontal pathway that processes "where" and an occipital-temporal-frontal pathway that processes "what" (Ungerleider and Mishkin, 1982). In humans, bilateral occipital-parietal damage results in severe spatial deficits and a neuropsychological disorder known as Balint's syndrome in which a single object can be perceived (simultanagnosia) but its location is unknown (Balint, 1995). The data reported here demonstrate that spatial information for visual features that cannot be explicitly located is represented normally below the level of spatial awareness even with large occipital-parietal lesions. They also demonstrate that parietal damage does not affect preattentive spatial coding of feature locations or complex spatial relationships between parts of a stimulus despite explicit spatial deficits and simultanagnosia.

Support for distinct subcomponents of spatial working memory: a double dissociation between spatial-simultaneous and spatial-sequential performance in unilateral neglect.

PubMed

Wansard, Murielle; Bartolomeo, Paolo; Bastin, Christine; Segovia, Fermín; Gillet, Sophie; Duret, Christophe; Meulemans, Thierry

2015-01-01

Over the last decade, many studies have demonstrated that visuospatial working memory (VSWM) can be divided into separate subsystems dedicated to the retention of visual patterns and their serial order. Impaired VSWM has been suggested to exacerbate left visual neglect in right-brain-damaged individuals. The aim of this study was to investigate the segregation between spatial-sequential and spatial-simultaneous working memory in individuals with neglect. We demonstrated that patterns of results on these VSWM tasks can be dissociated. Spatial-simultaneous and sequential aspects of VSWM can be selectively impaired in unilateral neglect. Our results support the hypothesis of multiple VSWM subsystems, which should be taken into account to better understand neglect-related deficits.

Trophic disruption: a meta-analysis of how habitat fragmentation affects resource consumption in terrestrial arthropod systems.

PubMed

Martinson, Holly M; Fagan, William F

2014-09-01

Habitat fragmentation is a complex process that affects ecological systems in diverse ways, altering everything from population persistence to ecosystem function. Despite widespread recognition that habitat fragmentation can influence food web interactions, consensus on the factors underlying variation in the impacts of fragmentation across systems remains elusive. In this study, we conduct a systematic review and meta-analysis to quantify the effects of habitat fragmentation and spatial habitat structure on resource consumption in terrestrial arthropod food webs. Across 419 studies, we found a negative overall effect of fragmentation on resource consumption. Variation in effect size was extensive but predictable. Specifically, resource consumption was reduced on small, isolated habitat fragments, higher at patch edges, and neutral with respect to landscape-scale spatial variables. In general, resource consumption increased in fragmented settings for habitat generalist consumers but decreased for specialist consumers. Our study demonstrates widespread disruption of trophic interactions in fragmented habitats and describes variation among studies that is largely predictable based on the ecological traits of the interacting species. We highlight future prospects for understanding how changes in spatial habitat structure may influence trophic modules and food webs. © 2014 John Wiley & Sons Ltd/CNRS.

Fractionating dead reckoning: role of the compass, odometer, logbook, and home base establishment in spatial orientation

NASA Astrophysics Data System (ADS)

Wallace, Douglas G.; Martin, Megan M.; Winter, Shawn S.

2008-06-01

Rats use multiple sources of information to maintain spatial orientation. Although previous work has focused on rats’ use of environmental cues, a growing number of studies have demonstrated that rats also use self-movement cues to organize navigation. This review examines the extent that kinematic analysis of naturally occurring behavior has provided insight into processes that mediate dead-reckoning-based navigation. This work supports a role for separate systems in processing self-movement cues that converge on the hippocampus. The compass system is involved in deriving directional information from self-movement cues; whereas, the odometer system is involved in deriving distance information from self-movement cues. The hippocampus functions similar to a logbook in that outward path unique information from the compass and odometer is used to derive the direction and distance of a path to the point at which movement was initiated. Finally, home base establishment may function to reset this system after each excursion and anchor environmental cues to self-movement cues. The combination of natural behaviors and kinematic analysis has proven to be a robust paradigm to investigate the neural basis of spatial orientation.

Fractionating dead reckoning: role of the compass, odometer, logbook, and home base establishment in spatial orientation

PubMed Central

Martin, Megan M.; Winter, Shawn S.

2008-01-01

Rats use multiple sources of information to maintain spatial orientation. Although previous work has focused on rats' use of environmental cues, a growing number of studies have demonstrated that rats also use self-movement cues to organize navigation. This review examines the extent that kinematic analysis of naturally occurring behavior has provided insight into processes that mediate dead-reckoning-based navigation. This work supports a role for separate systems in processing self-movement cues that converge on the hippocampus. The compass system is involved in deriving directional information from self-movement cues; whereas, the odometer system is involved in deriving distance information from self-movement cues. The hippocampus functions similar to a logbook in that outward path unique information from the compass and odometer is used to derive the direction and distance of a path to the point at which movement was initiated. Finally, home base establishment may function to reset this system after each excursion and anchor environmental cues to self-movement cues. The combination of natural behaviors and kinematic analysis has proven to be a robust paradigm to investigate the neural basis of spatial orientation. PMID:18553065

Spatial assessment and source identification of heavy metals pollution in surface water using several chemometric techniques.

PubMed

Ismail, Azimah; Toriman, Mohd Ekhwan; Juahir, Hafizan; Zain, Sharifuddin Md; Habir, Nur Liyana Abdul; Retnam, Ananthy; Kamaruddin, Mohd Khairul Amri; Umar, Roslan; Azid, Azman

2016-05-15

This study presents the determination of the spatial variation and source identification of heavy metal pollution in surface water along the Straits of Malacca using several chemometric techniques. Clustering and discrimination of heavy metal compounds in surface water into two groups (northern and southern regions) are observed according to level of concentrations via the application of chemometric techniques. Principal component analysis (PCA) demonstrates that Cu and Cr dominate the source apportionment in northern region with a total variance of 57.62% and is identified with mining and shipping activities. These are the major contamination contributors in the Straits. Land-based pollution originating from vehicular emission with a total variance of 59.43% is attributed to the high level of Pb concentration in the southern region. The results revealed that one state representing each cluster (northern and southern regions) is significant as the main location for investigating heavy metal concentration in the Straits of Malacca which would save monitoring cost and time. The monitoring of spatial variation and source of heavy metals pollution at the northern and southern regions of the Straits of Malacca, Malaysia, using chemometric analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cluster: A New Application for Spatial Analysis of Pixelated Data for Epiphytotics.

PubMed

Nelson, Scot C; Corcoja, Iulian; Pethybridge, Sarah J

2017-12-01

Spatial analysis of epiphytotics is essential to develop and test hypotheses about pathogen ecology, disease dynamics, and to optimize plant disease management strategies. Data collection for spatial analysis requires substantial investment in time to depict patterns in various frames and hierarchies. We developed a new approach for spatial analysis of pixelated data in digital imagery and incorporated the method in a stand-alone desktop application called Cluster. The user isolates target entities (clusters) by designating up to 24 pixel colors as nontargets and moves a threshold slider to visualize the targets. The app calculates the percent area occupied by targeted pixels, identifies the centroids of targeted clusters, and computes the relative compass angle of orientation for each cluster. Users can deselect anomalous clusters manually and/or automatically by specifying a size threshold value to exclude smaller targets from the analysis. Up to 1,000 stochastic simulations randomly place the centroids of each cluster in ranked order of size (largest to smallest) within each matrix while preserving their calculated angles of orientation for the long axes. A two-tailed probability t test compares the mean inter-cluster distances for the observed versus the values derived from randomly simulated maps. This is the basis for statistical testing of the null hypothesis that the clusters are randomly distributed within the frame of interest. These frames can assume any shape, from natural (e.g., leaf) to arbitrary (e.g., a rectangular or polygonal field). Cluster summarizes normalized attributes of clusters, including pixel number, axis length, axis width, compass orientation, and the length/width ratio, available to the user as a downloadable spreadsheet. Each simulated map may be saved as an image and inspected. Provided examples demonstrate the utility of Cluster to analyze patterns at various spatial scales in plant pathology and ecology and highlight the limitations, trade-offs, and considerations for the sensitivities of variables and the biological interpretations of results. The Cluster app is available as a free download for Apple computers at iTunes, with a link to a user guide website.

Geo-Spatial Social Network Analysis of Social Media to Mitigate Disasters

NASA Astrophysics Data System (ADS)

Carley, K. M.

2017-12-01

Understanding the spatial layout of human activity can afford a better understanding many phenomena - such as local cultural, the spread of ideas, and the scope of a disaster. Today, social media is one of the key sensors for acquiring information on socio-cultural activity, some with cues as to the geo-location. We ask, What can be learned by putting such data on maps? For example, are people who chat on line more likely to be near each other? Can Twitter data support disaster planning or early warning? In this talk, such issues are examined using data collected via Twitter and analyzed using ORA. ORA is a network analysis and visualization system. It supports not just social networks (who is interacting with whom), but also high dimensional networks with many types of nodes (e.g. people, organizations, resources, activities …) and relations, geo-spatial network analysis, dynamic network analysis, & geo-temporal analysis. Using ORA lessons learned from five case studies are considered: Arab Spring, Tsunami warning in Padang Indonesia, Twitter around Fukushima in Japan, Typhoon Haiyan (Yolanda), & regional conflict. Using Padang Indonesia data, we characterize the strengths and limitations of social media data to support disaster planning & early warning, identify at risk areas & issues of concern, and estimate where people are and which areas are impacted. Using Fukushima Japanese data, social media is used to estimate geo-spatial regularities in movement and communication that can inform disaster response and risk estimation. Using Arab Spring data, we find that the spread of bots & extremists varies by country and time, to the extent that using twitter to understand who is important or what ideas are critical can be compromised. Bots and extremists can exploit disaster messaging to create havoc and facilitate criminal activity e.g. human trafficking. Event discovery mechanisms support isolating geo-epi-centers for key events become crucial. Spatial inference enables improved country, and city identification. Geo-network analytics with and without these inferences reveal that explicitly geo-tagged data may not be representative and that improved location estimation provides better insight into the social condition. These results demonstrate the value of these technique to mitigate the social impact of disasters.

Trends and spatial distribution of deaths of children aged 12-60 months in São Paulo, Brazil, 1980-98.

PubMed Central

Antunes, José Leopoldo Ferreira; Waldman, Eliseu Alves

2002-01-01

OBJECTIVE: To describe trends in the mortality of children aged 12-60 months and to perform spatial data analysis of its distribution at the inner city district level in São Paulo from 1980 to 1998. METHODS: Official mortality data were analysed in relation to the underlying causes of death. The population of children aged 12-60 months, disaggregated by sex and age, was estimated for each year. Educational levels, income, employment status, and other socioeconomic indices were also assessed. Statistical Package for Social Sciences software was used for the statistical processing of time series. The Cochrane-Orcutt procedure of generalized least squares regression analysis was used to estimate the regression parameters with control of first-order autocorrelation. Spatial data analysis employed the discrimination of death rates and socioeconomic indices at the inner city district level. For classifying area-level death rates the method of K-means cluster analysis was used. Spatial correlation between variables was analysed by the simultaneous autoregressive regression method. FINDINGS: There was a steady decline in death rates during the 1980s at an average rate of 3.08% per year, followed by a levelling off. Infectious diseases remained the major cause of mortality, accounting for 43.1% of deaths during the last three years of the study. Injuries accounted for 16.5% of deaths. Mortality rates at the area level clearly demonstrated inequity in the city's health profile: there was an increasing difference between the rich and the underprivileged social strata in this respect. CONCLUSION: The overall mortality rate among children aged 12-60 months dropped by almost 30% during the study period. Most of the decline happened during the 1980s. Many people still live in a state of deprivation in underserved areas. Time-series and spatial data analysis provided indications of potential value in the planning of social policies promoting well-being, through the identification of factors affecting child survival and the regions with the worst health profiles, to which programmes and resources should be preferentially directed. PMID:12077615

Calibration and analysis of a multimodal micro-CT and structured light imaging system for the evaluation of excised breast tissue

NASA Astrophysics Data System (ADS)

McClatchy, David M., III; Rizzo, Elizabeth J.; Meganck, Jeff; Kempner, Josh; Vicory, Jared; Wells, Wendy A.; Paulsen, Keith D.; Pogue, Brian W.

2017-12-01

A multimodal micro-computed tomography (CT) and multi-spectral structured light imaging (SLI) system is introduced and systematically analyzed to test its feasibility to aid in margin delineation during breast conserving surgery (BCS). Phantom analysis of the micro-CT yielded a signal-to-noise ratio of 34, a contrast of 1.64, and a minimum detectable resolution of 240 μm for a 1.2 min scan. The SLI system, spanning wavelengths 490 nm to 800 nm and spatial frequencies up to 1.37 mm-1 , was evaluated with aqueous tissue simulating phantoms having variations in particle size distribution, scatter density, and blood volume fraction. The reduced scattering coefficient, μs\\prime and phase function parameter, γ, were accurately recovered over all wavelengths independent of blood volume fractions from 0% to 4%, assuming a flat sample geometry perpendicular to the imaging plane. The resolution of the optical system was tested with a step phantom, from which the modulation transfer function was calculated yielding a maximum resolution of 3.78 cycles per mm. The three dimensional spatial co-registration between the CT and optical imaging space was tested and shown to be accurate within 0.7 mm. A freshly resected breast specimen, with lobular carcinoma, fibrocystic disease, and adipose, was imaged with the system. The micro-CT provided visualization of the tumor mass and its spiculations, and SLI yielded superficial quantification of light scattering parameters for the malignant and benign tissue types. These results appear to be the first demonstration of SLI combined with standard medical tomography for imaging excised tumor specimens. While further investigations are needed to determine and test the spectral, spatial, and CT features required to classify tissue, this study demonstrates the ability of multimodal CT/SLI to quantify, visualize, and spatially navigate breast tumor specimens, which could potentially aid in the assessment of tumor margin status during BCS.

Research of GIS-services applicability for solution of spatial analysis tasks.

NASA Astrophysics Data System (ADS)

Terekhin, D. A.; Botygin, I. A.; Sherstneva, A. I.; Sherstnev, V. S.

2017-01-01

Experiments for working out the areas of applying various gis-services in the tasks of spatial analysis are discussed in this paper. Google Maps, Yandex Maps, Microsoft SQL Server are used as services of spatial analysis. All services have shown a comparable speed of analyzing the spatial data when carrying out elemental spatial requests (building up the buffer zone of a point object) as well as the preferences of Microsoft SQL Server in operating with more complicated spatial requests. When building up elemental spatial requests, internet-services show higher efficiency due to cliental data handling with JavaScript-subprograms. A weak point of public internet-services is an impossibility to handle data on a server side and a barren variety of spatial analysis functions. Microsoft SQL Server offers a large variety of functions needed for spatial analysis on the server side. The authors conclude that when solving practical problems, the capabilities of internet-services used in building up routes and completing other functions with spatial analysis with Microsoft SQL Server should be involved.

Vacuum scanning capillary photoemission microscopy.

PubMed

Aseyev, S A; Cherkun, A P; Mironov, B N; Petrunin, V V; Chekalin, S V

2017-08-01

We demonstrate the use of a conical capillary in a scanning probe microscopy for surface analysis. The probe can measure photoemission from a substrate by transmitting photoelectrons along the capillary as a function of probe position. The technique is demonstrated on a model substrate consisting of a gold reflecting layer on a compact disc which has been illuminated by an unfocused laser beam with a wavelength 400nm, from a femtosecond laser with a beam size of 4mm. A quartz capillary with a 2-µm aperture has been used in the experiments. The period of gold microstructure, shown to be 1.6µ, was measured by the conical probe operating in shear force mode. In shear force regime, the dielectric capillary has been used as a "classical" SPM tip, which provided images reflecting the surface topology. In a photoelectron regime photoelectrons passed through hollow tip and entered a detector. The spatial distribution of the recorded photoelectrons consisted of periodic mountain-valley strips, resembling the surface profile of the sample. Submicron spatial resolution has been achieved. This approach paves the way to study pulsed photodesorption of large organic molecular ions with high spatial and element resolution using the combination of a hollow-tip scanner with time-of-flight technique. Copyright © 2017 Elsevier B.V. All rights reserved.

Demonstration of Confined Electron Gas and Steep-Slope Behavior in Delta-Doped GaAs-AlGaAs Core-Shell Nanowire Transistors.

PubMed

Morkötter, S; Jeon, N; Rudolph, D; Loitsch, B; Spirkoska, D; Hoffmann, E; Döblinger, M; Matich, S; Finley, J J; Lauhon, L J; Abstreiter, G; Koblmüller, G

2015-05-13

Strong surface and impurity scattering in III-V semiconductor-based nanowires (NW) degrade the performance of electronic devices, requiring refined concepts for controlling charge carrier conductivity. Here, we demonstrate remote Si delta (δ)-doping of radial GaAs-AlGaAs core-shell NWs that unambiguously exhibit a strongly confined electron gas with enhanced low-temperature field-effect mobilities up to 5 × 10(3) cm(2) V(-1) s(-1). The spatial separation between the high-mobility free electron gas at the NW core-shell interface and the Si dopants in the shell is directly verified by atom probe tomographic (APT) analysis, band-profile calculations, and transport characterization in advanced field-effect transistor (FET) geometries, demonstrating powerful control over the free electron gas density and conductivity. Multigated NW-FETs allow us to spatially resolve channel width- and crystal phase-dependent variations in electron gas density and mobility along single NW-FETs. Notably, dc output and transfer characteristics of these n-type depletion mode NW-FETs reveal excellent drain current saturation and record low subthreshold slopes of 70 mV/dec at on/off ratios >10(4)-10(5) at room temperature.

Integration of GIS and Bim for Indoor Geovisual Analytics

NASA Astrophysics Data System (ADS)

Wu, B.; Zhang, S.

2016-06-01

This paper presents an endeavour of integration of GIS (Geographical Information System) and BIM (Building Information Modelling) for indoor geovisual analytics. The merits of two types of technologies, GIS and BIM are firstly analysed in the context of indoor environment. GIS has well-developed capabilities of spatial analysis such as network analysis, while BIM has the advantages for indoor 3D modelling and dynamic simulation. This paper firstly investigates the important aspects for integrating GIS and BIM. Different data standards and formats such as the IFC (Industry Foundation Classes) and GML (Geography Markup Language) are discussed. Their merits and limitations in data transformation between GIS and BIM are analysed in terms of semantic and geometric information. An optimized approach for data exchange between GIS and BIM datasets is then proposed. After that, a strategy of using BIM for 3D indoor modelling, GIS for spatial analysis, and BIM again for visualization and dynamic simulation of the analysis results is presented. Based on the developments, this paper selects a typical problem, optimized indoor emergency evacuation, to demonstrate the integration of GIS and BIM for indoor geovisual analytics. The block Z of the Hong Kong Polytechnic University is selected as a test site. Detailed indoor and outdoor 3D models of the block Z are created using a BIM software Revit. The 3D models are transferred to a GIS software ArcGIS to carry out spatial analysis. Optimized evacuation plans considering dynamic constraints are generated based on network analysis in ArcGIS assuming there is a fire accident inside the building. The analysis results are then transferred back to BIM software for visualization and dynamic simulation. The developed methods and results are of significance to facilitate future development of GIS and BIM integrated solutions in various applications.

Identification of spatially-localized initial conditions via sparse PCA

NASA Astrophysics Data System (ADS)

Dwivedi, Anubhav; Jovanovic, Mihailo

2017-11-01

Principal Component Analysis involves maximization of a quadratic form subject to a quadratic constraint on the initial flow perturbations and it is routinely used to identify the most energetic flow structures. For general flow configurations, principal components can be efficiently computed via power iteration of the forward and adjoint governing equations. However, the resulting flow structures typically have a large spatial support leading to a question of physical realizability. To obtain spatially-localized structures, we modify the quadratic constraint on the initial condition to include a convex combination with an additional regularization term which promotes sparsity in the physical domain. We formulate this constrained optimization problem as a nonlinear eigenvalue problem and employ an inverse power-iteration-based method to solve it. The resulting solution is guaranteed to converge to a nonlinear eigenvector which becomes increasingly localized as our emphasis on sparsity increases. We use several fluids examples to demonstrate that our method indeed identifies the most energetic initial perturbations that are spatially compact. This work was supported by Office of Naval Research through Grant Number N00014-15-1-2522.

Coexistence of fraternity and egoism for spatial social dilemmas.

PubMed

Szabó, György; Szolnoki, Attila; Czakó, Lilla

2013-01-21

We have studied an evolutionary game with spatially arranged players who can choose one of the two strategies (named cooperation and defection for social dilemmas) when playing with their neighbors. In addition to the application of the usual strategies in the present model the players are also characterized by one of the two extreme personal features representing the egoist or fraternal behavior. During the evolution each player can modify both her own strategy and/or personal feature via a myopic update process in order to improve her utility. The results of numerical simulations and stability analysis are summarized in phase diagrams representing a wide scale of spatially ordered distribution of strategies and personal features when varying the payoff parameters. In most of the cases only two of the four possible options prevail and may form sublattice ordered spatial structure. The evolutionary advantage of the fraternal attitude is demonstrated within a large range of payoff parameters including the region of prisoner's dilemma where egoist defectors and fraternal cooperators form a role-separating chessboard like pattern. Copyright © 2012 Elsevier Ltd. All rights reserved.

Lateral Temperature-Gradient Method for High-Throughput Characterization of Material Processing by Millisecond Laser Annealing.

PubMed

Bell, Robert T; Jacobs, Alan G; Sorg, Victoria C; Jung, Byungki; Hill, Megan O; Treml, Benjamin E; Thompson, Michael O

2016-09-12

A high-throughput method for characterizing the temperature dependence of material properties following microsecond to millisecond thermal annealing, exploiting the temperature gradients created by a lateral gradient laser spike anneal (lgLSA), is presented. Laser scans generate spatial thermal gradients of up to 5 °C/μm with peak temperatures ranging from ambient to in excess of 1400 °C, limited only by laser power and materials thermal limits. Discrete spatial property measurements across the temperature gradient are then equivalent to independent measurements after varying temperature anneals. Accurate temperature calibrations, essential to quantitative analysis, are critical and methods for both peak temperature and spatial/temporal temperature profile characterization are presented. These include absolute temperature calibrations based on melting and thermal decomposition, and time-resolved profiles measured using platinum thermistors. A variety of spatially resolved measurement probes, ranging from point-like continuous profiling to large area sampling, are discussed. Examples from annealing of III-V semiconductors, CdSe quantum dots, low-κ dielectrics, and block copolymers are included to demonstrate the flexibility, high throughput, and precision of this technique.

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China.

PubMed

Cao, Chunxiang; Chen, Wei; Zheng, Sheng; Zhao, Jian; Wang, Jinfeng; Cao, Wuchun

2016-01-01

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China

PubMed Central

Cao, Chunxiang; Zheng, Sheng; Zhao, Jian; Wang, Jinfeng; Cao, Wuchun

2016-01-01

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases. PMID:27597972

Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data.

PubMed

Aji, Ablimit; Wang, Fusheng; Saltz, Joel H

2012-11-06

Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the "big data" challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce.

Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data

PubMed Central

Aji, Ablimit; Wang, Fusheng; Saltz, Joel H.

2013-01-01

Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the “big data” challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce. PMID:24501719

Spatiotemporal Analysis of Coronal Loops Using Seismology of Damped Kink Oscillations and Forward Modeling of EUV Intensity Profiles

NASA Astrophysics Data System (ADS)

Pascoe, D. J.; Anfinogentov, S. A.; Goddard, C. R.; Nakariakov, V. M.

2018-06-01

The shape of the damping profile of kink oscillations in coronal loops has recently allowed the transverse density profile of the loop to be estimated. This requires accurate measurement of the damping profile that can distinguish the Gaussian and exponential damping regimes, otherwise there are more unknowns than observables. Forward modeling of the transverse intensity profile may also be used to estimate the width of the inhomogeneous layer of a loop, providing an independent estimate of one of these unknowns. We analyze an oscillating loop for which the seismological determination of the transverse structure is inconclusive except when supplemented by additional spatial information from the transverse intensity profile. Our temporal analysis describes the motion of a coronal loop as a kink oscillation damped by resonant absorption, and our spatial analysis is based on forward modeling the transverse EUV intensity profile of the loop under the isothermal and optically thin approximations. We use Bayesian analysis and Markov chain Monte Carlo sampling to apply our spatial and temporal models both individually and simultaneously to our data and compare the results with numerical simulations. Combining the two methods allows both the inhomogeneous layer width and density contrast to be calculated, which is not possible for the same data when each method is applied individually. We demonstrate that the assumption of an exponential damping profile leads to a significantly larger error in the inferred density contrast ratio compared with a Gaussian damping profile.

Image-based quantification and mathematical modeling of spatial heterogeneity in ESC colonies.

PubMed

Herberg, Maria; Zerjatke, Thomas; de Back, Walter; Glauche, Ingmar; Roeder, Ingo

2015-06-01

Pluripotent embryonic stem cells (ESCs) have the potential to differentiate into cells of all three germ layers. This unique property has been extensively studied on the intracellular, transcriptional level. However, ESCs typically form clusters of cells with distinct size and shape, and establish spatial structures that are vital for the maintenance of pluripotency. Even though it is recognized that the cells' arrangement and local interactions play a role in fate decision processes, the relations between transcriptional and spatial patterns have not yet been studied. We present a systems biology approach which combines live-cell imaging, quantitative image analysis, and multiscale, mathematical modeling of ESC growth. In particular, we develop quantitative measures of the morphology and of the spatial clustering of ESCs with different expression levels and apply them to images of both in vitro and in silico cultures. Using the same measures, we are able to compare model scenarios with different assumptions on cell-cell adhesions and intercellular feedback mechanisms directly with experimental data. Applying our methodology to microscopy images of cultured ESCs, we demonstrate that the emerging colonies are highly variable regarding both morphological and spatial fluorescence patterns. Moreover, we can show that most ESC colonies contain only one cluster of cells with high self-renewing capacity. These cells are preferentially located in the interior of a colony structure. The integrated approach combining image analysis with mathematical modeling allows us to reveal potential transcription factor related cellular and intercellular mechanisms behind the emergence of observed patterns that cannot be derived from images directly. © 2015 International Society for Advancement of Cytometry.

A comparative study of the spatial distribution of mast cells and microvessels in the foetal, adult human thymus and thymoma.

PubMed

Raica, Marius; Cimpean, Anca Maria; Nico, Beatrice; Guidolin, Diego; Ribatti, Domenico

2010-02-01

Mast cells (MCs) are widely distributed in human and animal tissues and have been shown to play an important role in angiogenesis in normal and pathological conditions. Few data are available about the relationship between MCs and blood vessels in the normal human thymus, and there are virtually no data about their distribution and significance in thymoma. The aim of this study was to analyse the spatial distribution of MCs and microvessels in the normal foetal and adult thymus and thymoma. Twenty biopsy specimens of human thymus, including foetal and adult normal thymus and thymoma were analysed. Double staining with CD34 and mast cell tryptase was used to count both mast cells and microvessels in the same fields. Computer-assisted image analysis was performed to characterize the spatial distribution of MCs and blood vessels in selected specimens. Results demonstrated that MCs were localized exclusively to the medulla. Their number was significantly higher in thymoma specimens as compared with adult and foetal normal specimens respectively. In contrast the microvessel area was unchanged. The analysis of the spatial distribution and relationship between MCs and microvessels revealed that only in the thymoma specimens was there a significant spatial association between MCs and microvessels. Overall, these data suggest that MCs do not contribute significantly to the development of the vascular network in foetal and adult thymus, whereas in thymoma they show a close relationship to blood vessels. This could be an expression of their involvement not only in endothelial cells but also in tumour cell proliferation.

Super-resolution mapping using multi-viewing CHRIS/PROBA data

NASA Astrophysics Data System (ADS)

Dwivedi, Manish; Kumar, Vinay

2016-04-01

High-spatial resolution Remote Sensing (RS) data provides detailed information which ensures high-definition visual image analysis of earth surface features. These data sets also support improved information extraction capabilities at a fine scale. In order to improve the spatial resolution of coarser resolution RS data, the Super Resolution Reconstruction (SRR) technique has become widely acknowledged which focused on multi-angular image sequences. In this study multi-angle CHRIS/PROBA data of Kutch area is used for SR image reconstruction to enhance the spatial resolution from 18 m to 6m in the hope to obtain a better land cover classification. Various SR approaches like Projection onto Convex Sets (POCS), Robust, Iterative Back Projection (IBP), Non-Uniform Interpolation and Structure-Adaptive Normalized Convolution (SANC) chosen for this study. Subjective assessment through visual interpretation shows substantial improvement in land cover details. Quantitative measures including peak signal to noise ratio and structural similarity are used for the evaluation of the image quality. It was observed that SANC SR technique using Vandewalle algorithm for the low resolution image registration outperformed the other techniques. After that SVM based classifier is used for the classification of SRR and data resampled to 6m spatial resolution using bi-cubic interpolation. A comparative analysis is carried out between classified data of bicubic interpolated and SR derived images of CHRIS/PROBA and SR derived classified data have shown a significant improvement of 10-12% in the overall accuracy. The results demonstrated that SR methods is able to improve spatial detail of multi-angle images as well as the classification accuracy.

A vorticity transport model to restore spatial gaps in velocity data

NASA Astrophysics Data System (ADS)

Ameli, Siavash; Shadden, Shawn

2017-11-01

Often measurements of velocity data do not have full spatial coverage in the probed domain or near boundaries. These gaps can be due to missing measurements or masked regions of corrupted data. These gaps confound interpretation, and are problematic when the data is used to compute Lagrangian or trajectory-based analyses. Various techniques have been proposed to overcome coverage limitations in velocity data such as unweighted least square fitting, empirical orthogonal function analysis, variational interpolation as well as boundary modal analysis. In this talk, we present a vorticity transport PDE to reconstruct regions of missing velocity vectors. The transport model involves both nonlinear anisotropic diffusion and advection. This approach is shown to preserve the main features of the flow even in cases of large gaps, and the reconstructed regions are continuous up to second order. We illustrate results for high-frequency radar (HFR) measurements of the ocean surface currents as this is a common application of limited coverage. We demonstrate that the error of the method is on the same order of the error of the original velocity data. In addition, we have developed a web-based gateway for data restoration, and we will demonstrate a practical application using available data. This work is supported by the NSF Grant No. 1520825.

Constellation of phase singularities in a speckle-like pattern for optical vortex metrology applied to biological kinematic analysis.

PubMed

Wang, Wei; Qiao, Yu; Ishijima, Reika; Yokozeki, Tomoaki; Honda, Daigo; Matsuda, Akihiro; Hanson, Steen G; Takeda, Mitsuo

2008-09-01

A novel technique for biological kinematic analysis is proposed that makes use of the pseudophase singularities in a complex signal generated from a speckle-like pattern. In addition to the information about the locations and the anisotropic core structures of the pseudophase singularities, we also detect the spatial structures of a cluster of phase singularities, which serves as a unique constellation characterizing the mutual position relation between the individual pseudophase singularities. Experimental results of in vivo measurements for a swimming fish along with its kinematic analysis are presented, which demonstrate the validity of the proposed technique.

archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems.

PubMed

Delory, Benjamin M; Li, Mao; Topp, Christopher N; Lobet, Guillaume

2018-01-01

Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for leaves, shoots, and root systems. In this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology. In addition, we also show that both geometric and topological descriptors are necessary to accurately compare root systems and assess their natural complexity.

archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems

PubMed Central

Delory, Benjamin M.; Li, Mao; Topp, Christopher N.; Lobet, Guillaume

2018-01-01

Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for leaves, shoots, and root systems. In this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology. In addition, we also show that both geometric and topological descriptors are necessary to accurately compare root systems and assess their natural complexity. PMID:29636899

Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces

NASA Astrophysics Data System (ADS)

Abu-Alqumsan, Mohammad; Peer, Angelika

2016-06-01

Objective. Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired Electroencephalographic (EEG) data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods. Approach. We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels. Main results. We found that the multivariate synchronization index method and the maximum contrast combination method are variations of the CCA method. All three methods were found to provide relatively unreliable detections in low signal-to-noise ratio (SNR) regimes. CVARS and the minimum energy combination methods were found to provide better estimates for different SNR levels. Significance. Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably.

Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures

PubMed Central

Zavodszky, Maria I.

2017-01-01

Background Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic profiles expressed as diverse molecular, morphological and spatial distributions. This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient treatment. Consequently, tools and techniques are being developed to properly characterize and quantify tumor heterogeneity. Multiplexed immunofluorescence (MxIF) is one such technology that offers molecular insight into both inter-individual and intratumor heterogeneity. It enables the quantification of both the concentration and spatial distribution of 60+ proteins across a tissue section. Upon bioimage processing, protein expression data can be generated for each cell from a tissue field of view. Results The Multi-Omics Heterogeneity Analysis (MOHA) tool was developed to compute tissue heterogeneity metrics from MxIF spatially resolved tissue imaging data. This technique computes the molecular state of each cell in a sample based on a pathway or gene set. Spatial states are then computed based on the spatial arrangements of the cells as distinguished by their respective molecular states. MOHA computes tissue heterogeneity metrics from the distributions of these molecular and spatially defined states. A colorectal cancer cohort of approximately 700 subjects with MxIF data is presented to demonstrate the MOHA methodology. Within this dataset, statistically significant correlations were found between the intratumor AKT pathway state diversity and cancer stage and histological tumor grade. Furthermore, intratumor spatial diversity metrics were found to correlate with cancer recurrence. Conclusions MOHA provides a simple and robust approach to characterize molecular and spatial heterogeneity of tissues. Research projects that generate spatially resolved tissue imaging data can take full advantage of this useful technique. The MOHA algorithm is implemented as a freely available R script (see supplementary information). PMID:29190747

Coupled Effects of Natural and Anthropogenic Controls on Seasonal and Spatial Variations of River Water Quality during Baseflow in a Coastal Watershed of Southeast China

PubMed Central

Huang, Jinliang; Huang, Yaling; Zhang, Zhenyu

2014-01-01

Surface water samples of baseflow were collected from 20 headwater sub-watersheds which were classified into three types of watersheds (natural, urban and agricultural) in the flood, dry and transition seasons during three consecutive years (2010–2012) within a coastal watershed of Southeast China. Integrating spatial statistics with multivariate statistical techniques, river water quality variations and their interactions with natural and anthropogenic controls were examined to identify the causal factors and underlying mechanisms governing spatiotemporal patterns of water quality. Anthropogenic input related to industrial effluents and domestic wastewater, agricultural activities associated with the precipitation-induced surface runoff, and natural weathering process were identified as the potential important factors to drive the seasonal variations in stream water quality for the transition, flood and dry seasons, respectively. All water quality indicators except SRP had the highest mean concentrations in the dry and transition seasons. Anthropogenic activities and watershed characteristics led to the spatial variations in stream water quality in three types of watersheds. Concentrations of NH4 +-N, SRP, K+, CODMn, and Cl− were generally highest in urban watersheds. NO3 –N Concentration was generally highest in agricultural watersheds. Mg2+ concentration in natural watersheds was significantly higher than that in agricultural watersheds. Spatial autocorrelations analysis showed similar levels of water pollution between the neighboring sub-watersheds exhibited in the dry and transition seasons while non-point source pollution contributed to the significant variations in water quality between neighboring sub-watersheds. Spatial regression analysis showed anthropogenic controls played critical roles in variations of water quality in the JRW. Management implications were further discussed for water resource management. This research demonstrates that the coupled effects of natural and anthropogenic controls involved in watershed processes, contribute to the seasonal and spatial variation of headwater stream water quality in a coastal watershed with high spatial variability and intensive anthropogenic activities. PMID:24618771

Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localization

NASA Astrophysics Data System (ADS)

Khaki, M.; Schumacher, M.; Forootan, E.; Kuhn, M.; Awange, J. L.; van Dijk, A. I. J. M.

2017-10-01

Assimilation of terrestrial water storage (TWS) information from the Gravity Recovery And Climate Experiment (GRACE) satellite mission can provide significant improvements in hydrological modelling. However, the rather coarse spatial resolution of GRACE TWS and its spatially correlated errors pose considerable challenges for achieving realistic assimilation results. Consequently, successful data assimilation depends on rigorous modelling of the full error covariance matrix of the GRACE TWS estimates, as well as realistic error behavior for hydrological model simulations. In this study, we assess the application of local analysis (LA) to maximize the contribution of GRACE TWS in hydrological data assimilation. For this, we assimilate GRACE TWS into the World-Wide Water Resources Assessment system (W3RA) over the Australian continent while applying LA and accounting for existing spatial correlations using the full error covariance matrix. GRACE TWS data is applied with different spatial resolutions including 1° to 5° grids, as well as basin averages. The ensemble-based sequential filtering technique of the Square Root Analysis (SQRA) is applied to assimilate TWS data into W3RA. For each spatial scale, the performance of the data assimilation is assessed through comparison with independent in-situ ground water and soil moisture observations. Overall, the results demonstrate that LA is able to stabilize the inversion process (within the implementation of the SQRA filter) leading to less errors for all spatial scales considered with an average RMSE improvement of 54% (e.g., 52.23 mm down to 26.80 mm) for all the cases with respect to groundwater in-situ measurements. Validating the assimilated results with groundwater observations indicates that LA leads to 13% better (in terms of RMSE) assimilation results compared to the cases with Gaussian errors assumptions. This highlights the great potential of LA and the use of the full error covariance matrix of GRACE TWS estimates for improved data assimilation results.

Determination of awareness in patients with severe brain injury using EEG power spectral analysis

PubMed Central

Goldfine, Andrew M.; Victor, Jonathan D.; Conte, Mary M.; Bardin, Jonathan C.; Schiff, Nicholas D.

2011-01-01

Objective To determine whether EEG spectral analysis could be used to demonstrate awareness in patients with severe brain injury. Methods We recorded EEG from healthy controls and three patients with severe brain injury, ranging from minimally conscious state (MCS) to locked-in-state (LIS), while they were asked to imagine motor and spatial navigation tasks. We assessed EEG spectral differences from 4 to 24 Hz with univariate comparisons (individual frequencies) and multivariate comparisons (patterns across the frequency range). Results In controls, EEG spectral power differed at multiple frequency bands and channels during performance of both tasks compared to a resting baseline. As patterns of signal change were inconsistent between controls, we defined a positive response in patient subjects as consistent spectral changes across task performances. One patient in MCS and one in LIS showed evidence of motor imagery task performance, though with patterns of spectral change different from the controls. Conclusion EEG power spectral analysis demonstrates evidence for performance of mental imagery tasks in healthy controls and patients with severe brain injury. Significance EEG power spectral analysis can be used as a flexible bedside tool to demonstrate awareness in brain-injured patients who are otherwise unable to communicate. PMID:21514214

PlanetServer: Innovative approaches for the online analysis of hyperspectral satellite data from Mars

NASA Astrophysics Data System (ADS)

Oosthoek, J. H. P.; Flahaut, J.; Rossi, A. P.; Baumann, P.; Misev, D.; Campalani, P.; Unnithan, V.

2014-06-01

PlanetServer is a WebGIS system, currently under development, enabling the online analysis of Compact Reconnaissance Imaging Spectrometer (CRISM) hyperspectral data from Mars. It is part of the EarthServer project which builds infrastructure for online access and analysis of huge Earth Science datasets. Core functionality consists of the rasdaman Array Database Management System (DBMS) for storage, and the Open Geospatial Consortium (OGC) Web Coverage Processing Service (WCPS) for data querying. Various WCPS queries have been designed to access spatial and spectral subsets of the CRISM data. The client WebGIS, consisting mainly of the OpenLayers javascript library, uses these queries to enable online spatial and spectral analysis. Currently the PlanetServer demonstration consists of two CRISM Full Resolution Target (FRT) observations, surrounding the NASA Curiosity rover landing site. A detailed analysis of one of these observations is performed in the Case Study section. The current PlanetServer functionality is described step by step, and is tested by focusing on detecting mineralogical evidence described in earlier Gale crater studies. Both the PlanetServer methodology and its possible use for mineralogical studies will be further discussed. Future work includes batch ingestion of CRISM data and further development of the WebGIS and analysis tools.

Silicon oxide nanoparticles doped PQ-PMMA for volume holographic imaging filters.

PubMed

Luo, Yuan; Russo, Juan M; Kostuk, Raymond K; Barbastathis, George

2010-04-15

Holographic imaging filters are required to have high Bragg selectivity, namely, narrow angular and spectral bandwidth, to obtain spatial-spectral information within a three-dimensional object. In this Letter, we present the design of holographic imaging filters formed using silicon oxide nanoparticles (nano-SiO(2)) in phenanthrenquinone-poly(methyl methacrylate) (PQ-PMMA) polymer recording material. This combination offers greater Bragg selectivity and increases the diffraction efficiency of holographic filters. The holographic filters with optimized ratio of nano-SiO(2) in PQ-PMMA can significantly improve the performance of Bragg selectivity and diffraction efficiency by 53% and 16%, respectively. We present experimental results and data analysis demonstrating this technique in use for holographic spatial-spectral imaging filters.

Landsat image data quality studies

NASA Technical Reports Server (NTRS)

Schueler, C. F.; Salomonson, V. V.

1985-01-01

Preliminary results of the Landsat-4 Image Data Quality Analysis (LIDQA) program to characterize the data obtained using the Thematic Mapper (TM) instrument on board the Landsat-4 and Landsat-5 satellites are reported. TM design specifications were compared to the obtained data with respect to four criteria, including spatial resolution; geometric fidelity; information content; and image relativity to Multispectral Scanner (MSS) data. The overall performance of the TM was rated excellent despite minor instabilities and radiometric anomalies in the data. Spatial performance of the TM exceeded design specifications in terms of both image sharpness and geometric accuracy, and the image utility of the TM data was at least twice as high as MSS data. The separability of alfalfa and sugar beet fields in a TM image is demonstrated.

Controlling the influence of elastic eigenmodes on nanomagnet dynamics through pattern geometry

NASA Astrophysics Data System (ADS)

Berk, C.; Yahagi, Y.; Dhuey, S.; Cabrini, S.; Schmidt, H.

2017-03-01

The effect of the nanoscale array geometry on the interaction between optically generated surface acoustic waves (SAWs) and nanomagnet dynamics is investigated using Time-Resolved Magneto-Optical Kerr Effect Microscopy (TR-MOKE). It is demonstrated that altering the nanomagnet geometry from a periodic to a randomized aperiodic pattern effectively removes the magneto-elastic effect of SAWs on the magnetization dynamics. The efficiency of this method depends on the extent of any residual spatial correlations and is quantified by spatial Fourier analysis of the two structures. Randomization allows observation and extraction of intrinsic magnetic parameters such as spin wave frequencies and damping to be resolvable using all-optical methods, enabling the conclusion that the fabrication process does not affect the damping.

Structurally controllable spin spatial splitter in a hybrid ferromagnet and semiconductor nanostructure

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

Lu, Mao-Wang, E-mail: maowanglu@126.com; Cao, Xue-Li; Huang, Xin-Hong

2014-05-07

We theoretically investigate modulation of a tunable δ-potential to the lateral displacement of electrons across a magnetically modulated semiconductor nanostructure. Experimentally, this nanostructure can be produced by depositing a nanosized ferromagnetic stripe with in-plane magnetization on top of a semiconductor heterostructure, while the δ-potential can be realized by means of the atomic layer doping technique. Theoretical analysis reveals that this δ-doping can break the intrinsic symmetry in nanostructure and a considerable spin polarization in the lateral displacement will appear. Numerical calculations demonstrate that both magnitude and sign of spin polarization can be manipulated by changing the height and/or position ofmore » the δ-doping, giving rise to a structurally tunable spin spatial splitter.« less

Effects of Hormone Therapy on Brain Volumes Changes of Postmenopausal Women Revealed by Optimally-Discriminative Voxel-Based Morphometry

PubMed Central

Zhang, Tianhao; Casanova, Ramon; Resnick, Susan M.; Manson, JoAnn E.; Baker, Laura D.; Padual, Claudia B.; Kuller, Lewis H.; Bryan, R. Nick; Espeland, Mark A.; Davatzikos, Christos

2016-01-01

Backgrounds The Women's Health Initiative Memory Study Magnetic Resonance Imaging (WHIMS-MRI) provides an opportunity to evaluate how menopausal hormone therapy (HT) affects the structure of older women’s brains. Our earlier work based on region of interest (ROI) analysis demonstrated potential structural changes underlying adverse effects of HT on cognition. However, the ROI-based analysis is limited in statistical power and precision, and cannot provide fine-grained mapping of whole-brain changes. Methods We aimed to identify local structural differences between HT and placebo groups from WHIMS-MRI in a whole-brain refined level, by using a novel method, named Optimally-Discriminative Voxel-Based Analysis (ODVBA). ODVBA is a recently proposed imaging pattern analysis approach for group comparisons utilizing a spatially adaptive analysis scheme to accurately locate areas of group differences, thereby providing superior sensitivity and specificity to detect the structural brain changes over conventional methods. Results Women assigned to HT treatments had significant Gray Matter (GM) losses compared to the placebo groups in the anterior cingulate and the adjacent medial frontal gyrus, and the orbitofrontal cortex, which persisted after multiple comparison corrections. There were no regions where HT was significantly associated with larger volumes compared to placebo, although a trend of marginal significance was found in the posterior cingulate cortical area. The CEE-Alone and CEE+MPA groups, although compared with different placebo controls, demonstrated similar effects according to the spatial patterns of structural changes. Conclusions HT had adverse effects on GM volumes and risk for cognitive impairment and dementia in older women. These findings advanced our understanding of the neurobiological underpinnings of HT effects. PMID:26974440

Complex-valued time-series correlation increases sensitivity in FMRI analysis.

PubMed

Kociuba, Mary C; Rowe, Daniel B

2016-07-01

To develop a linear matrix representation of correlation between complex-valued (CV) time-series in the temporal Fourier frequency domain, and demonstrate its increased sensitivity over correlation between magnitude-only (MO) time-series in functional MRI (fMRI) analysis. The standard in fMRI is to discard the phase before the statistical analysis of the data, despite evidence of task related change in the phase time-series. With a real-valued isomorphism representation of Fourier reconstruction, correlation is computed in the temporal frequency domain with CV time-series data, rather than with the standard of MO data. A MATLAB simulation compares the Fisher-z transform of MO and CV correlations for varying degrees of task related magnitude and phase amplitude change in the time-series. The increased sensitivity of the complex-valued Fourier representation of correlation is also demonstrated with experimental human data. Since the correlation description in the temporal frequency domain is represented as a summation of second order temporal frequencies, the correlation is easily divided into experimentally relevant frequency bands for each voxel's temporal frequency spectrum. The MO and CV correlations for the experimental human data are analyzed for four voxels of interest (VOIs) to show the framework with high and low contrast-to-noise ratios in the motor cortex and the supplementary motor cortex. The simulation demonstrates the increased strength of CV correlations over MO correlations for low magnitude contrast-to-noise time-series. In the experimental human data, the MO correlation maps are noisier than the CV maps, and it is more difficult to distinguish the motor cortex in the MO correlation maps after spatial processing. Including both magnitude and phase in the spatial correlation computations more accurately defines the correlated left and right motor cortices. Sensitivity in correlation analysis is important to preserve the signal of interest in fMRI data sets with high noise variance, and avoid excessive processing induced correlation. Copyright © 2016 Elsevier Inc. All rights reserved.

Exploratory spatial analysis of pilot fatality rates in general aviation crashes using geographic information systems.

PubMed

Grabowski, Jurek G; Curriero, Frank C; Baker, Susan P; Li, Guohua

2002-03-01

Geographic information systems and exploratory spatial analysis were used to describe the geographic characteristics of pilot fatality rates in 1983-1998 general aviation crashes within the continental United States. The authors plotted crash sites on a digital map; rates were computed at regular grid intersections and then interpolated by using geographic information systems. A test for significance was performed by using Monte Carlo simulations. Further analysis compared low-, medium-, and high-rate areas in relation to pilot characteristics, aircraft type, and crash circumstance. Of the 14,051 general aviation crashes studied, 31% were fatal. Seventy-four geographic areas were categorized as having low fatality rates and 53 as having high fatality rates. High-fatality-rate areas tended to be mountainous, such as the Rocky Mountains and the Appalachian region, whereas low-rate areas were relatively flat, such as the Great Plains. Further analysis comparing low-, medium-, and high-fatality-rate areas revealed that crashes in high-fatality-rate areas were more likely than crashes in other areas to have occurred under instrument meteorologic conditions and to involve aircraft fire. This study demonstrates that geographic information systems are a valuable tool for injury prevention and aviation safety research.

Accuracy improvement of quantitative analysis by spatial confinement in laser-induced breakdown spectroscopy.

PubMed

Guo, L B; Hao, Z Q; Shen, M; Xiong, W; He, X N; Xie, Z Q; Gao, M; Li, X Y; Zeng, X Y; Lu, Y F

2013-07-29

To improve the accuracy of quantitative analysis in laser-induced breakdown spectroscopy, the plasma produced by a Nd:YAG laser from steel targets was confined by a cavity. A number of elements with low concentrations, such as vanadium (V), chromium (Cr), and manganese (Mn), in the steel samples were investigated. After the optimization of the cavity dimension and laser fluence, significant enhancement factors of 4.2, 3.1, and 2.87 in the emission intensity of V, Cr, and Mn lines, respectively, were achieved at a laser fluence of 42.9 J/cm(2) using a hemispherical cavity (diameter: 5 mm). More importantly, the correlation coefficient of the V I 440.85/Fe I 438.35 nm was increased from 0.946 (without the cavity) to 0.981 (with the cavity); and similar results for Cr I 425.43/Fe I 425.08 nm and Mn I 476.64/Fe I 492.05 nm were also obtained. Therefore, it was demonstrated that the accuracy of quantitative analysis with low concentration elements in steel samples was improved, because the plasma became uniform with spatial confinement. The results of this study provide a new pathway for improving the accuracy of quantitative analysis of LIBS.

Investigation of noise properties in grating-based x-ray phase tomography with reverse projection method

NASA Astrophysics Data System (ADS)

Bao, Yuan; Wang, Yan; Gao, Kun; Wang, Zhi-Li; Zhu, Pei-Ping; Wu, Zi-Yu

2015-10-01

The relationship between noise variance and spatial resolution in grating-based x-ray phase computed tomography (PCT) imaging is investigated with reverse projection extraction method, and the noise variances of the reconstructed absorption coefficient and refractive index decrement are compared. For the differential phase contrast method, the noise variance in the differential projection images follows the same inverse-square law with spatial resolution as in conventional absorption-based x-ray imaging projections. However, both theoretical analysis and simulations demonstrate that in PCT the noise variance of the reconstructed refractive index decrement scales with spatial resolution follows an inverse linear relationship at fixed slice thickness, while the noise variance of the reconstructed absorption coefficient conforms with the inverse cubic law. The results indicate that, for the same noise variance level, PCT imaging may enable higher spatial resolution than conventional absorption computed tomography (ACT), while ACT benefits more from degraded spatial resolution. This could be a useful guidance in imaging the inner structure of the sample in higher spatial resolution. Project supported by the National Basic Research Program of China (Grant No. 2012CB825800), the Science Fund for Creative Research Groups, the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant Nos. KJCX2-YW-N42 and Y4545320Y2), the National Natural Science Foundation of China (Grant Nos. 11475170, 11205157, 11305173, 11205189, 11375225, 11321503, 11179004, and U1332109).

Arsenic-Safe Aquifers in Coastal Bangladesh: AN Investigation with Ordinary Kriging Estimation

NASA Astrophysics Data System (ADS)

Hassan, M. M.; Ahamed, R.

2017-10-01

Spatial point pattern is one of the most suitable methods for analysing groundwater arsenic concentrations. Groundwater arsenic poisoning in Bangladesh has been one of the biggest environmental health disasters in recent times. About 85 million people are exposed to arsenic more than 50 μg/L in drinking water. The paper seeks to identify the existing suitable aquifers for arsenic-safe drinking water along with "spatial arsenic discontinuity" using GIS-based spatial geostatistical analysis in a small study site (12.69 km2) in the coastal belt of southwest Bangladesh (Dhopakhali union of Bagerhat district). The relevant spatial data were collected with Geographical Positioning Systems (GPS), arsenic data with field testing kits, tubewell attributes with observation and questionnaire survey. Geostatistics with kriging methods can design water quality monitoring in different aquifers with hydrochemical evaluation by spatial mapping. The paper presents the interpolation of the regional estimates of arsenic data for spatial discontinuity mapping with Ordinary Kriging (OK) method that overcomes the areal bias problem for administrative boundary. This paper also demonstrates the suitability of isopleth maps that is easier to read than choropleth maps. The OK method investigated that around 80 percent of the study site are contaminated following the Bangladesh Drinking Water Standards (BDWS) of 50 μg/L. The study identified a very few scattered "pockets" of arsenic-safe zone at the shallow aquifer.

BDNF and TNF-α polymorphisms in memory.

PubMed

Yogeetha, B S; Haupt, L M; McKenzie, K; Sutherland, H G; Okolicsyani, R K; Lea, R A; Maher, B H; Chan, R C K; Shum, D H K; Griffiths, L R

2013-09-01

Here, we investigate the genetic basis of human memory in healthy individuals and the potential role of two polymorphisms, previously implicated in memory function. We have explored aspects of retrospective and prospective memory including semantic, short term, working and long-term memory in conjunction with brain derived neurotrophic factor (BDNF) and tumor necrosis factor-alpha (TNF-α). The memory scores for healthy individuals in the population were obtained for each memory type and the population was genotyped via restriction fragment length polymorphism for the BDNF rs6265 (Val66Met) SNP and via pyrosequencing for the TNF-α rs113325588 SNP. Using univariate ANOVA, a significant association of the BDNF polymorphism with visual and spatial memory retention and a significant association of the TNF-α polymorphism was observed with spatial memory retention. In addition, a significant interactive effect between BDNF and TNF-α polymorphisms was observed in spatial memory retention. In practice visual memory involves spatial information and the two memory systems work together, however our data demonstrate that individuals with the Val/Val BDNF genotype have poorer visual memory but higher spatial memory retention, indicating a level of interaction between TNF-α and BDNF in spatial memory retention. This is the first study to use genetic analysis to determine the interaction between BDNF and TNF-α in relation to memory in normal adults and provides important information regarding the effect of genetic determinants and gene interactions on human memory.

A multiple-point spatially weighted k-NN method for object-based classification

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

2016-10-01

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

Applications of Bayesian spectrum representation in acoustics

NASA Astrophysics Data System (ADS)

Botts, Jonathan M.

This dissertation utilizes a Bayesian inference framework to enhance the solution of inverse problems where the forward model maps to acoustic spectra. A Bayesian solution to filter design inverts a acoustic spectra to pole-zero locations of a discrete-time filter model. Spatial sound field analysis with a spherical microphone array is a data analysis problem that requires inversion of spatio-temporal spectra to directions of arrival. As with many inverse problems, a probabilistic analysis results in richer solutions than can be achieved with ad-hoc methods. In the filter design problem, the Bayesian inversion results in globally optimal coefficient estimates as well as an estimate the most concise filter capable of representing the given spectrum, within a single framework. This approach is demonstrated on synthetic spectra, head-related transfer function spectra, and measured acoustic reflection spectra. The Bayesian model-based analysis of spatial room impulse responses is presented as an analogous problem with equally rich solution. The model selection mechanism provides an estimate of the number of arrivals, which is necessary to properly infer the directions of simultaneous arrivals. Although, spectrum inversion problems are fairly ubiquitous, the scope of this dissertation has been limited to these two and derivative problems. The Bayesian approach to filter design is demonstrated on an artificial spectrum to illustrate the model comparison mechanism and then on measured head-related transfer functions to show the potential range of application. Coupled with sampling methods, the Bayesian approach is shown to outperform least-squares filter design methods commonly used in commercial software, confirming the need for a global search of the parameter space. The resulting designs are shown to be comparable to those that result from global optimization methods, but the Bayesian approach has the added advantage of a filter length estimate within the same unified framework. The application to reflection data is useful for representing frequency-dependent impedance boundaries in finite difference acoustic simulations. Furthermore, since the filter transfer function is a parametric model, it can be modified to incorporate arbitrary frequency weighting and account for the band-limited nature of measured reflection spectra. Finally, the model is modified to compensate for dispersive error in the finite difference simulation, from the filter design process. Stemming from the filter boundary problem, the implementation of pressure sources in finite difference simulation is addressed in order to assure that schemes properly converge. A class of parameterized source functions is proposed and shown to offer straightforward control of residual error in the simulation. Guided by the notion that the solution to be approximated affects the approximation error, sources are designed which reduce residual dispersive error to the size of round-off errors. The early part of a room impulse response can be characterized by a series of isolated plane waves. Measured with an array of microphones, plane waves map to a directional response of the array or spatial intensity map. Probabilistic inversion of this response results in estimates of the number and directions of image source arrivals. The model-based inversion is shown to avoid ambiguities associated with peak-finding or inspection of the spatial intensity map. For this problem, determining the number of arrivals in a given frame is critical for properly inferring the state of the sound field. This analysis is effectively compression of the spatial room response, which is useful for analysis or encoding of the spatial sound field. Parametric, model-based formulations of these problems enhance the solution in all cases, and a Bayesian interpretation provides a principled approach to model comparison and parameter estimation. v

Ambient infrared laser ablation mass spectrometry (AIRLAB-MS) of live plant tissue with plume capture by continuous flow solvent probe.

PubMed

O'Brien, Jeremy T; Williams, Evan R; Holman, Hoi-Ying N

2015-03-03

A new experimental setup for spatially resolved ambient infrared laser ablation-mass spectrometry (AIRLAB-MS) that uses an infrared microscope with an infinity-corrected reflective objective and a continuous flow solvent probe coupled to a Fourier transform ion cyclotron resonance mass spectrometer is described. The efficiency of material transfer from the sample to the electrospray ionization emitter was determined using glycerol/methanol droplets containing 1 mM nicotine and is ∼50%. This transfer efficiency is significantly higher than values reported for similar techniques. Laser desorption does not induce fragmentation of biomolecules in droplets containing bradykinin, leucine enkephalin and myoglobin, but loss of the heme group from myoglobin occurs as a result of the denaturing solution used. An application of AIRLAB-MS to biological materials is demonstrated for tobacco leaves. Chemical components are identified from the spatially resolved mass spectra of the ablated plant material, including nicotine and uridine. The reproducibility of measurements made using AIRLAB-MS on plant material was demonstrated by the ablation of six closely spaced areas (within 2 × 2 mm) on a young tobacco leaf, and the results indicate a standard deviation of <10% in the uridine signal obtained for each area. The spatial distribution of nicotine was measured for selected leaf areas and variation in the relative nicotine levels (15-100%) was observed. Comparative analysis of the nicotine distribution was demonstrated for two tobacco plant varieties, a genetically modified plant and its corresponding wild-type, indicating generally higher nicotine levels in the mutant.

Microfabricated gas chromatograph for on-site determinations of TCE in indoor air arising from vapor intrusion. 2. Spatial/temporal monitoring.

PubMed

Kim, Sun Kyu; Burris, David R; Bryant-Genevier, Jonathan; Gorder, Kyle A; Dettenmaier, Erik M; Zellers, Edward T

2012-06-05

We demonstrate the use of two prototype Si-microfabricated gas chromatographs (μGC) for continuous, short-term measurements of indoor trichloroethylene (TCE) vapor concentrations related to the investigation of TCE vapor intrusion (VI) in two houses. In the first house, with documented TCE VI, temporal variations in TCE air concentrations were monitored continuously for up to 48 h near the primary VI entry location under different levels of induced differential pressure (relative to the subslab). Concentrations ranged from 0.23 to 27 ppb by volume (1.2-150 μg/m(3)), and concentration trends agreed closely with those determined from concurrent reference samples. The sensitivity and temporal resolution of the measurements were sufficiently high to detect transient fluctuations in concentration resulting from short-term changes in variables affecting the extent of VI. Spatial monitoring showed a decreasing TCE concentration gradient with increasing distance from the primary VI entry location. In the second house, with no TCE VI, spatial profiles derived from the μGC prototype data revealed an intentionally hidden source of TCE within a closet, demonstrating the capability for locating non-VI sources. Concentrations measured in this house ranged from 0.51 to 56 ppb (2.7-300 μg/m(3)), in good agreement with reference method values. This first field demonstration of μGC technology for automated, near-real-time, selective VOC monitoring at low- or subppb levels augurs well for its use in short- and long-term on-site analysis of indoor air in support of VI assessments.

Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities.

PubMed

Frelat, Romain; Lindegren, Martin; Denker, Tim Spaanheden; Floeter, Jens; Fock, Heino O; Sguotti, Camilla; Stäbler, Moritz; Otto, Saskia A; Möllmann, Christian

2017-01-01

Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.

Enhancing Children's Spatial and Numerical Skills through a Dynamic Spatial Approach to Early Geometry Instruction: Effects of a 32-Week Intervention

ERIC Educational Resources Information Center

Hawes, Zachary; Moss, Joan; Caswell, Beverly; Naqvi, Sarah; MacKinnon, Sharla

2017-01-01

This study describes the implementation and effects of a 32-week teacher-led spatial reasoning intervention in K-2 classrooms. The intervention targeted spatial visualization skills as an integrated feature of regular mathematics instruction. Compared to an active control group, children in the spatial intervention demonstrated gains in spatial…

Asymmetric coding of categorical spatial relations in both language and vision.

PubMed

Roth, J C; Franconeri, S L

2012-01-01

Describing certain types of spatial relationships between a pair of objects requires that the objects are assigned different "roles" in the relation, e.g., "A is above B" is different than "B is above A." This asymmetric representation places one object in the "target" or "figure" role and the other in the "reference" or "ground" role. Here we provide evidence that this asymmetry may be present not just in spatial language, but also in perceptual representations. More specifically, we describe a model of visual spatial relationship judgment where the designation of the target object within such a spatial relationship is guided by the location of the "spotlight" of attention. To demonstrate the existence of this perceptual asymmetry, we cued attention to one object within a pair by briefly previewing it, and showed that participants were faster to verify the depicted relation when that object was the linguistic target. Experiment 1 demonstrated this effect for left-right relations, and Experiment 2 for above-below relations. These results join several other types of demonstrations in suggesting that perceptual representations of some spatial relations may be asymmetrically coded, and further suggest that the location of selective attention may serve as the mechanism that guides this asymmetry.

Reprint of "Spatial modeling of Cutaneous Leishmaniasis in Iran from 1983 to 2013".

PubMed

Holakouie-Naieni, Kourosh; Mostafavi, Ehsan; Boloorani, Ali Darvishi; Mohebali, Mehdi; Pakzad, Reza

2017-01-01

Cutaneous Leshmaniasis (CL), a parasitic skin infection caused by Leishmania species, is endemic in some regions of Iran. In this study, the effect of location on the incidence and distribution of CL in Iran was studied. We collected datas including the number of Cutaneous Leishmaniasis cases and populations at-risk of disease in Iran's different provinces reported by the Iranian ministry of health and the National Bureau of Statistics, respectively. Spatial modeling was performed using Arc GIS software. Descriptive maps, hotspot analysis, and high/low clustering analysis were used to demonstrate distribution of the cutaneous leishmaniasis, to determine regions at risk of disease's incidence, and to reach the most appropriate method for clustering of disease. The total number of cases of cutaneous leishmaniasis reported through the study period was 589,913. The annual incidence of CL was estimated to be 30.9 per 100,000 in Iranian population. We also demonstrated that Cutaneous leishmaniasis most prominently occurs in regions with dry and desert climates as well as in central parts of Iran. It affected the southwest of Iran between 1983 and 1997, and subsequently developed towards the center and the eastern between 1998 and 2013. Disease hotspots were focused in the provinces of Yazd, Khozestan and Kohgiloyeh-Boyer-Ahmad (p<0.05). No pattern of spatial clustering was observed. Cutaneous leishmaniasis is a major health problem which could be a serious threat for inhabitants who live in high-risk provinces of Iran; much more resources need to be allocated in these areas, to warrant the prevention as well as effectively management of this disease. Copyright © 2016 Elsevier B.V. All rights reserved.

Spatial modeling of cutaneous leishmaniasis in Iran from 1983 to 2013.

PubMed

Holakouie-Naieni, Kourosh; Mostafavi, Ehsan; Boloorani, Ali Darvishi; Mohebali, Mehdi; Pakzad, Reza

2017-02-01

Cutaneous Leshmaniasis (CL), a parasitic skin infection caused by Leishmania species, is endemic in some regions of Iran. In this study, the effect of location on the incidence and distribution of CL in Iran was studied. We collected datas including the number of Cutaneous Leishmaniasis cases and populations at-risk of disease in Iran's different provinces reported by the Iranian ministry of health and the National Bureau of Statistics, respectively. Spatial modeling was performed using Arc GIS software. Descriptive maps, hotspot analysis, and high/low clustering analysis were used to demonstrate distribution of the cutaneous leishmaniasis, to determine regions at risk of disease's incidence, and to reach the most appropriate method for clustering of disease. The total number of cases of cutaneous leishmaniasis reported through the study period was 589,913. The annual incidence of CL was estimated to be 30.9 per 100,000 in Iranian population. We also demonstrated that Cutaneous leishmaniasis most prominently occurs in regions with dry and desert climates as well as in central parts of Iran. It affected the southwest of Iran between 1983 and 1997, and subsequently developed towards the center and the eastern between 1998 and 2013. Disease hotspots were focused in the provinces of Yazd, Khozestan and Kohgiloyeh-Boyer-Ahmad (p<0.05). No pattern of spatial clustering was observed. Cutaneous leishmaniasis is a major health problem which could be a serious threat for inhabitants who live in high-risk provinces of Iran; much more resources need to be allocated in these areas, to warrant the prevention as well as effectively management of this disease. Copyright © 2016 Elsevier B.V. All rights reserved.

Meta-analysis: how does posterior parietal cortex contribute to reasoning?

PubMed Central

Wendelken, Carter

2015-01-01

Reasoning depends on the contribution of posterior parietal cortex (PPC). But PPC is involved in many basic operations—including spatial attention, mathematical cognition, working memory, long-term memory, and language—and the nature of its contribution to reasoning is unclear. Psychological theories of the processes underlying reasoning make divergent claims about the neural systems that are likely to be involved, and better understanding the specific contribution of PPC can help to inform these theories. We set out to address several competing hypotheses, concerning the role of PPC in reasoning: (1) reasoning involves application of formal logic and is dependent on language, with PPC activation for reasoning mainly reflective of linguistic processing; (2) reasoning involves probabilistic computation and is thus dependent on numerical processing mechanisms in PPC; and (3) reasoning is built upon the representation and processing of spatial relations, and PPC activation associated with reasoning reflects spatial processing. We conducted two separate meta-analyses. First, we pooled data from our own studies of reasoning in adults, and examined activation in PPC regions of interest (ROI). Second, we conducted an automated meta-analysis using Neurosynth, in which we examined overlap between activation maps associated with reasoning and maps associated with other key functions of PPC. In both analyses, we observed reasoning-related activation concentrated in the left Inferior Parietal Lobe (IPL). Reasoning maps demonstrated the greatest overlap with mathematical cognition. Maintenance, visuospatial, and phonological processing also demonstrated some overlap with reasoning, but a large portion of the reasoning map did not overlap with the map for any other function. This evidence suggests that the PPC’s contribution to reasoning may be most closely related to its role in mathematical cognition, but that a core component of this contribution may be specific to reasoning. PMID:25653604

Assessing the condition of bayous and estuaries: Bayou Chico Gulf of Mexico demonstration study

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

Dickson, K.; Acevedo, M.; Waller, T.

1995-12-31

A demonstration study was conducted in May 1994 on Bayou Chico to assess the utility of various assessment and measurement endpoints in determining the condition of bayous and estuaries. Bayou Chico has water quality problems attributed to its low flushing rate and urban/industrial land use in its watershed. The sampling scheme assessed the within-sampling station and spatial variability of measurement endpoints. Fourteen sampling stations in Bayou Chico and 3 stations in Pensacola Bay were selected based on an intensified EMAP sampling grid. Time and space coordinated sampling was conducted for: sediment contaminants and properties, sediment toxicity, water quality, benthic infauna,more » zooplankton and phytoplankton populations. Fish and crabs were also collected and analyzed for a suite of biomarkers and organic chemical residues. Primary productivity was measured via the light bottle dark bottle oxygen method and via diurnal oxygen measurements made with continuous recording data sondes. Stream sites were evaluated for water and sediment quality, water and sediment toxicity, benthic invertebrates and fish. Watershed analyses included assessment of land use/landcover (via SPOT and TM images), soils, pollution sources (point and non-point) and hydrography. These data were coordinated via an Arc/Info GIS system for display and spatial analysis. 1994 survey data were used to parameterize environmental fate models such as SWMM (Storm Water Management Model), DYNHYD5 (WASP5 hydrodynamics model) and WASP5 (Water Quality Analysis Simulation Program) to make predictions about the dynamics and fate of chemical contaminants in Bayou Chico. This paper will present an overview, and report on the results in regards to within-site and spatial variability in Bayou Chico. Conclusions on the efficacy of the assessment and measurement endpoints in evaluating the condition (health) of Bayou Chico will be presented.« less

Quantitative Characterisation and Analysis of Siliciclastic Fluvial Depositional Systems Using 3D Digital Outcrop Models

NASA Astrophysics Data System (ADS)

Burnham, Brian Scott

Outcrop analogue studies of fluvial sedimentary systems are often undertaken to identify spatial and temporal characteristics (e.g. stacking patterns, lateral continuity, lithofacies proportions). However, the lateral extent typically exceeds that of the exposure, and/or the true width and thickness are not apparent. Accurate characterisation of fluvial sand bodies is integral for accurate identification and subsequent modelling of aquifer and hydrocarbon reservoir architecture. The studies presented in this thesis utilise techniques that integrate lidar, highresolution photography and differential geospatial measurements, to create accurate three-dimensional (3D) digital outcrop models (DOMs) of continuous 3D and laterally extensive 2D outcrop exposures. The sedimentary architecture of outcrops in the medial portion of a large Distributive Fluvial System (DFS) (Huesca fluvial fan) in the Ebro Basin, north-east Spain, and in the fluvio-deltaic succession of the Breathitt Group in the eastern Appalachian Basin, USA, are evaluated using traditional sedimentological and digital outcrop analytical techniques. The major sand bodies in the study areas are quantitatively analysed to accurately characterise spatial and temporal changes in sand body architecture, from two different outcrop exposure types and scales. Several stochastic reservoir simulations were created to approximate fluvial sand body lithological component and connectivity within the medial portion of the Huesca DFS. Results demonstrate a workflow and current methodology adaptation of digital outcrop techniques required for each study to approximate true geobody widths, thickness and characterise architectural patterns (internal and external) of major fluvial sand bodies interpreted as products of DFSs in the Huesca fluvial fan, and both palaeovalleys and progradational DFSs in the Pikeville and Hyden Formations in the Breathitt Group. The results suggest key geostatistical metrics, which are translatable across any fluvial system that can be used to analyse 3D digital outcrop data, and identify spatial attributes of sand bodies to identify their genetic origin and lithological component within fluvial reservoir systems, and the rock record. 3D quantitative analysis of major sand bodies have allowed more accurate width vs. thickness relationships within the La Serreta area, showing a vertical increase in width and channel-fill facies, and demonstrates a 22% increase of in-channel facies from previous interpretations. Additionally, identification of deposits that are products of a nodal avulsion event have been characterised and are interpreted to be the cause for the increase in width and channel-fill facies. Furthermore, analysis of the Pikeville and Hyden Fms contain sand bodies of stacked distributaries and palaeovalleys, as previously interpreted, and demonstrates that a 3D spatial approach to determine basin-wide architectural trends is integral to identifying the genetic origin, and preservation potential of sand bodies of both palaeovalleys and distributive fluvial systems. The resultant geostatistics assimilated in the thesis demonstrates the efficacy of integrated lidar studies of outcrop analogues, and provide empirical relationships which can be applied to subsurface analogues for reservoir model development and the distribution of both DFS and palaeovalley depositional systems in the rock record.

Note: An improved 3D imaging system for electron-electron coincidence measurements

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

Lin, Yun Fei; Lee, Suk Kyoung; Adhikari, Pradip

We demonstrate an improved imaging system that can achieve highly efficient 3D detection of two electrons in coincidence. The imaging system is based on a fast frame complementary metal-oxide semiconductor camera and a high-speed waveform digitizer. We have shown previously that this detection system is capable of 3D detection of ions and electrons with good temporal and spatial resolution. Here, we show that with a new timing analysis algorithm, this system can achieve an unprecedented dead-time (<0.7 ns) and dead-space (<1 mm) when detecting two electrons. A true zero dead-time detection is also demonstrated.

Note: An improved 3D imaging system for electron-electron coincidence measurements

NASA Astrophysics Data System (ADS)

Lin, Yun Fei; Lee, Suk Kyoung; Adhikari, Pradip; Herath, Thushani; Lingenfelter, Steven; Winney, Alexander H.; Li, Wen

2015-09-01

We demonstrate an improved imaging system that can achieve highly efficient 3D detection of two electrons in coincidence. The imaging system is based on a fast frame complementary metal-oxide semiconductor camera and a high-speed waveform digitizer. We have shown previously that this detection system is capable of 3D detection of ions and electrons with good temporal and spatial resolution. Here, we show that with a new timing analysis algorithm, this system can achieve an unprecedented dead-time (<0.7 ns) and dead-space (<1 mm) when detecting two electrons. A true zero dead-time detection is also demonstrated.

DoE Phase II SBIR: Spectrally-Assisted Vehicle Tracking

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

Villeneuve, Pierre V.

2013-02-28

The goal of this Phase II SBIR is to develop a prototype software package to demonstrate spectrally-aided vehicle tracking performance. The primary application is to demonstrate improved target vehicle tracking performance in complex environments where traditional spatial tracker systems may show reduced performance. Example scenarios in Figure 1 include a) the target vehicle obscured by a large structure for an extended period of time, or b), the target engaging in extreme maneuvers amongst other civilian vehicles. The target information derived from spatial processing is unable to differentiate between the green versus the red vehicle. Spectral signature exploitation enables comparison ofmore » new candidate targets with existing track signatures. The ambiguity in this confusing scenario is resolved by folding spectral analysis results into each target nomination and association processes. Figure 3 shows a number of example spectral signatures from a variety of natural and man-made materials. The work performed over the two-year effort was divided into three general areas: algorithm refinement, software prototype development, and prototype performance demonstration. The tasks performed under this Phase II to accomplish the program goals were as follows: 1. Acquire relevant vehicle target datasets to support prototype. 2. Refine algorithms for target spectral feature exploitation. 3. Implement a prototype multi-hypothesis target tracking software package. 4. Demonstrate and quantify tracking performance using relevant data.« less

'spup' - an R package for uncertainty propagation analysis in spatial environmental modelling

NASA Astrophysics Data System (ADS)

Sawicka, Kasia; Heuvelink, Gerard

2017-04-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Currently, advances in uncertainty propagation and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability and being able to deal with case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the 'spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques, as well as several uncertainty visualization functions. Uncertain environmental variables are represented in the package as objects whose attribute values may be uncertain and described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is also accommodated for. For uncertainty propagation the package has implemented the MC approach with efficient sampling algorithms, i.e. stratified random sampling and Latin hypercube sampling. The design includes facilitation of parallel computing to speed up MC computation. The MC realizations may be used as an input to the environmental models called from R, or externally. Selected visualization methods that are understandable by non-experts with limited background in statistics can be used to summarize and visualize uncertainty about the measured input, model parameters and output of the uncertainty propagation. We demonstrate that the 'spup' package is an effective and easy tool to apply and can be used in multi-disciplinary research and model-based decision support.

Using vadose zone data and spatial statistics to assess the impact of cultivated land and dairy waste lagoons on groundwater contamination

NASA Astrophysics Data System (ADS)

Baram, S.; Ronen, Z.; Kurtzman, D.; Peeters, A.; Dahan, O.

2013-12-01

Land cultivation and dairy waste lagoons are considered to be nonpoint and point sources of groundwater contamination by chloride (Cl-) and nitrate (NO3-). The objective of this work is to introduce a methodology to assess the past and future impacts of such agricultural activities on regional groundwater quality. The method is based on mass balances and on spatial statistical analysis of Cl- and NO3-concentration distributions in the saturated and unsaturated zones. The method enables quantitative analysis of the relation between the locations of pollution point sources and the spatial variability in Cl- and NO3- concentrations in groundwater. The method was applied to the Beer-Tuvia region, Israel, where intensive dairy farming along with land cultivation has been practiced for over 50 years above the local phreatic aquifer. Mass balance calculations accounted for the various groundwater recharge and abstraction sources and sinks in the entire region. The mass balances showed that leachates from lagoons and the cultivated land have contributed 6.0 and 89.4 % of the total mass of Cl- added to the aquifer and 12.6 and 77.4 % of the total mass of NO3-. The chemical composition of the aquifer and vadose zone water suggested that irrigated agricultural activity in the region is the main contributor of Cl- and NO3- to the groundwater. A low spatial correlation between the Cl- and NO3- concentrations in the groundwater and the on-land location of the dairy farms strengthened this assumption, despite the dairy waste lagoon being a point source for groundwater contamination by Cl- and NO3-. Results demonstrate that analyzing vadose zone and groundwater data by spatial statistical analysis methods can significantly contribute to the understanding of the relations between groundwater contaminating sources, and to assessing appropriate remediation steps.

Spatially uniform and nonuniform analyses of electroencephalographic dynamics,with application to the topography of the alpha rhythm

NASA Astrophysics Data System (ADS)

O'Connor, S. C.; Robinson, P. A.

2004-07-01

Corticothalamic dynamics are investigated using a model in which spatial nonuniformities are incorporated via the coupling of spatial eigenmodes. Comparison of spectra generated using the nonuniform analysis with those generated using a uniform one demonstrates that, for most frequencies, local activity is only weakly dependent on activity elsewhere in the cortex; however, dispersion of low-wave-number activity ensures that distant dynamics influence local dynamics at low frequencies (below approximately 2Hz ), and at the alpha frequency (approximately 10Hz ), where propagating signals are inherently weakly damped, and wavelengths are large. When certain model parameters have similar spatial profiles, as is expected from physiology, the low-frequency discrepancies tend to cancel, and the uniform analysis with local parameter values is an adequate approximation to the full nonuniform one across the whole spectrum, at least for large-scale nonuniformities. After comparing the uniform and nonuniform analyses, we consider one possible application of the nonuniform analysis: studying the phenomenon of occipital alpha dominance, whereby the alpha frequency and power are greater at the back of the head (occipitally) than at the front. In order to infer realistic nonuniformities in the model parameters, the uniform version of the model is first fitted to data recorded from 98 normal subjects in a waking, eyes-closed state. This yields a set of parameters at each of five electrode sites along the midline. The inferred parameter nonuniformities are consistent with anatomical and physiological constraints. Introducing these spatial profiles into the full nonuniform model then quantitatively reproduces observed site-dependent variations in the alpha power and frequency. The results confirm that the frequency shift is mainly due to a decrease in the corticothalamic propagation delay, but indicate that the delay nonuniformity cannot account for the observed occipital increase in alpha power; the occipital alpha dominance is due to decreased cortical gains and increased thalamic gains in occipital regions compared to frontal ones.

Addressing the complexity of water chemistry in environmental fate modeling for engineered nanoparticles.

PubMed

Sani-Kast, Nicole; Scheringer, Martin; Slomberg, Danielle; Labille, Jérôme; Praetorius, Antonia; Ollivier, Patrick; Hungerbühler, Konrad

2015-12-01

Engineered nanoparticle (ENP) fate models developed to date - aimed at predicting ENP concentration in the aqueous environment - have limited applicability because they employ constant environmental conditions along the modeled system or a highly specific environmental representation; both approaches do not show the effects of spatial and/or temporal variability. To address this conceptual gap, we developed a novel modeling strategy that: 1) incorporates spatial variability in environmental conditions in an existing ENP fate model; and 2) analyzes the effect of a wide range of randomly sampled environmental conditions (representing variations in water chemistry). This approach was employed to investigate the transport of nano-TiO2 in the Lower Rhône River (France) under numerous sets of environmental conditions. The predicted spatial concentration profiles of nano-TiO2 were then grouped according to their similarity by using cluster analysis. The analysis resulted in a small number of clusters representing groups of spatial concentration profiles. All clusters show nano-TiO2 accumulation in the sediment layer, supporting results from previous studies. Analysis of the characteristic features of each cluster demonstrated a strong association between the water conditions in regions close to the ENP emission source and the cluster membership of the corresponding spatial concentration profiles. In particular, water compositions favoring heteroaggregation between the ENPs and suspended particulate matter resulted in clusters of low variability. These conditions are, therefore, reliable predictors of the eventual fate of the modeled ENPs. The conclusions from this study are also valid for ENP fate in other large river systems. Our results, therefore, shift the focus of future modeling and experimental research of ENP environmental fate to the water characteristic in regions near the expected ENP emission sources. Under conditions favoring heteroaggregation in these regions, the fate of the ENPs can be readily predicted. Copyright © 2014 Elsevier B.V. All rights reserved.

The Development of GIS Educational Resources Sharing among Central Taiwan Universities

NASA Astrophysics Data System (ADS)

Chou, T.-Y.; Yeh, M.-L.; Lai, Y.-C.

2011-09-01

Using GIS in the classroom enhance students' computer skills and explore the range of knowledge. The paper highlights GIS integration on e-learning platform and introduces a variety of abundant educational resources. This research project will demonstrate tools for e-learning environment and delivers some case studies for learning interaction from Central Taiwan Universities. Feng Chia University (FCU) obtained a remarkable academic project subsidized by Ministry of Education and developed e-learning platform for excellence in teaching/learning programs among Central Taiwan's universities. The aim of the project is to integrate the educational resources of 13 universities in central Taiwan. FCU is serving as the hub of Center University. To overcome the problem of distance, e-platforms have been established to create experiences with collaboration enhanced learning. The e-platforms provide coordination of web service access among the educational community and deliver GIS educational resources. Most of GIS related courses cover the development of GIS, principles of cartography, spatial data analysis and overlaying, terrain analysis, buffer analysis, 3D GIS application, Remote Sensing, GPS technology, and WebGIS, MobileGIS, ArcGIS manipulation. In each GIS case study, students have been taught to know geographic meaning, collect spatial data and then use ArcGIS software to analyze spatial data. On one of e-Learning platforms provide lesson plans and presentation slides. Students can learn Arc GIS online. As they analyze spatial data, they can connect to GIS hub to get data they need including satellite images, aerial photos, and vector data. Moreover, e-learning platforms provide solutions and resources. Different levels of image scales have been integrated into the systems. Multi-scale spatial development and analyses in Central Taiwan integrate academic research resources among CTTLRC partners. Thus, establish decision-making support mechanism in teaching and learning. Accelerate communication, cooperation and sharing among academic units

An assessment of the performance and quality control procedures of PACS workstation monitors used in Irish radiology departments

NASA Astrophysics Data System (ADS)

Wade, Cherrie; Brennan, Patrick C.; Mc Entee, Mark F.

2005-04-01

Diagnostic efficacy in soft-copy reporting relies heavily on the quality of workstation monitors and an investigation performed in 2002 demonstrated that CRT monitors in Dublin imaging departments were not operating at optimal levels. The current work examines the performance of CRTs being used in Dublin and other parts of Ireland to establish if problems reported in the earlier work have been rectified. All hospitals performing soft-copy reporting for general radiology using CRTs were included in the work. Examination of ambient lighting, calibration of monitors and analysis of CRT performance using the SMPTE test pattern and a selection of the AAPM test images was performed. Maximum luminance, spatial uniformity of luminance, temporal luminance stability, gamma, geometry, sharpness, veiling glare and spatial resolution of each monitor was evaluated. Ambient lighting in all reporting areas was within recommended levels. All the monitors were calibrated appropriately and were performing at acceptable levels for maximum luminance and temporal stability and only one of the thirty-three investigated failed to reach the standard for spatial uniformity. In contrast a number of the CRTs investigated showed poor adherence to acceptable levels for geometrical distortions, veiling glare and spatial resolution all of which are important influencers of image quality. Gamma values also appeared to be low for a number of monitors but this interpretation is provisional and subject to the establishment of ratified guideline values. The results demonstrate that although some improvement on the previous situation is evident, greater adherence to acceptable levels is required for certain parameters.

Low-loss electron energy loss spectroscopy: An atomic-resolution complement to optical spectroscopies and application to graphene

DOE PAGES

Kapetanakis, Myron; Zhou, Wu; Oxley, Mark P.; ...

2015-09-25

Photon-based spectroscopies have played a central role in exploring the electronic properties of crystalline solids and thin films. They are a powerful tool for probing the electronic properties of nanostructures, but they are limited by lack of spatial resolution. On the other hand, electron-based spectroscopies, e.g., electron energy loss spectroscopy (EELS), are now capable of subangstrom spatial resolution. Core-loss EELS, a spatially resolved analog of x-ray absorption, has been used extensively in the study of inhomogeneous complex systems. In this paper, we demonstrate that low-loss EELS in an aberration-corrected scanning transmission electron microscope, which probes low-energy excitations, combined with amore » theoretical framework for simulating and analyzing the spectra, is a powerful tool to probe low-energy electron excitations with atomic-scale resolution. The theoretical component of the method combines density functional theory–based calculations of the excitations with dynamical scattering theory for the electron beam. We apply the method to monolayer graphene in order to demonstrate that atomic-scale contrast is inherent in low-loss EELS even in a perfectly periodic structure. The method is a complement to optical spectroscopy as it probes transitions entailing momentum transfer. The theoretical analysis identifies the spatial and orbital origins of excitations, holding the promise of ultimately becoming a powerful probe of the structure and electronic properties of individual point and extended defects in both crystals and inhomogeneous complex nanostructures. The method can be extended to probe magnetic and vibrational properties with atomic resolution.« less

Dealing with ocular artifacts on lateralized ERPs in studies of visual-spatial attention and memory: ICA correction versus epoch rejection.

PubMed

Drisdelle, Brandi Lee; Aubin, Sébrina; Jolicoeur, Pierre

2017-01-01

The objective of the present study was to assess the robustness and reliability of independent component analysis (ICA) as a method for ocular artifact correction in electrophysiological studies of visual-spatial attention and memory. The N2pc and sustained posterior contralateral negativity (SPCN), electrophysiological markers of visual-spatial attention and memory, respectively, are lateralized posterior ERPs typically observed following the presentation of lateral stimuli (targets and distractors) along with instructions to maintain fixation on the center of the visual search for the entire trial. Traditionally, trials in which subjects may have displaced their gaze are rejected based on a cutoff threshold, minimizing electrophysiological contamination by saccades. Given the loss of data resulting from rejection, we examined ocular correction by comparing results using standard fixation instructions against a condition where subjects were instructed to shift their gaze toward possible targets. Both conditions were analyzed using a rejection threshold and ICA correction for saccade activity management. Results demonstrate that ICA conserves data that would have otherwise been removed and leaves the underlying neural activity intact, as demonstrated by experimental manipulations previously shown to modulate the N2pc and the SPCN. Not only does ICA salvage and not distort data, but also large eye movements had only subtle effects. Overall, the findings provide convincing evidence for ICA correction for not only special cases (e.g., subjects did not follow fixation instruction) but also as a candidate for standard ocular artifact management in electrophysiological studies interested in visual-spatial attention and memory. © 2016 Society for Psychophysiological Research.

Different Dimensions of Cognitive Style in Typical and Atypical Cognition: New Evidence and a New Measurement Tool.

PubMed

Mealor, Andy D; Simner, Julia; Rothen, Nicolas; Carmichael, Duncan A; Ward, Jamie

2016-01-01

We developed the Sussex Cognitive Styles Questionnaire (SCSQ) to investigate visual and verbal processing preferences and incorporate global/local processing orientations and systemising into a single, comprehensive measure. In Study 1 (N = 1542), factor analysis revealed six reliable subscales to the final 60 item questionnaire: Imagery Ability (relating to the use of visual mental imagery in everyday life); Technical/Spatial (relating to spatial mental imagery, and numerical and technical cognition); Language & Word Forms; Need for Organisation; Global Bias; and Systemising Tendency. Thus, we replicate previous findings that visual and verbal styles are separable, and that types of imagery can be subdivided. We extend previous research by showing that spatial imagery clusters with other abstract cognitive skills, and demonstrate that global/local bias can be separated from systemising. Study 2 validated the Technical/Spatial and Language & Word Forms factors by showing that they affect performance on memory tasks. In Study 3, we validated Imagery Ability, Technical/Spatial, Language & Word Forms, Global Bias, and Systemising Tendency by issuing the SCSQ to a sample of synaesthetes (N = 121) who report atypical cognitive profiles on these subscales. Thus, the SCSQ consolidates research from traditionally disparate areas of cognitive science into a comprehensive cognitive style measure, which can be used in the general population, and special populations.

GIS Data Collection for Pedestrian Facilities and Furniture Using Mapinr for Android

NASA Astrophysics Data System (ADS)

Naharudin, N.; Ahamad, M. S. S.; Sadullah, A. F. M.

2016-09-01

Mobile GIS is introduced to reduce the time taken in completing the field data collection procedure. With the expansion of technology today, mobile GIS is not far behind. It can be integrated with the high-end innovation tools like smartphones. Spatial data capture which deemed to be the toughest stage of a GIS project is made simple with this method. Many studies had demonstrated the usage of mobile GIS in collecting spatial data and this paper discusses how it can be applied in capturing the GPS location of pedestrian furniture and facilities. Although some of the spatial data are available from local agencies, still a more detailed data is needed to create a better data model for this study. This study uses a free android application, MAPinr, which is available on the Google PlayStore to collect spatial data on site. It adopted the GNSS and cellular network positioning to locate the position of the required data. As the application allows the captured data to be exported to a GIS platform, the geometric error of the data was improved. In the end, an authenticated spatial dataset comprising pedestrian facilities and furniture in point and line form will be produced and later be used in a pedestrian network analysis study.

Cross-Scale Molecular Analysis of Chemical Heterogeneity in Shale Rocks

DOE PAGES

Hao, Zhao; Bechtel, Hans A.; Kneafsey, Timothy; ...

2018-02-07

The organic and mineralogical heterogeneity in shale at micrometer and nanometer spatial scales contributes to the quality of gas reserves, gas flow mechanisms and gas production. Here, we demonstrate two molecular imaging approaches based on infrared spectroscopy to obtain mineral and kerogen information at these mesoscale spatial resolutions in large-sized shale rock samples. The first method is a modified microscopic attenuated total reflectance measurement that utilizes a large germanium hemisphere combined with a focal plane array detector to rapidly capture chemical images of shale rock surfaces spanning hundreds of micrometers with micrometer spatial resolution. The second method, synchrotron infrared nano-spectroscopy,more » utilizes a metallic atomic force microscope tip to obtain chemical images of micrometer dimensions but with nanometer spatial resolution. This chemically "deconvoluted" imaging at the nano-pore scale is then used to build a machine learning model to generate a molecular distribution map across scales with a spatial span of 1000 times, which enables high-throughput geochemical characterization in greater details across the nano-pore and micro-grain scales and allows us to identify co-localization of mineral phases with chemically distinct organics and even with gas phase sorbents. Finally, this characterization is fundamental to understand mineral and organic compositions affecting the behavior of shales.« less

Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

PubMed

Chen, Shi; Ilany, Amiyaal; White, Brad J; Sanderson, Michael W; Lanzas, Cristina

2015-01-01

Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways.

Cross-Scale Molecular Analysis of Chemical Heterogeneity in Shale Rocks

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

Hao, Zhao; Bechtel, Hans A.; Kneafsey, Timothy

The organic and mineralogical heterogeneity in shale at micrometer and nanometer spatial scales contributes to the quality of gas reserves, gas flow mechanisms and gas production. Here, we demonstrate two molecular imaging approaches based on infrared spectroscopy to obtain mineral and kerogen information at these mesoscale spatial resolutions in large-sized shale rock samples. The first method is a modified microscopic attenuated total reflectance measurement that utilizes a large germanium hemisphere combined with a focal plane array detector to rapidly capture chemical images of shale rock surfaces spanning hundreds of micrometers with micrometer spatial resolution. The second method, synchrotron infrared nano-spectroscopy,more » utilizes a metallic atomic force microscope tip to obtain chemical images of micrometer dimensions but with nanometer spatial resolution. This chemically "deconvoluted" imaging at the nano-pore scale is then used to build a machine learning model to generate a molecular distribution map across scales with a spatial span of 1000 times, which enables high-throughput geochemical characterization in greater details across the nano-pore and micro-grain scales and allows us to identify co-localization of mineral phases with chemically distinct organics and even with gas phase sorbents. Finally, this characterization is fundamental to understand mineral and organic compositions affecting the behavior of shales.« less

Different Dimensions of Cognitive Style in Typical and Atypical Cognition: New Evidence and a New Measurement Tool

PubMed Central

Mealor, Andy D.; Simner, Julia; Rothen, Nicolas; Carmichael, Duncan A.; Ward, Jamie

2016-01-01

We developed the Sussex Cognitive Styles Questionnaire (SCSQ) to investigate visual and verbal processing preferences and incorporate global/local processing orientations and systemising into a single, comprehensive measure. In Study 1 (N = 1542), factor analysis revealed six reliable subscales to the final 60 item questionnaire: Imagery Ability (relating to the use of visual mental imagery in everyday life); Technical/Spatial (relating to spatial mental imagery, and numerical and technical cognition); Language & Word Forms; Need for Organisation; Global Bias; and Systemising Tendency. Thus, we replicate previous findings that visual and verbal styles are separable, and that types of imagery can be subdivided. We extend previous research by showing that spatial imagery clusters with other abstract cognitive skills, and demonstrate that global/local bias can be separated from systemising. Study 2 validated the Technical/Spatial and Language & Word Forms factors by showing that they affect performance on memory tasks. In Study 3, we validated Imagery Ability, Technical/Spatial, Language & Word Forms, Global Bias, and Systemising Tendency by issuing the SCSQ to a sample of synaesthetes (N = 121) who report atypical cognitive profiles on these subscales. Thus, the SCSQ consolidates research from traditionally disparate areas of cognitive science into a comprehensive cognitive style measure, which can be used in the general population, and special populations. PMID:27191169

Processing and analysis of commercial satellite image data of the nuclear accident near Chernobyl, U.S.S.R.

USGS Publications Warehouse

Sadowski, Franklin G.; Covington, Steven J.

1987-01-01

Advanced digital processing techniques were applied to Landsat-5 Thematic Mapper (TM) data and SPOT highresolution visible (HRV) panchromatic data to maximize the utility of images of a nuclear powerplant emergency at Chernobyl in the Soviet Ukraine. The images demonstrate the unique interpretive capabilities provided by the numerous spectral bands of the Thematic Mapper and the high spatial resolution of the SPOT HRV sensor.

A fast recursive algorithm for molecular dynamics simulation

NASA Technical Reports Server (NTRS)

Jain, A.; Vaidehi, N.; Rodriguez, G.

1993-01-01

The present recursive algorithm for solving molecular systems' dynamical equations of motion employs internal variable models that reduce such simulations' computation time by an order of magnitude, relative to Cartesian models. Extensive use is made of spatial operator methods recently developed for analysis and simulation of the dynamics of multibody systems. A factor-of-450 speedup over the conventional O(N-cubed) algorithm is demonstrated for the case of a polypeptide molecule with 400 residues.

Clutch size variation in Tawny Owls (Strix aluco) from adjacent valley systems: can this be used as a surrogate to investigate temporal and spatial variations in vole density?

Treesearch

Steve J. Petty; Billy L. Fawkes

1997-01-01

Research on Tawny Owls (Strix aluco) in Kielder Forest, northern England, since 1981 demonstrated that field voles (Microtus agrestis) were their most important food. Here, field voles exhibited a 3-4 year cycle of abundance, and mean clutch size in Tawny Owls was significantly related to vole abundance in March. In this analysis...

Effect of Electric Field Gradient on Sub-nanometer Spatial Resolution of Tip-enhanced Raman Spectroscopy

PubMed Central

Meng, Lingyan; Yang, Zhilin; Chen, Jianing; Sun, Mengtao

2015-01-01

Tip-enhanced Raman spectroscopy (TERS) with sub-nanometer spatial resolution has been recently demonstrated experimentally. However, the physical mechanism underlying is still under discussion. Here we theoretically investigate the electric field gradient of a coupled tip-substrate system. Our calculations suggest that the ultra-high spatial resolution of TERS can be partially attributed to the electric field gradient effect owning to its tighter spatial confinement and sensitivity to the infrared (IR)-active of molecules. Particularly, in the case of TERS of flat-lying H2TBPP molecules,we find the electric field gradient enhancement is the dominating factor for the high spatial resolution, which qualitatively coincides with previous experimental report. Our theoretical study offers a new paradigm for understanding the mechanisms of the ultra-high spatial resolution demonstrated in tip-enhanced spectroscopy which is of importance but neglected. PMID:25784161

Speech Cues Contribute to Audiovisual Spatial Integration

PubMed Central

Bishop, Christopher W.; Miller, Lee M.

2011-01-01

Speech is the most important form of human communication but ambient sounds and competing talkers often degrade its acoustics. Fortunately the brain can use visual information, especially its highly precise spatial information, to improve speech comprehension in noisy environments. Previous studies have demonstrated that audiovisual integration depends strongly on spatiotemporal factors. However, some integrative phenomena such as McGurk interference persist even with gross spatial disparities, suggesting that spatial alignment is not necessary for robust integration of audiovisual place-of-articulation cues. It is therefore unclear how speech-cues interact with audiovisual spatial integration mechanisms. Here, we combine two well established psychophysical phenomena, the McGurk effect and the ventriloquist's illusion, to explore this dependency. Our results demonstrate that conflicting spatial cues may not interfere with audiovisual integration of speech, but conflicting speech-cues can impede integration in space. This suggests a direct but asymmetrical influence between ventral ‘what’ and dorsal ‘where’ pathways. PMID:21909378

Geostatistics for spatial genetic structures: study of wild populations of perennial ryegrass.

PubMed

Monestiez, P; Goulard, M; Charmet, G

1994-04-01

Methods based on geostatistics were applied to quantitative traits of agricultural interest measured on a collection of 547 wild populations of perennial ryegrass in France. The mathematical background of these methods, which resembles spatial autocorrelation analysis, is briefly described. When a single variable is studied, the spatial structure analysis is similar to spatial autocorrelation analysis, and a spatial prediction method, called "kriging", gives a filtered map of the spatial pattern over all the sampled area. When complex interactions of agronomic traits with different evaluation sites define a multivariate structure for the spatial analysis, geostatistical methods allow the spatial variations to be broken down into two main spatial structures with ranges of 120 km and 300 km, respectively. The predicted maps that corresponded to each range were interpreted as a result of the isolation-by-distance model and as a consequence of selection by environmental factors. Practical collecting methodology for breeders may be derived from such spatial structures.

A Spatial Analysis and Game Theoretical Approach Over the Disputed Islands in the Aegean Sea

DTIC Science & Technology

2016-06-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited A SPATIAL ANALYSIS ...REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE A SPATIAL ANALYSIS AND GAME THEORETICAL APPROACH OVER THE DISPUTED ISLANDS...including perimeter, area, population, distance to Greece, distance to Turkey, and territorial water area. After applying spatial analysis to two

Signature of phase singularities in diffusive regimes in disordered waveguide lattices: interplay and qualitative analysis.

PubMed

Ghosh, Somnath

2018-05-10

Coexistence and interplay between mesoscopic light dynamics with singular optics in spatially disordered waveguide lattices are reported. Two CW light beams of a 1.55 μm operating wavelength are launched as inputs to 1D waveguide lattices with controllable weak disorder in a complex refractive index profile. Direct observation of phase singularities in the speckle pattern along the length is numerically demonstrated. Quantitative analysis of the onset of such singular behavior and diffusive wave propagation is analyzed for the first time, to the best of our knowledge.

Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System.

PubMed

Roth, Zvi N

2016-01-01

Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream.

Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System

PubMed Central

Roth, Zvi N.

2016-01-01

Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream. PMID:27242455

Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources.

PubMed

Ge, Ruiyang; Wang, Yubao; Zhang, Jipeng; Yao, Li; Zhang, Hang; Long, Zhiying

2016-04-01

As a blind source separation technique, independent component analysis (ICA) has many applications in functional magnetic resonance imaging (fMRI). Although either temporal or spatial prior information has been introduced into the constrained ICA and semi-blind ICA methods to improve the performance of ICA in fMRI data analysis, certain types of additional prior information, such as the sparsity, has seldom been added to the ICA algorithms as constraints. In this study, we proposed a SparseFastICA method by adding the source sparsity as a constraint to the FastICA algorithm to improve the performance of the widely used FastICA. The source sparsity is estimated through a smoothed ℓ0 norm method. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of SparseFastICA and made a performance comparison between SparseFastICA, FastICA and Infomax ICA. Results of the simulated and real fMRI data demonstrated the feasibility and robustness of SparseFastICA for the source separation in fMRI data. Both the simulated and real fMRI experimental results showed that SparseFastICA has better robustness to noise and better spatial detection power than FastICA. Although the spatial detection power of SparseFastICA and Infomax did not show significant difference, SparseFastICA had faster computation speed than Infomax. SparseFastICA was comparable to the Infomax algorithm with a faster computation speed. More importantly, SparseFastICA outperformed FastICA in robustness and spatial detection power and can be used to identify more accurate brain networks than FastICA algorithm. Copyright © 2016 Elsevier B.V. All rights reserved.

Linear optical quantum computing in a single spatial mode.

PubMed

Humphreys, Peter C; Metcalf, Benjamin J; Spring, Justin B; Moore, Merritt; Jin, Xian-Min; Barbieri, Marco; Kolthammer, W Steven; Walmsley, Ian A

2013-10-11

We present a scheme for linear optical quantum computing using time-bin-encoded qubits in a single spatial mode. We show methods for single-qubit operations and heralded controlled-phase (cphase) gates, providing a sufficient set of operations for universal quantum computing with the Knill-Laflamme-Milburn [Nature (London) 409, 46 (2001)] scheme. Our protocol is suited to currently available photonic devices and ideally allows arbitrary numbers of qubits to be encoded in the same spatial mode, demonstrating the potential for time-frequency modes to dramatically increase the quantum information capacity of fixed spatial resources. As a test of our scheme, we demonstrate the first entirely single spatial mode implementation of a two-qubit quantum gate and show its operation with an average fidelity of 0.84±0.07.

Linking Automated Data Analysis and Visualization with Applications in Developmental Biology and High-Energy Physics

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

Ruebel, Oliver

2009-11-20

Knowledge discovery from large and complex collections of today's scientific datasets is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the increasing number of data dimensions and data objects is presenting tremendous challenges for data analysis and effective data exploration methods and tools. Researchers are overwhelmed with data and standard tools are often insufficient to enable effective data analysis and knowledge discovery. The main objective of this thesis is to provide important new capabilities to accelerate scientific knowledge discovery form large, complex, and multivariate scientific data. The research coveredmore » in this thesis addresses these scientific challenges using a combination of scientific visualization, information visualization, automated data analysis, and other enabling technologies, such as efficient data management. The effectiveness of the proposed analysis methods is demonstrated via applications in two distinct scientific research fields, namely developmental biology and high-energy physics.Advances in microscopy, image analysis, and embryo registration enable for the first time measurement of gene expression at cellular resolution for entire organisms. Analysis of high-dimensional spatial gene expression datasets is a challenging task. By integrating data clustering and visualization, analysis of complex, time-varying, spatial gene expression patterns and their formation becomes possible. The analysis framework MATLAB and the visualization have been integrated, making advanced analysis tools accessible to biologist and enabling bioinformatic researchers to directly integrate their analysis with the visualization. Laser wakefield particle accelerators (LWFAs) promise to be a new compact source of high-energy particles and radiation, with wide applications ranging from medicine to physics. To gain insight into the complex physical processes of particle acceleration, physicists model LWFAs computationally. The datasets produced by LWFA simulations are (i) extremely large, (ii) of varying spatial and temporal resolution, (iii) heterogeneous, and (iv) high-dimensional, making analysis and knowledge discovery from complex LWFA simulation data a challenging task. To address these challenges this thesis describes the integration of the visualization system VisIt and the state-of-the-art index/query system FastBit, enabling interactive visual exploration of extremely large three-dimensional particle datasets. Researchers are especially interested in beams of high-energy particles formed during the course of a simulation. This thesis describes novel methods for automatic detection and analysis of particle beams enabling a more accurate and efficient data analysis process. By integrating these automated analysis methods with visualization, this research enables more accurate, efficient, and effective analysis of LWFA simulation data than previously possible.« less

Spatial analysis of under-5 mortality and potential risk factors in the Basse Health and Demographic Surveillance System, the Gambia.

PubMed

Quattrochi, John; Jasseh, Momodou; Mackenzie, Grant; Castro, Marcia C

2015-07-01

To describe the spatial pattern in under-5 mortality rates in the Basse Health and Demographic Surveillance System (BHDSS) and to test for associations between under-5 deaths and biodemographic and socio-economic risk factors. Using data on child survival from 2007 to 2011 in the BHDSS, we mapped under-5 mortality by km(2) . We tested for spatial clustering of high or low death rates using Kulldorff's spatial scan statistic. Associations between child death and a variety of biodemographic and socio-economic factors were assessed with Cox proportional hazards models, and deviance residuals from the best-fitting model were tested for spatial clustering. The overall death rate among children under 5 was 0.0195 deaths per child-year. We found two spatial clusters of high death rates and one spatial cluster of low death rates; children in the two high clusters died at a rate of 0.0264 and 0.0292 deaths per child-year, while in the low cluster, the rate was 0.0144 deaths per child-year. We also found that children born to Fula mothers experienced, on average, a higher hazard of death, whereas children born in the households in the upper two quintiles of asset ownership experienced, on average, a lower hazard of death. After accounting for the spatial distribution of biodemographic and socio-economic characteristics, we found no residual spatial pattern in child mortality risk. This study demonstrates that significant inequality in under-5 death rates can occur within a relatively small area (1100 km(2) ). Risks of under-5 mortality were associated with mother's ethnicity and household wealth. If high mortality clusters persist, then equity concerns may require additional public health efforts in those areas. © 2015 John Wiley & Sons Ltd.

Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

USGS Publications Warehouse

Sleeter, Rachel; Acevedo, William; Soulard, Christopher E.; Sleeter, Benjamin M.

2015-01-01

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.

Identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis by using the Delphi Technique

NASA Astrophysics Data System (ADS)

Halim, N. Z. A.; Sulaiman, S. A.; Talib, K.; Ng, E. G.

2018-02-01

This paper explains the process carried out in identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis. The research was initially a part of a larger research exercise to identify the significance of NDCDB from the legal, technical, role and land-based analysis perspectives. The research methodology of applying the Delphi technique is substantially discussed in this paper. A heterogeneous panel of 14 experts was created to determine the importance of NDCDB from the technical relevance standpoint. Three statements describing the relevant features of NDCDB for spatial analysis were established after three rounds of consensus building. It highlighted the NDCDB’s characteristics such as its spatial accuracy, functions, and criteria as a facilitating tool for spatial analysis. By recognising the relevant features of NDCDB for spatial analysis in this study, practical application of NDCDB for various analysis and purpose can be widely implemented.

Multimodal MSI in Conjunction with Broad Coverage Spatially Resolved MS2 Increases Confidence in Both Molecular Identification and Localization.

PubMed

Veličković, Dušan; Chu, Rosalie K; Carrell, Alyssa A; Thomas, Mathew; Paša-Tolić, Ljiljana; Weston, David J; Anderton, Christopher R

2018-01-02

One critical aspect of mass spectrometry imaging (MSI) is the need to confidently identify detected analytes. While orthogonal tandem MS (e.g., LC-MS 2 ) experiments from sample extracts can assist in annotating ions, the spatial information about these molecules is lost. Accordingly, this could cause mislead conclusions, especially in cases where isobaric species exhibit different distributions within a sample. In this Technical Note, we employed a multimodal imaging approach, using matrix assisted laser desorption/ionization (MALDI)-MSI and liquid extraction surface analysis (LESA)-MS 2 I, to confidently annotate and localize a broad range of metabolites involved in a tripartite symbiosis system of moss, cyanobacteria, and fungus. We found that the combination of these two imaging modalities generated very congruent ion images, providing the link between highly accurate structural information onfered by LESA and high spatial resolution attainable by MALDI. These results demonstrate how this combined methodology could be very useful in differentiating metabolite routes in complex systems.

Superfund, Hedonics, and the Scales of Environmental Justice

NASA Astrophysics Data System (ADS)

Noonan, Douglas S.; Turaga, Rama Mohana R.; Baden, Brett M.

2009-11-01

Environmental justice (EJ) is prominent in environmental policy, yet EJ research is plagued by debates over methodological procedures. A well-established economic approach, the hedonic price method, can offer guidance on one contentious aspect of EJ research: the choice of the spatial unit of analysis. Environmental managers charged with preventing or remedying inequities grapple with these framing problems. This article reviews the theoretical and empirical literature on unit choice in EJ, as well as research employing hedonic pricing to assess the spatial extent of hazardous waste site impacts. The insights from hedonics are demonstrated in a series of EJ analyses for a national inventory of Superfund sites. First, as evidence of injustice exhibits substantial sensitivity to the choice of spatial unit, hedonics suggests some units conform better to Superfund impacts than others. Second, hedonic estimates for a particular site can inform the design of appropriate tests of environmental inequity for that site. Implications for policymakers and practitioners of EJ analyses are discussed.

Membrane Driven Spatial Organization of GPCRs

NASA Astrophysics Data System (ADS)

Mondal, Sayan; Johnston, Jennifer M.; Wang, Hao; Khelashvili, George; Filizola, Marta; Weinstein, Harel

2013-10-01

Spatial organization of G-protein coupled receptors (GPCRs) into dimers and higher order oligomers has been demonstrated in vitro and in vivo. The pharmacological readout was shown to depend on the specific interfaces, but why particular regions of the GPCR structure are involved, and how ligand-determined states change them remains unknown. Here we show why protein-membrane hydrophobic matching is attained upon oligomerization at specific interfaces from an analysis of coarse-grained molecular dynamics simulations of the spontaneous diffusion-interaction of the prototypical beta2-adrenergic (β2AR) receptors in a POPC lipid bilayer. The energy penalty from mismatch is significantly reduced in the spontaneously emerging oligomeric arrays, making the spatial organization of the GPCRs dependent on the pattern of mismatch in the monomer. This mismatch pattern is very different for β2AR compared to the highly homologous and structurally similar β1AR, consonant with experimentally observed oligomerization patterns of β2AR and β1AR. The results provide a mechanistic understanding of the structural context of oligomerization.

Superfund, hedonics, and the scales of environmental justice.

PubMed

Noonan, Douglas S; Turaga, Rama Mohana R; Baden, Brett M

2009-11-01

Environmental justice (EJ) is prominent in environmental policy, yet EJ research is plagued by debates over methodological procedures. A well-established economic approach, the hedonic price method, can offer guidance on one contentious aspect of EJ research: the choice of the spatial unit of analysis. Environmental managers charged with preventing or remedying inequities grapple with these framing problems. This article reviews the theoretical and empirical literature on unit choice in EJ, as well as research employing hedonic pricing to assess the spatial extent of hazardous waste site impacts. The insights from hedonics are demonstrated in a series of EJ analyses for a national inventory of Superfund sites. First, as evidence of injustice exhibits substantial sensitivity to the choice of spatial unit, hedonics suggests some units conform better to Superfund impacts than others. Second, hedonic estimates for a particular site can inform the design of appropriate tests of environmental inequity for that site. Implications for policymakers and practitioners of EJ analyses are discussed.

Line-scan spatially offset Raman spectroscopy for inspecting subsurface food safety and quality

NASA Astrophysics Data System (ADS)

Qin, Jianwei; Chao, Kuanglin; Kim, Moon S.

2016-05-01

This paper presented a method for subsurface food inspection using a newly developed line-scan spatially offset Raman spectroscopy (SORS) technique. A 785 nm laser was used as a Raman excitation source. The line-shape SORS data was collected in a wavenumber range of 0-2815 cm-1 using a detection module consisting of an imaging spectrograph and a CCD camera. A layered sample, which was created by placing a plastic sheet cut from the original container on top of cane sugar, was used to test the capability for subsurface food inspection. A whole set of SORS data was acquired in an offset range of 0-36 mm (two sides of the laser) with a spatial interval of 0.07 mm. Raman spectrum from the cane sugar under the plastic sheet was resolved using self-modeling mixture analysis algorithms, demonstrating the potential of the technique for authenticating foods and ingredients through packaging. The line-scan SORS measurement technique provides a new method for subsurface inspection of food safety and quality.

Separating Bulk and Surface Contributions to Electronic Excited-State Processes in Hybrid Mixed Perovskite Thin Films via Multimodal All-Optical Imaging.

PubMed

Simpson, Mary Jane; Doughty, Benjamin; Das, Sanjib; Xiao, Kai; Ma, Ying-Zhong

2017-07-20

A comprehensive understanding of electronic excited-state phenomena underlying the impressive performance of solution-processed hybrid halide perovskite solar cells requires access to both spatially resolved electronic processes and corresponding sample morphological characteristics. Here, we demonstrate an all-optical multimodal imaging approach that enables us to obtain both electronic excited-state and morphological information on a single optical microscope platform with simultaneous high temporal and spatial resolution. Specifically, images were acquired for the same region of interest in thin films of chloride containing mixed lead halide perovskites (CH 3 NH 3 PbI 3-x Cl x ) using femtosecond transient absorption, time-integrated photoluminescence, confocal reflectance, and transmission microscopies. Comprehensive image analysis revealed the presence of surface- and bulk-dominated contributions to the various images, which describe either spatially dependent electronic excited-state properties or morphological variations across the probed region of the thin films. These results show that PL probes effectively the species near or at the film surface.

Spatial and seasonal heterogeneity of atmospheric particles induced reactive oxygen species in urban areas and the role of water-soluble metals.

PubMed

Gali, Nirmal Kumar; Yang, Fenhuan; Jiang, Sabrina Yanan; Chan, Ka Lok; Sun, Li; Ho, Kin-fai; Ning, Zhi

2015-03-01

Adverse health effects are associated with exposure to atmospheric particulate matter (PM), which carry various chemical constituents and induce both exogenous and endogenous oxidative stress. This study investigated the spatial and seasonal variability of PM-induced ROS at four sites with different characteristics in Hong Kong. Cytotoxicity, exogenous and endogenous ROS was determined on a dose and time dependent analysis. Large spatial variation of ROS was observed with fine PM at urban site showing highest ROS levels while coarse PM at traffic site ranks the top. No consistent seasonal difference was observed for ROS levels among all sites. The highly heterogeneous distribution of PM-induced ROS demonstrates the differential capability of PM to produce oxidative stress, and the need to use appropriate metrics as surrogates of exposure instead of PM mass in epidemiologic studies. Several transition metals were found associated with ROS by different degree illustrating the complexity of mechanisms involved. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatial Heterodyne Observation of Water (SHOW) from a high altitude aircraft

NASA Astrophysics Data System (ADS)

Bourassa, A. E.; Langille, J.; Solheim, B.; Degenstein, D. A.; Letros, D.; Lloyd, N. D.; Loewen, P.

2017-12-01

The Spatial Heterodyne Observations of Water instrument (SHOW) is limb-sounding satellite prototype that is being developed in collaboration between the University of Saskatchewan, York University, the Canadian Space Agency and ABB. The SHOW instrument combines a field-widened SHS with an imaging system to observe limb-scattered sunlight in a vibrational band of water (1363 nm - 1366 nm). Currently, the instrument has been optimized for deployment on NASA's ER-2 aircraft. Flying at an altitude of 70, 000 ft the ER-2 configuration and SHOW viewing geometry provides high spatial resolution (< 500 m) limb-measurements of water vapor in the Upper troposphere and lower stratosphere region. During an observation campaign from July 15 - July 22, the SHOW instrument performed 10 hours of observations from the ER-2. This paper describes the SHOW measurement technique and presents the preliminary analysis and results from these flights. These observations are used to validate the SHOW measurement technique and demonstrate the sampling capabilities of the instrument.

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

Digital Hadron Calorimetry

NASA Astrophysics Data System (ADS)

Bilki, Burak

2018-03-01

The Particle Flow Algorithms attempt to measure each particle in a hadronic jet individually, using the detector providing the best energy/momentum resolution. Therefore, the spatial segmentation of the calorimeter plays a crucial role. In this context, the CALICE Collaboration developed the Digital Hadron Calorimeter. The Digital Hadron Calorimeter uses Resistive Plate Chambers as active media and has a 1-bit resolution (digital) readout of 1 × 1 cm2 pads. The calorimeter was tested with steel and tungsten absorber structures, as well as with no absorber structure, at the Fermilab and CERN test beam facilities over several years. In addition to conventional calorimetric measurements, the Digital Hadron Calorimeter offers detailed measurements of event shapes, rigorous tests of simulation models and various tools for improved performance due to its very high spatial granularity. Here we report on the results from the analysis of pion and positron events. Results of comparisons with the Monte Carlo simulations are also discussed. The analysis demonstrates the unique utilization of detailed event topologies.

Hierarchical patch-based co-registration of differently stained histopathology slides

NASA Astrophysics Data System (ADS)

Yigitsoy, Mehmet; Schmidt, Günter

2017-03-01

Over the past decades, digital pathology has emerged as an alternative way of looking at the tissue at subcellular level. It enables multiplexed analysis of different cell types at micron level. Information about cell types can be extracted by staining sections of a tissue block using different markers. However, robust fusion of structural and functional information from different stains is necessary for reproducible multiplexed analysis. Such a fusion can be obtained via image co-registration by establishing spatial correspondences between tissue sections. Spatial correspondences can then be used to transfer various statistics about cell types between sections. However, the multi-modal nature of images and sparse distribution of interesting cell types pose several challenges for the registration of differently stained tissue sections. In this work, we propose a co-registration framework that efficiently addresses such challenges. We present a hierarchical patch-based registration of intensity normalized tissue sections. Preliminary experiments demonstrate the potential of the proposed technique for the fusion of multi-modal information from differently stained digital histopathology sections.

Optoelectronic scanning system upgrade by energy center localization methods

NASA Astrophysics Data System (ADS)

Flores-Fuentes, W.; Sergiyenko, O.; Rodriguez-Quiñonez, J. C.; Rivas-López, M.; Hernández-Balbuena, D.; Básaca-Preciado, L. C.; Lindner, L.; González-Navarro, F. F.

2016-11-01

A problem of upgrading an optoelectronic scanning system with digital post-processing of the signal based on adequate methods of energy center localization is considered. An improved dynamic triangulation analysis technique is proposed by an example of industrial infrastructure damage detection. A modification of our previously published method aimed at searching for the energy center of an optoelectronic signal is described. Application of the artificial intelligence algorithm of compensation for the error of determining the angular coordinate in calculating the spatial coordinate through dynamic triangulation is demonstrated. Five energy center localization methods are developed and tested to select the best method. After implementation of these methods, digital compensation for the measurement error, and statistical data analysis, a non-parametric behavior of the data is identified. The Wilcoxon signed rank test is applied to improve the result further. For optical scanning systems, it is necessary to detect a light emitter mounted on the infrastructure being investigated to calculate its spatial coordinate by the energy center localization method.

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope

PubMed Central

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-01-01

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications. PMID:26525841

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope.

PubMed

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-11-03

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications.

Microfluidic PDMS on paper (POP) devices.

PubMed

Shangguan, Jin-Wen; Liu, Yu; Pan, Jian-Bin; Xu, Bi-Yi; Xu, Jing-Juan; Chen, Hong-Yuan

2016-12-20

In this paper, we propose a generalized concept of microfluidic polydimethylsiloxane (PDMS) on paper (POP) devices, which combines well the merits of paper chips and PDMS chips. First, we optimized the conditions for accurate PDMS spatial patterning on paper, based on screen printing and a high temperature enabled superfast curing technique, which enables PDMS patterning to an accuracy of tens of microns in less than ten seconds. This, in turn, makes it available for seamless, reversible and reliable integration of the resulting paper layer with other PDMS channel structures. The integrated POP devices allow for both porous paper and smooth channels to be spatially defined on the devices, greatly extending the flexibility for designers to be able to construct powerful functional structures. To demonstrate the versatility of this design, a prototype POP device for the colorimetric analysis of liver function markers, serum protein, alkaline phosphatase (ALP) and aspartate aminotransferase (AST), was constructed. On this POP device, quantitative sample loading, mixing and multiplex analysis have all been realized.

Quantifying Human Visible Color Variation from High Definition Digital Images of Orb Web Spiders.

PubMed

Tapia-McClung, Horacio; Ajuria Ibarra, Helena; Rao, Dinesh

2016-01-01

Digital processing and analysis of high resolution images of 30 individuals of the orb web spider Verrucosa arenata were performed to extract and quantify human visible colors present on the dorsal abdomen of this species. Color extraction was performed with minimal user intervention using an unsupervised algorithm to determine groups of colors on each individual spider, which was then analyzed in order to quantify and classify the colors obtained, both spatially and using energy and entropy measures of the digital images. Analysis shows that the colors cover a small region of the visible spectrum, are not spatially homogeneously distributed over the patterns and from an entropic point of view, colors that cover a smaller region on the whole pattern carry more information than colors covering a larger region. This study demonstrates the use of processing tools to create automatic systems to extract valuable information from digital images that are precise, efficient and helpful for the understanding of the underlying biology.

Estimating commercial property prices: an application of cokriging with housing prices as ancillary information

NASA Astrophysics Data System (ADS)

Montero-Lorenzo, José-María; Larraz-Iribas, Beatriz; Páez, Antonio

2009-12-01

A vast majority of the recent literature on spatial hedonic analysis has been concerned with residential property values, with only very few examples of studies focused on commercial property prices. The dearth of studies can be attributed to some of the challenges faced in the analysis of commercial properties, in particular the scarcity of information compared to residential transactions. In order to address this issue, in this paper we propose the use of cokriging and housing prices as ancillary information to estimate commercial property prices. Cokriging takes into account the spatial autocorrelation structure of property prices, and the use of more abundant information on housing prices helps to improve the accuracy of property value estimates. A case study of Toledo in Spain, a city for which commercial activity stemming from tourism is one of the key elements of the economy in the city, demonstrates that substantial accuracy and precision gains can be obtained from the use of cokriging.

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope

NASA Astrophysics Data System (ADS)

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-11-01

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications.

Quantifying Human Visible Color Variation from High Definition Digital Images of Orb Web Spiders

PubMed Central

Ajuria Ibarra, Helena; Rao, Dinesh

2016-01-01

Digital processing and analysis of high resolution images of 30 individuals of the orb web spider Verrucosa arenata were performed to extract and quantify human visible colors present on the dorsal abdomen of this species. Color extraction was performed with minimal user intervention using an unsupervised algorithm to determine groups of colors on each individual spider, which was then analyzed in order to quantify and classify the colors obtained, both spatially and using energy and entropy measures of the digital images. Analysis shows that the colors cover a small region of the visible spectrum, are not spatially homogeneously distributed over the patterns and from an entropic point of view, colors that cover a smaller region on the whole pattern carry more information than colors covering a larger region. This study demonstrates the use of processing tools to create automatic systems to extract valuable information from digital images that are precise, efficient and helpful for the understanding of the underlying biology. PMID:27902724

Real-time 2D spatially selective MRI experiments: Comparative analysis of optimal control design methods.

PubMed

Maximov, Ivan I; Vinding, Mads S; Tse, Desmond H Y; Nielsen, Niels Chr; Shah, N Jon

2015-05-01

There is an increasing need for development of advanced radio-frequency (RF) pulse techniques in modern magnetic resonance imaging (MRI) systems driven by recent advancements in ultra-high magnetic field systems, new parallel transmit/receive coil designs, and accessible powerful computational facilities. 2D spatially selective RF pulses are an example of advanced pulses that have many applications of clinical relevance, e.g., reduced field of view imaging, and MR spectroscopy. The 2D spatially selective RF pulses are mostly generated and optimised with numerical methods that can handle vast controls and multiple constraints. With this study we aim at demonstrating that numerical, optimal control (OC) algorithms are efficient for the design of 2D spatially selective MRI experiments, when robustness towards e.g. field inhomogeneity is in focus. We have chosen three popular OC algorithms; two which are gradient-based, concurrent methods using first- and second-order derivatives, respectively; and a third that belongs to the sequential, monotonically convergent family. We used two experimental models: a water phantom, and an in vivo human head. Taking into consideration the challenging experimental setup, our analysis suggests the use of the sequential, monotonic approach and the second-order gradient-based approach as computational speed, experimental robustness, and image quality is key. All algorithms used in this work were implemented in the MATLAB environment and are freely available to the MRI community. Copyright © 2015 Elsevier Inc. All rights reserved.

Distributed watershed modeling of design storms to identify nonpoint source loading areas

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

Endreny, T.A.; Wood, E.F.

1999-03-01

Watershed areas that generate nonpoint source (NPS) polluted runoff need to be identified prior to the design of basin-wide water quality projects. Current watershed-scale NPS models lack a variable source area (VSA) hydrology routine, and are therefore unable to identify spatially dynamic runoff zones. The TOPLATS model used a watertable-driven VSA hydrology routine to identify runoff zones in a 17.5 km{sup 2} agricultural watershed in central Oklahoma. Runoff areas were identified in a static modeling framework as a function of prestorm watertable depth and also in a dynamic modeling framework by simulating basin response to 2, 10, and 25 yrmore » return period 6 h design storms. Variable source area expansion occurred throughout the duration of each 6 h storm and total runoff area increased with design storm intensity. Basin-average runoff rates of 1 mm h{sup {minus}1} provided little insight into runoff extremes while the spatially distributed analysis identified saturation excess zones with runoff rates equaling effective precipitation. The intersection of agricultural landcover areas with these saturation excess runoff zones targeted the priority potential NPS runoff zones that should be validated with field visits. These intersected areas, labeled as potential NPS runoff zones, were mapped within the watershed to demonstrate spatial analysis options available in TOPLATS for managing complex distributions of watershed runoff. TOPLATS concepts in spatial saturation excess runoff modelling should be incorporated into NPS management models.« less

A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images

PubMed Central

Tang, Yunwei; Jing, Linhai; Ding, Haifeng

2017-01-01

The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods. PMID:29064416

Sensitivity of landscape metrics to changing scale of remote sensing data in spatial pattern analysis: effect, criticality and scaling.

NASA Astrophysics Data System (ADS)

Xu, C.; Zhao, S.; Zhao, B.

2017-12-01

Spatial heterogeneity is scale-dependent, that is, the quantification and representation of spatial pattern vary with the resolution and extent. Overwhelming practices focused on scale effect of landscape metrics, and predicable scaling relationships found among some of them are thought to be the most effective and precise way to quantify multi-scale characteristics. However, previous studies tended to consider a narrow range of scales, and few focused on the critical threshold of scaling function. Here we examine the scalograms of 38 widely-used landscape-level metrics in a more integral spectrum of grain size among 96 landscapes with various extent (i.e. from 25km2 up towards to 221 km2), which sampled randomly from NLCD product. Our goal is to explore the existence of scaling domain and whether the response of metrics to changing resolution would be influenced by spatial extent. Results clearly show the existence of scaling domain for 13 of them (Type II), while the behaviors of other 13 (Type I) exhibit simple scaling functions and the rest (Type III) demonstrate various forms like no obvious change or fluctuation across the integral spectrum of grain size. In addition, an invariant power law scaling relationship was found between critical resolution and spatial extent for metrics falling into Type II, as the critical resolution is proportional to Eρ (ρ is a constant, and E is the extent). All the scaling exponents (ρ) are positive, suggesting that the critical resolutions for these characteristics of landscape structure can be relaxed as the spatial extent expands. This agrees well with empirical perception that coarser grain size might be allowed for spatial data with larger extent. Furthermore, the parameters of scaling functions for metrics falling into Type I and Type II vary with spatial extent, and power law or logarithmic relationships could be identified between them for some metrics. Our finding support the existence of self-organized criticality for a hierarchically-structured landscape. Although the underlying mechanism driving the scaling relationship remains unclear, it could provide guidance toward general principles in spatial pattern analysis and on selecting the proper resolution to avoid the misrepresentation of spatial pattern and profound biases in further ecological progress research.

Optimal timber harvest scheduling with spatially defined sediment objectives

Treesearch

Jon Hof; Michael Bevers

2000-01-01

This note presents a simple model formulation that focuses on the spatial relationships over time between timber harvesting and sediment levels in water runoff courses throughout the watershed being managed. A hypothetical example is developed to demonstrate the formulation and show how sediment objectives can be spatially defined anywhere in the watershed. Spatial...

RADSS: an integration of GIS, spatial statistics, and network service for regional data mining

NASA Astrophysics Data System (ADS)

Hu, Haitang; Bao, Shuming; Lin, Hui; Zhu, Qing

2005-10-01

Regional data mining, which aims at the discovery of knowledge about spatial patterns, clusters or association between regions, has widely applications nowadays in social science, such as sociology, economics, epidemiology, crime, and so on. Many applications in the regional or other social sciences are more concerned with the spatial relationship, rather than the precise geographical location. Based on the spatial continuity rule derived from Tobler's first law of geography: observations at two sites tend to be more similar to each other if the sites are close together than if far apart, spatial statistics, as an important means for spatial data mining, allow the users to extract the interesting and useful information like spatial pattern, spatial structure, spatial association, spatial outlier and spatial interaction, from the vast amount of spatial data or non-spatial data. Therefore, by integrating with the spatial statistical methods, the geographical information systems will become more powerful in gaining further insights into the nature of spatial structure of regional system, and help the researchers to be more careful when selecting appropriate models. However, the lack of such tools holds back the application of spatial data analysis techniques and development of new methods and models (e.g., spatio-temporal models). Herein, we make an attempt to develop such an integrated software and apply it into the complex system analysis for the Poyang Lake Basin. This paper presents a framework for integrating GIS, spatial statistics and network service in regional data mining, as well as their implementation. After discussing the spatial statistics methods involved in regional complex system analysis, we introduce RADSS (Regional Analysis and Decision Support System), our new regional data mining tool, by integrating GIS, spatial statistics and network service. RADSS includes the functions of spatial data visualization, exploratory spatial data analysis, and spatial statistics. The tool also includes some fundamental spatial and non-spatial database in regional population and environment, which can be updated by external database via CD or network. Utilizing this data mining and exploratory analytical tool, the users can easily and quickly analyse the huge mount of the interrelated regional data, and better understand the spatial patterns and trends of the regional development, so as to make a credible and scientific decision. Moreover, it can be used as an educational tool for spatial data analysis and environmental studies. In this paper, we also present a case study on Poyang Lake Basin as an application of the tool and spatial data mining in complex environmental studies. At last, several concluding remarks are discussed.

Environmental science applications with Rapid Integrated Mapping and analysis System (RIMS)

NASA Astrophysics Data System (ADS)

Shiklomanov, A.; Prusevich, A.; Gordov, E.; Okladnikov, I.; Titov, A.

2016-11-01

The Rapid Integrated Mapping and analysis System (RIMS) has been developed at the University of New Hampshire as an online instrument for multidisciplinary data visualization, analysis and manipulation with a focus on hydrological applications. Recently it was enriched with data and tools to allow more sophisticated analysis of interdisciplinary data. Three different examples of specific scientific applications with RIMS are demonstrated and discussed. Analysis of historical changes in major components of the Eurasian pan-Arctic water budget is based on historical discharge data, gridded observational meteorological fields, and remote sensing data for sea ice area. Express analysis of the extremely hot and dry summer of 2010 across European Russia is performed using a combination of near-real time and historical data to evaluate the intensity and spatial distribution of this event and its socioeconomic impacts. Integrative analysis of hydrological, water management, and population data for Central Asia over the last 30 years provides an assessment of regional water security due to changes in climate, water use and demography. The presented case studies demonstrate the capabilities of RIMS as a powerful instrument for hydrological and coupled human-natural systems research.

What are we ‘tweeting’ about obesity? Mapping tweets with Topic Modeling and Geographic Information System

PubMed Central

Ghosh, Debarchana (Debs); Guha, Rajarshi

2014-01-01

Public health related tweets are difficult to identify in large conversational datasets like Twitter.com. Even more challenging is the visualization and analyses of the spatial patterns encoded in tweets. This study has the following objectives: How can topic modeling be used to identify relevant public health topics such as obesity on Twitter.com? What are the common obesity related themes? What is the spatial pattern of the themes? What are the research challenges of using large conversational datasets from social networking sites? Obesity is chosen as a test theme to demonstrate the effectiveness of topic modeling using Latent Dirichlet Allocation (LDA) and spatial analysis using Geographic Information System (GIS). The dataset is constructed from tweets (originating from the United States) extracted from Twitter.com on obesity-related queries. Examples of such queries are ‘food deserts’, ‘fast food’, and ‘childhood obesity’. The tweets are also georeferenced and time stamped. Three cohesive and meaningful themes such as ‘childhood obesity and schools’, ‘obesity prevention’, and ‘obesity and food habits’ are extracted from the LDA model. The GIS analysis of the extracted themes show distinct spatial pattern between rural and urban areas, northern and southern states, and between coasts and inland states. Further, relating the themes with ancillary datasets such as US census and locations of fast food restaurants based upon the location of the tweets in a GIS environment opened new avenues for spatial analyses and mapping. Therefore the techniques used in this study provide a possible toolset for computational social scientists in general and health researchers in specific to better understand health problems from large conversational datasets. PMID:25126022

What are we 'tweeting' about obesity? Mapping tweets with Topic Modeling and Geographic Information System.

PubMed

Ghosh, Debarchana Debs; Guha, Rajarshi

2013-01-01

Public health related tweets are difficult to identify in large conversational datasets like Twitter.com. Even more challenging is the visualization and analyses of the spatial patterns encoded in tweets. This study has the following objectives: How can topic modeling be used to identify relevant public health topics such as obesity on Twitter.com? What are the common obesity related themes? What is the spatial pattern of the themes? What are the research challenges of using large conversational datasets from social networking sites? Obesity is chosen as a test theme to demonstrate the effectiveness of topic modeling using Latent Dirichlet Allocation (LDA) and spatial analysis using Geographic Information System (GIS). The dataset is constructed from tweets (originating from the United States) extracted from Twitter.com on obesity-related queries. Examples of such queries are 'food deserts', 'fast food', and 'childhood obesity'. The tweets are also georeferenced and time stamped. Three cohesive and meaningful themes such as 'childhood obesity and schools', 'obesity prevention', and 'obesity and food habits' are extracted from the LDA model. The GIS analysis of the extracted themes show distinct spatial pattern between rural and urban areas, northern and southern states, and between coasts and inland states. Further, relating the themes with ancillary datasets such as US census and locations of fast food restaurants based upon the location of the tweets in a GIS environment opened new avenues for spatial analyses and mapping. Therefore the techniques used in this study provide a possible toolset for computational social scientists in general and health researchers in specific to better understand health problems from large conversational datasets.

Determination of the complex refractive index segments of turbid sample with multispectral spatially modulated structured light and models approximation

NASA Astrophysics Data System (ADS)

Meitav, Omri; Shaul, Oren; Abookasis, David

2017-09-01

Spectral data enabling the derivation of a biological tissue sample's complex refractive index (CRI) can provide a range of valuable information in the clinical and research contexts. Specifically, changes in the CRI reflect alterations in tissue morphology and chemical composition, enabling its use as an optical marker during diagnosis and treatment. In the present work, we report a method for estimating the real and imaginary parts of the CRI of a biological sample using Kramers-Kronig (KK) relations in the spatial frequency domain. In this method, phase-shifted sinusoidal patterns at single high spatial frequency are serially projected onto the sample surface at different near-infrared wavelengths while a camera mounted normal to the sample surface acquires the reflected diffuse light. In the offline analysis pipeline, recorded images at each wavelength are converted to spatial phase maps using KK analysis and are then calibrated against phase-models derived from diffusion approximation. The amplitude of the reflected light, together with phase data, is then introduced into Fresnel equations to resolve both real and imaginary segments of the CRI at each wavelength. The technique was validated in tissue-mimicking phantoms with known optical parameters and in mouse models of ischemic injury and heat stress. Experimental data obtained indicate variations in the CRI among brain tissue suffering from injury. CRI fluctuations correlated with alterations in the scattering and absorption coefficients of the injured tissue are demonstrated. This technique for deriving dynamic changes in the CRI of tissue may be further developed as a clinical diagnostic tool and for biomedical research applications. To the best of our knowledge, this is the first report of the estimation of the spectral CRI of a mouse head following injury obtained in the spatial frequency domain.

A Spatially Offset Raman Spectroscopy Method for Non-Destructive Detection of Gelatin-Encapsulated Powders

PubMed Central

Chao, Kuanglin; Dhakal, Sagar; Qin, Jianwei; Peng, Yankun; Schmidt, Walter F.; Kim, Moon S.; Chan, Diane E.

2017-01-01

Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785-nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. As the offset distance was increased, the spectral contribution from the subsurface powder gradually outweighed that of the surface capsule layers, allowing for detection of the encapsulated powders. Containing mixed contributions from the powder and capsule, the SORS spectra for each sample were resolved into pure component spectra using self-modeling mixture analysis (SMA) and the corresponding components were identified using spectral information divergence values. As demonstrated here for detecting chemicals contained inside thick capsule layers, this SORS measurement technique coupled with SMA has the potential to be a reliable non-destructive method for subsurface inspection and authentication of foods, health supplements, and pharmaceutical products that are prepared or packaged with semi-transparent materials. PMID:28335453