Study on homogenization of synthetic GNSS-retrieved IWV time series and its impact on trend estimates with autoregressive noise

NASA Astrophysics Data System (ADS)

Klos, Anna; Pottiaux, Eric; Van Malderen, Roeland; Bock, Olivier; Bogusz, Janusz

2017-04-01

A synthetic benchmark dataset of Integrated Water Vapour (IWV) was created within the activity of "Data homogenisation" of sub-working group WG3 of COST ES1206 Action. The benchmark dataset was created basing on the analysis of IWV differences retrieved by Global Positioning System (GPS) International GNSS Service (IGS) stations using European Centre for Medium-Range Weather Forecats (ECMWF) reanalysis data (ERA-Interim). Having analysed a set of 120 series of IWV differences (ERAI-GPS) derived for IGS stations, we delivered parameters of a number of gaps and breaks for every certain station. Moreover, we estimated values of trends, significant seasonalities and character of residuals when deterministic model was removed. We tested five different noise models and found that a combination of white and autoregressive processes of first order describes the stochastic part with a good accuracy. Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different types of noise: white as well as combination of white and autoregressive processes. We also added few strictly defined offsets, creating three variants of synthetic dataset: easy, less-complicated and fully-complicated. The 'Easy' dataset included seasonal signals (annual, semi-annual, 3 and 4 months if present for a particular station), offsets and white noise. The 'Less-complicated' dataset included above-mentioned, as well as the combination of white and first order autoregressive processes (AR(1)+WH). The 'Fully-complicated' dataset included, beyond above, a trend and gaps. In this research, we show the impact of manual homogenisation on the estimates of trend and its error. We also cross-compare the results for three above-mentioned datasets, as the synthetized noise type might have a significant influence on manual homogenisation. Therefore, it might mostly affect the values of trend and their uncertainties when inappropriately handled. In a future, the synthetic dataset we present is going to be used as a benchmark to test various statistical tools in terms of homogenisation task.

A Study of the Effects of School Size and Single-Sex Education in English Schools

ERIC Educational Resources Information Center

Spielhofer, Thomas; Benton, Tom; Schagen, Sandie

2004-01-01

National value-added datasets have recently become available that record a pupil's progress from Key Stage 2 right through to GCSE. Such a dataset is clearly a useful tool for assessing the impact various characteristics of secondary schools have on pupil performance. This paper reports on a research project which involved the use of a variety of…

PDF added value of a high resolution climate simulation for precipitation

NASA Astrophysics Data System (ADS)

Soares, Pedro M. M.; Cardoso, Rita M.

2015-04-01

General Circulation Models (GCMs) are models suitable to study the global atmospheric system, its evolution and response to changes in external forcing, namely to increasing emissions of CO2. However, the resolution of GCMs, of the order of 1o, is not sufficient to reproduce finer scale features of the atmospheric flow related to complex topography, coastal processes and boundary layer processes, and higher resolution models are needed to describe observed weather and climate. The latter are known as Regional Climate Models (RCMs) and are widely used to downscale GCMs results for many regions of the globe and are able to capture physically consistent regional and local circulations. Most of the RCMs evaluations rely on the comparison of its results with observations, either from weather stations networks or regular gridded datasets, revealing the ability of RCMs to describe local climatic properties, and assuming most of the times its higher performance in comparison with the forcing GCMs. The additional climatic details given by RCMs when compared with the results of the driving models is usually named as added value, and it's evaluation is still scarce and controversial in the literuature. Recently, some studies have proposed different methodologies to different applications and processes to characterize the added value of specific RCMs. A number of examples reveal that some RCMs do add value to GCMs in some properties or regions, and also the opposite, elighnening that RCMs may add value to GCM resuls, but improvements depend basically on the type of application, model setup, atmospheric property and location. The precipitation can be characterized by histograms of daily precipitation, or also known as probability density functions (PDFs). There are different strategies to evaluate the quality of both GCMs and RCMs in describing the precipitation PDFs when compared to observations. Here, we present a new method to measure the PDF added value obtained from dynamical downscaling, based on simple PDF skill scores. The measure can assess the full quality of the PDFs and at the same time integrates a flexible manner to weight differently the PDF tails. In this study we apply the referred method to characaterize the PDF added value of a high resolution simulation with the WRF model. Results from a WRF climate simulation centred at the Iberian Penisnula with two nested grids, a larger one at 27km and a smaller one at 9km. This simulation is forced by ERA-Interim. The observational data used covers from rain gauges precipitation records to observational regular grids of daily precipitation. Two regular gridded precipitation datasets are used. A Portuguese grid precipitation dataset developed at 0.2°× 0.2°, from observed rain gauges daily precipitation. A second one corresponding to the ENSEMBLES observational gridded dataset for Europe, which includes daily precipitation values at 0.25°. The analisys shows an important PDF added value from the higher resolution simulation, regarding the full PDF and the extremes. This method shows higher potential to be applied to other simulation exercises and to evaluate other variables.

Extended Kd distributions for freshwater environment.

PubMed

Boyer, Patrick; Wells, Claire; Howard, Brenda

2018-06-18

Many of the freshwater K d values required for quantifying radionuclide transfer in the environment (e.g. ERICA Tool, Symbiose modelling platform) are either poorly reported in the literature or not available. To partially address this deficiency, Working Group 4 of the IAEA program MODARIA (2012-2015) has completed an update of the freshwater K d databases and K d distributions given in TRS 472 (IAEA, 2010). Over 2300 new values for 27 new elements were added to the dataset and 270 new K d values were added for the 25 elements already included in TRS 472 (IAEA, 2010). For 49 chemical elements, the K d values have been classified according to three solid-liquid exchange conditions (adsorption, desorption and field) as was previously carried out in TRS 472. Additionally, the K d values were classified into two environmental components (suspended and deposited sediments). Each combination (radionuclide x component x condition) was associated with log-normal distributions when there was at least ten K d values in the dataset and to a geometric mean when there was less than ten values. The enhanced K d dataset shows that K d values for suspended sediments are significantly higher than for deposited sediments and that the variability of K d distributions are higher for deposited than for suspended sediments. For suspended sediments in field conditions, the variability of K d distributions can be significantly reduced as a function of the suspended load that explains more than 50% of the variability of the K d datasets of U, Si, Mo, Pb, S, Se, Cd, Ca, B, K, Ra and Po. The distinction between adsorption and desorption conditions is justified for deterministic calculations because the geometric means are systematically greater in desorption conditions. Conversely, this distinction is less relevant for probabilistic calculations due to systematic overlapping between the K d distributions of these two conditions. Copyright © 2018. Published by Elsevier Ltd.

Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD

PubMed Central

Lorenz, David J.; Nieto-Lugilde, Diego; Blois, Jessica L.; Fitzpatrick, Matthew C.; Williams, John W.

2016-01-01

Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950–2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850–2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity. PMID:27377537

Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD.

PubMed

Lorenz, David J; Nieto-Lugilde, Diego; Blois, Jessica L; Fitzpatrick, Matthew C; Williams, John W

2016-07-05

Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950-2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850-2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.

Measuring Teacher Effectiveness with the Pennsylvania Value-Added Assessment System

ERIC Educational Resources Information Center

Bowen, Naomi

2017-01-01

The purpose of this research was to determine if the Pennsylvania Value-Added Assessment System Average Growth Index (PVAAS AGI) scores, derived from standardized tests and calculated for Pennsylvania schools, provide a valid and reliable assessment of teacher effectiveness, as these scores are currently used to derive 15% of the annual…

Do lab-derived distribution coefficient values of pesticides match distribution coefficient values determined from column and field-scale experiments? A critical analysis of relevant literature.

PubMed

Vereecken, H; Vanderborght, J; Kasteel, R; Spiteller, M; Schäffer, A; Close, M

2011-01-01

In this study, we analyzed sorption parameters for pesticides that were derived from batch and column or batch and field experiments. The batch experiments analyzed in this study were run with the same pesticide and soil as in the column and field experiments. We analyzed the relationship between the pore water velocity of the column and field experiments, solute residence times, and sorption parameters, such as the organic carbon normalized distribution coefficient ( ) and the mass exchange coefficient in kinetic models, as well as the predictability of sorption parameters from basic soil properties. The batch/column analysis included 38 studies with a total of 139 observations. The batch/field analysis included five studies, resulting in a dataset of 24 observations. For the batch/column data, power law relationships between pore water velocity, residence time, and sorption constants were derived. The unexplained variability in these equations was reduced, taking into account the saturation status and the packing status (disturbed-undisturbed) of the soil sample. A new regression equation was derived that allows estimating the values derived from column experiments using organic matter and bulk density with an value of 0.56. Regression analysis of the batch/column data showed that the relationship between batch- and column-derived values depends on the saturation status and packing of the soil column. Analysis of the batch/field data showed that as the batch-derived value becomes larger, field-derived values tend to be lower than the corresponding batch-derived values, and vice versa. The present dataset also showed that the variability in the ratio of batch- to column-derived value increases with increasing pore water velocity, with a maximum value approaching 3.5. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.

Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations

NASA Astrophysics Data System (ADS)

di Luca, Alejandro; de Elía, Ramón; Laprise, René

2012-03-01

Regional Climate Models (RCMs) constitute the most often used method to perform affordable high-resolution regional climate simulations. The key issue in the evaluation of nested regional models is to determine whether RCM simulations improve the representation of climatic statistics compared to the driving data, that is, whether RCMs add value. In this study we examine a necessary condition that some climate statistics derived from the precipitation field must satisfy in order that the RCM technique can generate some added value: we focus on whether the climate statistics of interest contain some fine spatial-scale variability that would be absent on a coarser grid. The presence and magnitude of fine-scale precipitation variance required to adequately describe a given climate statistics will then be used to quantify the potential added value (PAV) of RCMs. Our results show that the PAV of RCMs is much higher for short temporal scales (e.g., 3-hourly data) than for long temporal scales (16-day average data) due to the filtering resulting from the time-averaging process. PAV is higher in warm season compared to cold season due to the higher proportion of precipitation falling from small-scale weather systems in the warm season. In regions of complex topography, the orographic forcing induces an extra component of PAV, no matter the season or the temporal scale considered. The PAV is also estimated using high-resolution datasets based on observations allowing the evaluation of the sensitivity of changing resolution in the real climate system. The results show that RCMs tend to reproduce relatively well the PAV compared to observations although showing an overestimation of the PAV in warm season and mountainous regions.

Microtopographic characterization of ice-wedge polygon landscape in Barrow, Alaska: a digital map of troughs, rims, centers derived from high resolution (0.25 m) LiDAR data

DOE Data Explorer

Gangodagamage, Chandana; Wullschleger, Stan

2014-07-03

The dataset represents microtopographic characterization of the ice-wedge polygon landscape in Barrow, Alaska. Three microtopographic features are delineated using 0.25 m high resolution digital elevation dataset derived from LiDAR. The troughs, rims, and centers are the three categories in this classification scheme. The polygon troughs are the surface expression of the ice-wedges that are in lower elevations than the interior polygon. The elevated shoulders of the polygon interior immediately adjacent to the polygon troughs are the polygon rims for the low center polygons. In case of high center polygons, these features are the topographic highs. In this classification scheme, both topographic highs and rims are considered as polygon rims. The next version of the dataset will include more refined classification scheme including separate classes for rims ad topographic highs. The interior part of the polygon just adjacent to the polygon rims are the polygon centers.

Value-Added Models and the Measurement of Teacher Productivity. CALDER Working Paper No. 54

ERIC Educational Resources Information Center

Harris, Douglas; Sass, Tim; Semykina, Anastasia

2010-01-01

Research on teacher productivity, and recently developed accountability systems for teachers, rely on value-added models to estimate the impact of teachers on student performance. The authors test many of the central assumptions required to derive value-added models from an underlying structural cumulative achievement model and reject nearly all…

Molprobity's ultimate rotamer-library distributions for model validation.

PubMed

Hintze, Bradley J; Lewis, Steven M; Richardson, Jane S; Richardson, David C

2016-09-01

Here we describe the updated MolProbity rotamer-library distributions derived from an order-of-magnitude larger and more stringently quality-filtered dataset of about 8000 (vs. 500) protein chains, and we explain the resulting changes and improvements to model validation as seen by users. To include only side-chains with satisfactory justification for their given conformation, we added residue-specific filters for electron-density value and model-to-density fit. The combined new protocol retains a million residues of data, while cleaning up false-positive noise in the multi- χ datapoint distributions. It enables unambiguous characterization of conformational clusters nearly 1000-fold less frequent than the most common ones. We describe examples of local interactions that favor these rare conformations, including the role of authentic covalent bond-angle deviations in enabling presumably strained side-chain conformations. Further, along with favored and outlier, an allowed category (0.3-2.0% occurrence in reference data) has been added, analogous to Ramachandran validation categories. The new rotamer distributions are used for current rotamer validation in MolProbity and PHENIX, and for rotamer choice in PHENIX model-building and refinement. The multi-dimensional χ distributions and Top8000 reference dataset are freely available on GitHub. These rotamers are termed "ultimate" because data sampling and quality are now fully adequate for this task, and also because we believe the future of conformational validation should integrate side-chain with backbone criteria. Proteins 2016; 84:1177-1189. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

MolProbity’s Ultimate Rotamer-Library Distributions for Model Validation

PubMed Central

Hintze, Bradley J.; Lewis, Steven M.; Richardson, Jane S.; Richardson, David C.

2016-01-01

Here we describe the updated MolProbity rotamer-library distributions derived from an order-of-magnitude larger and more stringently quality-filtered dataset of about 8000 (vs. 500) protein chains, and we explain the resulting changes and improvements to model validation as seen by users. To include only sidechains with satisfactory justification for their given conformation, we added residue-specific filters for electron-density value and model-to-density fit. The combined new protocol retains a million residues of data, while cleaning up false-positive noise in the multi-χ datapoint distributions. It enables unambiguous characterization of conformational clusters nearly 1000-fold less frequent than the most common ones. We describe examples of local interactions that favor these rare conformations, including the role of authentic covalent bond-angle deviations in enabling presumably strained sidechain conformations. Further, along with favored and outlier, an allowed category (0.3% to 2.0% occurrence in reference data) has been added, analogous to Ramachandran validation categories. The new rotamer distributions are used for current rotamer validation in Mol-Probity and PHENIX, and for rotamer choice in PHENIX model-building and refinement. The multi-dimensional χ distributions and Top8000 reference dataset are freely available on GitHub. These rotamers are termed “ultimate” because data sampling and quality are now fully adequate for this task, and also because we believe the future of conformational validation should integrate sidechain with backbone criteria. PMID:27018641

Genetic overlap between Alzheimer’s disease and Parkinson’s disease at the MAPT locus

PubMed Central

Desikan, Rahul S.; Schork, Andrew J.; Wang, Yunpeng; Witoelar, Aree; Sharma, Manu; McEvoy, Linda K.; Holland, Dominic; Brewer, James B.; Chen, Chi-Hua; Thompson, Wesley K.; Harold, Denise; Williams, Julie; Owen, Michael J.; O’Donovan, Michael C.; Pericak-Vance, Margaret A.; Mayeux, Richard; Haines, Jonathan L.; Farrer, Lindsay A.; Schellenberg, Gerard D.; Heutink, Peter; Singleton, Andrew B.; Brice, Alexis; Wood, Nicolas W.; Hardy, John; Martinez, Maria; Choi, Seung Hoi; DeStefano, Anita; Ikram, M. Arfan; Bis, Joshua C.; Smith, Albert; Fitzpatrick, Annette L.; Launer, Lenore; van Duijn, Cornelia; Seshadri, Sudha; Ulstein, Ingun Dina; Aarsland, Dag; Fladby, Tormod; Djurovic, Srdjan; Hyman, Bradley T.; Snaedal, Jon; Stefansson, Hreinn; Stefansson, Kari; Gasser, Thomas; Andreassen, Ole A.; Dale, Anders M.

2015-01-01

We investigated genetic overlap between Alzheimer’s disease (AD) and Parkinson’s disease (PD). Using summary statistics (p-values) from large recent genomewide association studies (GWAS) (total n = 89,904 individuals), we sought to identify single nucleotide polymorphisms (SNPs) associating with both AD and PD. We found and replicated association of both AD and PD with the A allele of rs393152 within the extended MAPT region on chromosome 17 (meta analysis p-value across 5 independent AD cohorts = 1.65 × 10−7). In independent datasets, we found a dose-dependent effect of the A allele of rs393152 on intra-cerebral MAPT transcript levels and volume loss within the entorhinal cortex and hippocampus. Our findings identify the tau-associated MAPT locus as a site of genetic overlap between AD and PD and extending prior work, we show that the MAPT region increases risk of Alzheimer’s neurodegeneration. PMID:25687773

MWRRET Value-Added Product: The Retrieval of Liquid Water Path and Precipitable Water Vapor from Microwave Radiometer (MWR) Datasets

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

KL Gaustad; DD Turner

2007-09-30

This report provides a short description of the Atmospheric Radiation Measurement (ARM) microwave radiometer (MWR) RETrievel (MWRRET) Value-Added Product (VAP) algorithm. This algorithm utilizes complimentary physical and statistical retrieval methods and applies brightness temperature offsets to reduce spurious liquid water path (LWP) bias in clear skies resulting in significantly improved precipitable water vapor (PWV) and LWP retrievals. We present a general overview of the technique, input parameters, output products, and describe data quality checks. A more complete discussion of the theory and results is given in Turner et al. (2007b).

Multivariate Protein Signatures of Pre-Clinical Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Plasma Proteome Dataset

PubMed Central

Johnstone, Daniel; Milward, Elizabeth A.; Berretta, Regina; Moscato, Pablo

2012-01-01

Background Recent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD. Methods and Findings We focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3×10−13). We applied a multivariate approach based on combinatorial optimization ((α,β)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering “meta-features,” representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%. Conclusions Applying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages. PMID:22485168

Selection-Fusion Approach for Classification of Datasets with Missing Values

PubMed Central

Ghannad-Rezaie, Mostafa; Soltanian-Zadeh, Hamid; Ying, Hao; Dong, Ming

2010-01-01

This paper proposes a new approach based on missing value pattern discovery for classifying incomplete data. This approach is particularly designed for classification of datasets with a small number of samples and a high percentage of missing values where available missing value treatment approaches do not usually work well. Based on the pattern of the missing values, the proposed approach finds subsets of samples for which most of the features are available and trains a classifier for each subset. Then, it combines the outputs of the classifiers. Subset selection is translated into a clustering problem, allowing derivation of a mathematical framework for it. A trade off is established between the computational complexity (number of subsets) and the accuracy of the overall classifier. To deal with this trade off, a numerical criterion is proposed for the prediction of the overall performance. The proposed method is applied to seven datasets from the popular University of California, Irvine data mining archive and an epilepsy dataset from Henry Ford Hospital, Detroit, Michigan (total of eight datasets). Experimental results show that classification accuracy of the proposed method is superior to those of the widely used multiple imputations method and four other methods. They also show that the level of superiority depends on the pattern and percentage of missing values. PMID:20212921

QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1.

PubMed

Comelli, Nieves C; Duchowicz, Pablo R; Castro, Eduardo A

2014-10-01

The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (-logIC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure D-optimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (Rtest2). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method. Copyright © 2014 Elsevier B.V. All rights reserved.

Adding the missing piece: Spitzer imaging of the HSC-Deep/PFS fields

NASA Astrophysics Data System (ADS)

Sajina, Anna; Bezanson, Rachel; Capak, Peter; Egami, Eiichi; Fan, Xiaohui; Farrah, Duncan; Greene, Jenny; Goulding, Andy; Lacy, Mark; Lin, Yen-Ting; Liu, Xin; Marchesini, Danilo; Moutard, Thibaud; Ono, Yoshiaki; Ouchi, Masami; Sawicki, Marcin; Strauss, Michael; Surace, Jason; Whitaker, Katherine

2018-05-01

We propose to observe a total of 7sq.deg. to complete the Spitzer-IRAC coverage of the HSC-Deep survey fields. These fields are the sites of the PrimeFocusSpectrograph (PFS) galaxy evolution survey which will provide spectra of wide wavelength range and resolution for almost all M* galaxies at z 0.7-1.7, and extend out to z 7 for targeted samples. Our fields already have deep broadband and narrowband photometry in 12 bands spanning from u through K and a wealth of other ancillary data. We propose completing the matching depth IRAC observations in the extended COSMOS, ELAIS-N1 and Deep2-3 fields. By complementing existing Spitzer coverage, this program will lead to an unprecedended in spectro-photometric coverage dataset across a total of 15 sq.deg. This dataset will have significant legacy value as it samples a large enough cosmic volume to be representative of the full range of environments, but also doing so with sufficient information content per galaxy to confidently derive stellar population characteristics. This enables detailed studies of the growth and quenching of galaxies and their supermassive black holes in the context of a galaxy's local and large scale environment.

Anomalous Diffusion Measured by a Twice-Refocused Spin Echo Pulse Sequence: Analysis Using Fractional Order Calculus

PubMed Central

2011-01-01

Purpose To theoretically develop and experimentally validate a formulism based on a fractional order calculus (FC) diffusion model to characterize anomalous diffusion in brain tissues measured with a twice-refocused spin-echo (TRSE) pulse sequence. Materials and Methods The FC diffusion model is the fractional order generalization of the Bloch-Torrey equation. Using this model, an analytical expression was derived to describe the diffusion-induced signal attenuation in a TRSE pulse sequence. To experimentally validate this expression, a set of diffusion-weighted (DW) images was acquired at 3 Tesla from healthy human brains using a TRSE sequence with twelve b-values ranging from 0 to 2,600 s/mm2. For comparison, DW images were also acquired using a Stejskal-Tanner diffusion gradient in a single-shot spin-echo echo planar sequence. For both datasets, a Levenberg-Marquardt fitting algorithm was used to extract three parameters: diffusion coefficient D, fractional order derivative in space β, and a spatial parameter μ (in units of μm). Using adjusted R-squared values and standard deviations, D, β and μ values and the goodness-of-fit in three specific regions of interest (ROI) in white matter, gray matter, and cerebrospinal fluid were evaluated for each of the two datasets. In addition, spatially resolved parametric maps were assessed qualitatively. Results The analytical expression for the TRSE sequence, derived from the FC diffusion model, accurately characterized the diffusion-induced signal loss in brain tissues at high b-values. In the selected ROIs, the goodness-of-fit and standard deviations for the TRSE dataset were comparable with the results obtained from the Stejskal-Tanner dataset, demonstrating the robustness of the FC model across multiple data acquisition strategies. Qualitatively, the D, β, and μ maps from the TRSE dataset exhibited fewer artifacts, reflecting the improved immunity to eddy currents. Conclusion The diffusion-induced signal attenuation in a TRSE pulse sequence can be described by an FC diffusion model at high b-values. This model performs equally well for data acquired from the human brain tissues with a TRSE pulse sequence or a conventional Stejskal-Tanner sequence. PMID:21509877

Anomalous diffusion measured by a twice-refocused spin echo pulse sequence: analysis using fractional order calculus.

PubMed

Gao, Qing; Srinivasan, Girish; Magin, Richard L; Zhou, Xiaohong Joe

2011-05-01

To theoretically develop and experimentally validate a formulism based on a fractional order calculus (FC) diffusion model to characterize anomalous diffusion in brain tissues measured with a twice-refocused spin-echo (TRSE) pulse sequence. The FC diffusion model is the fractional order generalization of the Bloch-Torrey equation. Using this model, an analytical expression was derived to describe the diffusion-induced signal attenuation in a TRSE pulse sequence. To experimentally validate this expression, a set of diffusion-weighted (DW) images was acquired at 3 Tesla from healthy human brains using a TRSE sequence with twelve b-values ranging from 0 to 2600 s/mm(2). For comparison, DW images were also acquired using a Stejskal-Tanner diffusion gradient in a single-shot spin-echo echo planar sequence. For both datasets, a Levenberg-Marquardt fitting algorithm was used to extract three parameters: diffusion coefficient D, fractional order derivative in space β, and a spatial parameter μ (in units of μm). Using adjusted R-squared values and standard deviations, D, β, and μ values and the goodness-of-fit in three specific regions of interest (ROIs) in white matter, gray matter, and cerebrospinal fluid, respectively, were evaluated for each of the two datasets. In addition, spatially resolved parametric maps were assessed qualitatively. The analytical expression for the TRSE sequence, derived from the FC diffusion model, accurately characterized the diffusion-induced signal loss in brain tissues at high b-values. In the selected ROIs, the goodness-of-fit and standard deviations for the TRSE dataset were comparable with the results obtained from the Stejskal-Tanner dataset, demonstrating the robustness of the FC model across multiple data acquisition strategies. Qualitatively, the D, β, and μ maps from the TRSE dataset exhibited fewer artifacts, reflecting the improved immunity to eddy currents. The diffusion-induced signal attenuation in a TRSE pulse sequence can be described by an FC diffusion model at high b-values. This model performs equally well for data acquired from the human brain tissues with a TRSE pulse sequence or a conventional Stejskal-Tanner sequence. Copyright © 2011 Wiley-Liss, Inc.

No association between SNP rs498055 on chromosome 10 and late-onset Alzheimer disease in multiple datasets.

PubMed

Liang, Xueying; Schnetz-Boutaud, Nathalie; Bartlett, Jackie; Allen, Melissa J; Gwirtsman, Harry; Schmechel, Don E; Carney, Regina M; Gilbert, John R; Pericak-Vance, Margaret A; Haines, Jonathan L

2008-01-01

SNP rs498055 in the predicted gene LOC439999 on chromosome 10 was recently identified as being strongly associated with late-onset Alzheimer disease (LOAD). This SNP falls within a chromosomal region that has engendered continued interest generated from both preliminary genetic linkage and candidate gene studies. To independently evaluate this interesting candidate SNP we examined four independent datasets, three family-based and one case-control. All the cases were late-onset AD Caucasian patients with minimum age at onset >or= 60 years. None of the three family samples or the combined family-based dataset showed association in either allelic or genotypic family-based association tests at p < 0.05. Both original and OSA two-point LOD scores were calculated. However, there was no evidence indicating linkage no matter what covariates were applied (the highest LOD score was 0.82). The case-control dataset did not demonstrate any association between this SNP and AD (all p-values > 0.52). Our results do not confirm the previous association, but are consistent with a more recent negative association result that used family-based association tests to examine the effect of this SNP in two family datasets. Thus we conclude that rs498055 is not associated with an increased risk of LOAD.

School system evaluation by value added analysis under endogeneity.

PubMed

Manzi, Jorge; San Martín, Ernesto; Van Bellegem, Sébastien

2014-01-01

Value added is a common tool in educational research on effectiveness. It is often modeled as a (prediction of a) random effect in a specific hierarchical linear model. This paper shows that this modeling strategy is not valid when endogeneity is present. Endogeneity stems, for instance, from a correlation between the random effect in the hierarchical model and some of its covariates. This paper shows that this phenomenon is far from exceptional and can even be a generic problem when the covariates contain the prior score attainments, a typical situation in value added modeling. Starting from a general, model-free definition of value added, the paper derives an explicit expression of the value added in an endogeneous hierarchical linear Gaussian model. Inference on value added is proposed using an instrumental variable approach. The impact of endogeneity on the value added and the estimated value added is calculated accurately. This is also illustrated on a large data set of individual scores of about 200,000 students in Chile.

Integrative missing value estimation for microarray data.

PubMed

Hu, Jianjun; Li, Haifeng; Waterman, Michael S; Zhou, Xianghong Jasmine

2006-10-12

Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples. We present the integrative Missing Value Estimation method (iMISS) by incorporating information from multiple reference microarray datasets to improve missing value estimation. For each gene with missing data, we derive a consistent neighbor-gene list by taking reference data sets into consideration. To determine whether the given reference data sets are sufficiently informative for integration, we use a submatrix imputation approach. Our experiments showed that iMISS can significantly and consistently improve the accuracy of the state-of-the-art Local Least Square (LLS) imputation algorithm by up to 15% improvement in our benchmark tests. We demonstrated that the order-statistics-based integrative imputation algorithms can achieve significant improvements over the state-of-the-art missing value estimation approaches such as LLS and is especially good for imputing microarray datasets with a limited number of samples, high rates of missing data, or very noisy measurements. With the rapid accumulation of microarray datasets, the performance of our approach can be further improved by incorporating larger and more appropriate reference datasets.

Expanding the biomass derived chemical space

PubMed Central

Brun, Nicolas; Hesemann, Peter

2017-01-01

Biorefinery aims at the conversion of biomass and renewable feedstocks into fuels and platform chemicals, in analogy to conventional oil refinery. In the past years, the scientific community has defined a number of primary building blocks that can be obtained by direct biomass decomposition. However, the large potential of this “renewable chemical space” to contribute to the generation of value added bio-active compounds and materials still remains unexplored. In general, biomass derived building blocks feature a diverse range of chemical functionalities. In order to be integrated into value-added compounds, they require additional functionalization and/or covalent modification thereby generating secondary building blocks. The latter can be thus regarded as functional components of bio-active molecules or materials and represent an expansion of the renewable chemical space. This perspective highlights the most recent developments and opportunities for the synthesis of secondary biomass derived building blocks and their application to the preparation of value added products. PMID:28959397

MWRRET Value-Added Product: The Retrieval of Liquid Water Path and Precipitable Water Vapor from Microwave Radiometer (MWR) Datasets May 2009

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

Gaustad, KL; Turner, DD

2009-05-30

This report provides a short description of the Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) microwave radiometer (MWR) RETrievel (MWRRET) value-added product (VAP) algorithm. This algorithm utilizes a complementary physical retrieval method and applies brightness temperature offsets to reduce spurious liquid water path (LWP) bias in clear skies resulting in significantly improved precipitable water vapor (PWV) and LWP retrievals. We present a general overview of the technique, input parameters, output products, and describe data quality checks. A more complete discussion of the theory and results is given in Turner et al. (2007b).

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry.

PubMed

Nait Aicha, Ahmed; Englebienne, Gwenn; van Schooten, Kimberley S; Pijnappels, Mirjam; Kröse, Ben

2018-05-22

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data.

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

PubMed Central

Englebienne, Gwenn; Pijnappels, Mirjam

2018-01-01

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data. PMID:29786659

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study.

PubMed

AbdelRahman, Samir E; Zhang, Mingyuan; Bray, Bruce E; Kawamoto, Kensaku

2014-05-27

The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time. Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak. The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%. The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.

The NASA-GES-DISC Satellite Data/Products Access, Distribution, Services and Dissemination to Users

NASA Technical Reports Server (NTRS)

Vicente, Gilberto A.

2005-01-01

The NASA/GES/DISC/DAAC is a virtual data portal that provides convenient access to Atmospheric, Oceanic and Land datasets and value added products from various current NASA missions and instruments as well as heritage datasets from AIRS/AMSU/HSB, AVHRR, CZCS, LIMS, MODIS, MSU, OCTS, SeaWiFS, SORCE, SSI, TOMS, TOVS, UARS and TRMM. The GES-DISC-DAAC also provided a variety of services that allow users to analyze and visualize gridded data interactively online without having to download any data.

12 CFR 1267.1 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... means generally a financial contract the value of which is derived from the values of one or more referenced assets, rates, or indices of asset values, or credit-related events. Derivative contracts include... removing the definitions for “Investment grade” and “NRSRO” and adding in correct alphabetical order a...

Evaluation, Calibration and Comparison of the Precipitation-Runoff Modeling System (PRMS) National Hydrologic Model (NHM) Using Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) Gridded Datasets

NASA Astrophysics Data System (ADS)

Norton, P. A., II; Haj, A. E., Jr.

2014-12-01

The United States Geological Survey is currently developing a National Hydrologic Model (NHM) to support and facilitate coordinated and consistent hydrologic modeling efforts at the scale of the continental United States. As part of this effort, the Geospatial Fabric (GF) for the NHM was created. The GF is a database that contains parameters derived from datasets that characterize the physical features of watersheds. The GF was used to aggregate catchments and flowlines defined in the National Hydrography Dataset Plus dataset for more than 100,000 hydrologic response units (HRUs), and to establish initial parameter values for input to the Precipitation-Runoff Modeling System (PRMS). Many parameter values are adjusted in PRMS using an automated calibration process. Using these adjusted parameter values, the PRMS model estimated variables such as evapotranspiration (ET), potential evapotranspiration (PET), snow-covered area (SCA), and snow water equivalent (SWE). In order to evaluate the effectiveness of parameter calibration, and model performance in general, several satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) gridded datasets including ET, PET, SCA, and SWE were compared to PRMS-simulated values. The MODIS and SNODAS data were spatially averaged for each HRU, and compared to PRMS-simulated ET, PET, SCA, and SWE values for each HRU in the Upper Missouri River watershed. Default initial GF parameter values and PRMS calibration ranges were evaluated. Evaluation results, and the use of MODIS and SNODAS datasets to update GF parameter values and PRMS calibration ranges, are presented and discussed.

Validation of systems biology derived molecular markers of renal donor organ status associated with long term allograft function.

PubMed

Perco, Paul; Heinzel, Andreas; Leierer, Johannes; Schneeberger, Stefan; Bösmüller, Claudia; Oberhuber, Rupert; Wagner, Silvia; Engler, Franziska; Mayer, Gert

2018-05-03

Donor organ quality affects long term outcome after renal transplantation. A variety of prognostic molecular markers is available, yet their validity often remains undetermined. A network-based molecular model reflecting donor kidney status based on transcriptomics data and molecular features reported in scientific literature to be associated with chronic allograft nephropathy was created. Significantly enriched biological processes were identified and representative markers were selected. An independent kidney pre-implantation transcriptomics dataset of 76 organs was used to predict estimated glomerular filtration rate (eGFR) values twelve months after transplantation using available clinical data and marker expression values. The best-performing regression model solely based on the clinical parameters donor age, donor gender, and recipient gender explained 17% of variance in post-transplant eGFR values. The five molecular markers EGF, CD2BP2, RALBP1, SF3B1, and DDX19B representing key molecular processes of the constructed renal donor organ status molecular model in addition to the clinical parameters significantly improved model performance (p-value = 0.0007) explaining around 33% of the variability of eGFR values twelve months after transplantation. Collectively, molecular markers reflecting donor organ status significantly add to prediction of post-transplant renal function when added to the clinical parameters donor age and gender.

A preliminary comparison between TOVS and GOME level 2 ozone data

NASA Astrophysics Data System (ADS)

Rathman, William; Monks, Paul S.; Llewellyn-Jones, David; Burrows, John P.

1997-09-01

A preliminary comparison between total column ozone concentration values derived from TIROS Operational Vertical Sounder (TOVS) and Global Ozone Monitoring Experiment (GOME) has been carried out. Two comparisons of ozone datasets have been made: a) TOVS ozone analysis maps vs. GOME level 2 data; b) TOVS data located at Northern Hemisphere Ground Ozone Stations (NHGOS) vs. GOME data. Both analyses consistently showed an offset in the value of the total column ozone between the datasets [for analyses a) 35 Dobson Units (DU); and for analyses b) 10 DU], despite a good correlation between the spatial and temporal features of the datasets. A noticeably poor correlation in the latitudinal bands 10°/20° North and 10°/20° South was observed—the reasons for which are discussed. The smallest region which was statistically representative of the ozone value correlation dataset of TOVS data at NHGOS and GOME level-2 data was determined to be a region that was enclosed by effective radius of 0.75 arc-degrees (83.5km).

Linking the Long Tail of Data: A Bottoms-up Approach to Connecting Scientific Research

NASA Astrophysics Data System (ADS)

Jacob, B.; Arctur, D. K.

2016-12-01

Highly curated ontologies are often developed for big scientific data, but the long tail of research data rarely receives the same treatment. The learning curve for Semantic Web technology is steep, and the value of linking each long-tail data set to known taxonomies and ontologies in isolation rarely justifies the level of effort required to bring a Knowledge Engineer into the project. We present an approach that takes a bottoms-up approach of producing a Linked Data model of datasets mechanically, inferring the shape and structure of the data from the original format, and adding derived variables and semantic linkages via iterative, interactive refinements of that model. In this way, the vast corpus of small but rich scientific data becomes part of the greater linked web of knowledge, and the connectivity of that data can be iteratively improved over time.

You have more capital than you think.

PubMed

Merton, Robert C

2005-11-01

Senior executives typically delegate the responsibility for managing a firm's derivatives portfolio to in-house financial experts and the company's financial advisers. That's a strategic blunder, argues this Nobel laureate, because the inventiveness of modern financial markets makes it possible for companies to double or even triple their capacity to invest in their strategic assets and competencies. Risks fall into two categories: either a company adds value by assuming them on behalf of its shareholders or it does not. By hedging or insuring against non-value-adding risks with derivative securities and contracts, thereby removing them from what the author calls the risk balance sheet, managers can release equity capital for assuming more value-adding risk. This is not just a theoretical possibility. One innovation-the interest rate swap, introduced about 20 years ago-has already enabled the banking industry to dramatically increase its capacity for adding value to each dollar of invested equity capital. With the range of derivative instruments growing, there is no reason why other companies could not similarly remove strategic risks, potentially creating billions of dollars in shareholder value. The possibilities are especially important for private companies that have no access to public equity markets and therefore cannot easily increase their equity capital by issuing more shares. The author describes how derivative contracts of various kinds are already being employed strategically to mitigate or eliminate various risks. He also shows how companies can use the risk balance sheet to identify risks they should not bear directly and to determine how much equity capacity they can release for assuming more value-adding risk.

MicroRNA array normalization: an evaluation using a randomized dataset as the benchmark.

PubMed

Qin, Li-Xuan; Zhou, Qin

2014-01-01

MicroRNA arrays possess a number of unique data features that challenge the assumption key to many normalization methods. We assessed the performance of existing normalization methods using two microRNA array datasets derived from the same set of tumor samples: one dataset was generated using a blocked randomization design when assigning arrays to samples and hence was free of confounding array effects; the second dataset was generated without blocking or randomization and exhibited array effects. The randomized dataset was assessed for differential expression between two tumor groups and treated as the benchmark. The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark. Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%. Adding a batch adjustment step before normalization further reduced the number of false positive markers while maintaining a similar number of true positive markers, which resulted in a false discovery rate of 32% to 48%, depending on the specific normalization method. We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays.

MicroRNA Array Normalization: An Evaluation Using a Randomized Dataset as the Benchmark

PubMed Central

Qin, Li-Xuan; Zhou, Qin

2014-01-01

MicroRNA arrays possess a number of unique data features that challenge the assumption key to many normalization methods. We assessed the performance of existing normalization methods using two microRNA array datasets derived from the same set of tumor samples: one dataset was generated using a blocked randomization design when assigning arrays to samples and hence was free of confounding array effects; the second dataset was generated without blocking or randomization and exhibited array effects. The randomized dataset was assessed for differential expression between two tumor groups and treated as the benchmark. The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark. Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%. Adding a batch adjustment step before normalization further reduced the number of false positive markers while maintaining a similar number of true positive markers, which resulted in a false discovery rate of 32% to 48%, depending on the specific normalization method. We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays. PMID:24905456

NOAA Big Data Partnership RFI

NASA Astrophysics Data System (ADS)

de la Beaujardiere, J.

2014-12-01

In February 2014, the US National Oceanic and Atmospheric Administration (NOAA) issued a Big Data Request for Information (RFI) from industry and other organizations (e.g., non-profits, research laboratories, and universities) to assess capability and interest in establishing partnerships to position a copy of NOAA's vast data holdings in the Cloud, co-located with easy and affordable access to analytical capabilities. This RFI was motivated by a number of concerns. First, NOAA's data facilities do not necessarily have sufficient network infrastructure to transmit all available observations and numerical model outputs to all potential users, or sufficient infrastructure to support simultaneous computation by many users. Second, the available data are distributed across multiple services and data facilities, making it difficult to find and integrate data for cross-domain analysis and decision-making. Third, large datasets require users to have substantial network, storage, and computing capabilities of their own in order to fully interact with and exploit the latent value of the data. Finally, there may be commercial opportunities for value-added products and services derived from our data. Putting a working copy of data in the Cloud outside of NOAA's internal networks and infrastructures should reduce demands and risks on our systems, and should enable users to interact with multiple datasets and create new lines of business (much like the industries built on government-furnished weather or GPS data). The NOAA Big Data RFI therefore solicited information on technical and business approaches regarding possible partnership(s) that -- at no net cost to the government and minimum impact on existing data facilities -- would unleash the commercial potential of its environmental observations and model outputs. NOAA would retain the master archival copy of its data. Commercial partners would not be permitted to charge fees for access to the NOAA data they receive, but would be able to develop and sell value-added products and services. This effort is still very much in the initial market research phase and has complexity in technical, business, and technical domains. This paper will discuss the current status of the activity and potential next steps.

Publicly Releasing a Large Simulation Dataset with NDS Labs

NASA Astrophysics Data System (ADS)

Goldbaum, Nathan

2016-03-01

Optimally, all publicly funded research should be accompanied by the tools, code, and data necessary to fully reproduce the analysis performed in journal articles describing the research. This ideal can be difficult to attain, particularly when dealing with large (>10 TB) simulation datasets. In this lightning talk, we describe the process of publicly releasing a large simulation dataset to accompany the submission of a journal article. The simulation was performed using Enzo, an open source, community-developed N-body/hydrodynamics code and was analyzed using a wide range of community- developed tools in the scientific Python ecosystem. Although the simulation was performed and analyzed using an ecosystem of sustainably developed tools, we enable sustainable science using our data by making it publicly available. Combining the data release with the NDS Labs infrastructure allows a substantial amount of added value, including web-based access to analysis and visualization using the yt analysis package through an IPython notebook interface. In addition, we are able to accompany the paper submission to the arXiv preprint server with links to the raw simulation data as well as interactive real-time data visualizations that readers can explore on their own or share with colleagues during journal club discussions. It is our hope that the value added by these services will substantially increase the impact and readership of the paper.

A New Decision Tree to Solve the Puzzle of Alzheimer's Disease Pathogenesis Through Standard Diagnosis Scoring System.

PubMed

Kumar, Ashwani; Singh, Tiratha Raj

2017-03-01

Alzheimer's disease (AD) is a progressive, incurable and terminal neurodegenerative disorder of the brain and is associated with mutations in amyloid precursor protein, presenilin 1, presenilin 2 or apolipoprotein E, but its underlying mechanisms are still not fully understood. Healthcare sector is generating a large amount of information corresponding to diagnosis, disease identification and treatment of an individual. Mining knowledge and providing scientific decision-making for the diagnosis and treatment of disease from the clinical dataset are therefore increasingly becoming necessary. The current study deals with the construction of classifiers that can be human readable as well as robust in performance for gene dataset of AD using a decision tree. Models of classification for different AD genes were generated according to Mini-Mental State Examination scores and all other vital parameters to achieve the identification of the expression level of different proteins of disorder that may possibly determine the involvement of genes in various AD pathogenesis pathways. The effectiveness of decision tree in AD diagnosis is determined by information gain with confidence value (0.96), specificity (92 %), sensitivity (98 %) and accuracy (77 %). Besides this functional gene classification using different parameters and enrichment analysis, our finding indicates that the measures of all the gene assess in single cohorts are sufficient to diagnose AD and will help in the prediction of important parameters for other relevant assessments.

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar

PubMed Central

Wang, Dongliang; Xin, Xiaoping; Shao, Quanqin; Brolly, Matthew; Zhu, Zhiliang; Chen, Jin

2017-01-01

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass (R2 = 0.340, root-mean-square error (RMSE) = 81.89 g·m−2, and relative error of 14.1%). The improvement of multiple regressions to the R2 and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns. PMID:28106819

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar.

PubMed

Wang, Dongliang; Xin, Xiaoping; Shao, Quanqin; Brolly, Matthew; Zhu, Zhiliang; Chen, Jin

2017-01-19

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass ( R ² = 0.340, root-mean-square error (RMSE) = 81.89 g·m -2 , and relative error of 14.1%). The improvement of multiple regressions to the R ² and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns.

Hagedorn Temperature of AdS5/CFT4 via Integrability

NASA Astrophysics Data System (ADS)

Harmark, Troels; Wilhelm, Matthias

2018-02-01

We establish a framework for calculating the Hagedorn temperature of AdS5/CFT4 via integrability. Concretely, we derive the thermodynamic Bethe ansatz equations that yield the Hagedorn temperature of planar N =4 super Yang-Mills theory at any value of the 't Hooft coupling. We solve these equations perturbatively at weak coupling via the associated Y system, confirming the known results at tree level and one-loop order as well as deriving the previously unknown two-loop Hagedorn temperature. Finally, we comment on solving the equations at finite coupling.

The CMEMS L3 scatterometer wind product

NASA Astrophysics Data System (ADS)

de Kloe, Jos; Stoffelen, Ad; Verhoef, Anton

2017-04-01

Within the Copernicus Marine Environment Monitoring Service KNMI produces several ocean surface Level 3 wind products. These are daily updated global maps on a regular grid of the available scatterometer wind observations and derived properties, and produced from our EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) operational near-real time (NRT) Level 2 swath-based wind products by linear interpolation. Currently available products are the ASCAT on Metop A/B stress equivalent wind vectors, accompanied by ECMWF NWP reference stress equivalent winds from the operational ECMWF NWP model. For each ASCAT scatterometer we provide products on 2 different resolutions, 0.25 and 0.125 degrees. In addition we provide wind stress vectors, and derivative fields (curl and divergence) for stress equivalent wind and wind stress, both for the observations and for the NWP reference winds. New NRT scatterometer products will be made available when additional scatterometer instruments become available, and NRT access to the data can be arranged. We hope OSCAT on the Indian ScatSat-1 satellite will be the the next NRT product to be added. In addition multi-year reprocessing datasets have been made available for ASCAT on Metop-A (1-Jan-2007 up to 31-Mar-2014) and Seawinds on QuikScat (19-Jul-1999 up to 21-Nov-2009). For ASCAT 0.25 and 0.125 degree resolution products are provided, and for QuikScat 0.50 and 0.25 degree resolution products are provided, These products are based on reprocessing the L2 scatterometer products with the latest processing software version, and include reference winds from the ECMWF ERA-Interim model. Additional reprocessing datasets will be added when reprocessed L2 datasets become available. This will hopefully include the ERS-1 and ERS-2 scatterometer datasets (1992-2001), which will extend the available date range back to 1992. These products are available for download through the CMEMS portal website: http://marine.copernicus.eu/

The Added Value of Collecting Information on Pain Experience When Predicting Time on Benefits for Injured Workers with Back Pain.

PubMed

Steenstra, Ivan A; Franche, Renée-Louise; Furlan, Andrea D; Amick, Ben; Hogg-Johnson, Sheilah

2016-06-01

Objectives Some injured workers with work-related, compensated back pain experience a troubling course in return to work. A prediction tool was developed in an earlier study, using administrative data only. This study explored the added value of worker reported data in identifying those workers with back pain at higher risk of being on benefits for a longer period of time. Methods This was a cohort study of workers with compensated back pain in 2005 in Ontario. Workplace Safety and Insurance Board (WSIB) data was used. As well, we examined the added value of patient-reported prognostic factors obtained from a prospective cohort study. Improvement of model fit was determined by comparing area under the curve (AUC) statistics. The outcome measure was time on benefits during a first workers' compensation claim for back pain. Follow-up was 2 years. Results Among 1442 workers with WSIB data still on full benefits at 4 weeks, 113 were also part of the prospective cohort study. Model fit of an established rule in the smaller dataset of 113 workers was comparable to the fit previously established in the larger dataset. Adding worker rating of pain at baseline improved the rule substantially (AUC = 0.80, 95 % CI 0.68, 0.91 compared to benefit status at 180 days, AUC = 0.88, 95 % CI 0.74, 1.00 compared to benefits status at 360 days). Conclusion Although data routinely collected by workers' compensation boards show some ability to predict prolonged time on benefits, adding information on experienced pain reported by the worker improves the predictive ability of the model from 'fairly good' to 'good'. In this study, a combination of prognostic factors, reported by multiple stakeholders, including the worker, could identify those at high risk of extended duration on disability benefits and in potentially in need of additional support at the individual level.

Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: surficial geology

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the area of surficial geology types in square meters compiled for every catchment of NHDPlus for the conterminous United States. The source data set is the "Digital data set describing surficial geology in the conterminous US" (Clawges and Price, 1999). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Big Data Transforms Discovery-Utilization Therapeutics Continuum

PubMed Central

Waldman, SA; Terzic, A

2015-01-01

Enabling omic technologies adopt a holistic view to produce unprecedented insights into the molecular underpinnings of health and disease, in part, by generating massive high-dimensional biological data. Leveraging these systems-level insights as an engine driving the healthcare evolution is maximized through integration with medical, demographic, and environmental datasets from individuals to populations. Big data analytics has accordingly emerged to add value to the technical aspects of storage, transfer, and analysis required for merging vast arrays of omic-, clinical- and eco-datasets. In turn, this new field at the interface of biology, medicine, and information science is systematically transforming modern therapeutics across discovery, development, regulation, and utilization. “…a man's discourse was like to a rich Persian carpet, the beautiful figures and patterns of which can be shown only by spreading and extending it out; when it is contracted and folded up, they are obscured and lost” Themistocles quoted by Plutarch AD 46 – AD 120 PMID:26888297

Educational Effectiveness in Chilean Secondary Education: Comparing Different "Value Added" Approaches to Evaluate Schools

ERIC Educational Resources Information Center

Muñoz-Chereau, B.; Thomas, S. M.

2016-01-01

This article reports an original investigation into school performance measures and the multilevel nature of pupil achievement data in the Chilean school system using a sample of 177,461 students, nested within 7146 classrooms, 2283 secondary schools and 313 municipalities. The data-set comprised Year 10 students' 2006 SIMCE test's results in two…

A new method to assess the added value of high-resolution regional climate simulations: application to the EURO-CORDEX dataset

NASA Astrophysics Data System (ADS)

Soares, P. M. M.; Cardoso, R. M.

2017-12-01

Regional climate models (RCM) are used with increasing resolutions pursuing to represent in an improved way regional to local scale atmospheric phenomena. The EURO-CORDEX simulations at 0.11° and simulations exploiting finer grid spacing approaching the convective-permitting regimes are representative examples. The climate runs are computationally very demanding and do not always show improvements. These depend on the region, variable and object of study. The gains or losses associated with the use of higher resolution in relation to the forcing model (global climate model or reanalysis), or to different resolution RCM simulations, is known as added value. Its characterization is a long-standing issue, and many different added-value measures have been proposed. In the current paper, a new method is proposed to assess the added value of finer resolution simulations, in comparison to its forcing data or coarser resolution counterparts. This approach builds on a probability density function (PDF) matching score, giving a normalised measure of the difference between diverse resolution PDFs, mediated by the observational ones. The distribution added value (DAV) is an objective added value measure that can be applied to any variable, region or temporal scale, from hindcast or historical (non-synchronous) simulations. The DAVs metric and an application to the EURO-CORDEX simulations, for daily temperatures and precipitation, are here presented. The EURO-CORDEX simulations at both resolutions (0.44o,0.11o) display a clear added value in relation to ERA-Interim, with values around 30% in summer and 20% in the intermediate seasons, for precipitation. When both RCM resolutions are directly compared the added value is limited. The regions with the larger precipitation DAVs are areas where convection is relevant, e.g. Alps and Iberia. When looking at the extreme precipitation PDF tail, the higher resolution improvement is generally greater than the low resolution for seasons and regions. For temperature, the added value is smaller. AcknowledgmentsThe authors wish to acknowledge SOLAR (PTDC/GEOMET/7078/2014) and FCT UID/GEO/50019/ 2013 (Instituto Dom Luiz) projects.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Mean Infiltration-Excess Overland Flow, 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This tabular data set represents the mean value for infiltration-excess overland flow as estimated by the watershed model TOPMODEL, compiled for every catchment of NHDPlus for the conterminous United States. Infiltration-excess overland flow, expressed as a percent of total overland flow, is simulated in TOPMODEL as precipitation that exceeds the infiltration capacity of the soil and enters the stream channel. The source data set is Infiltration-Excess Overland Flow Estimated by TOPMODEL for the Conterminous United States (Wolock, 2003). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots

NASA Astrophysics Data System (ADS)

Lal, Mohan; Mishra, S. K.; Pandey, Ashish; Pandey, R. P.; Meena, P. K.; Chaudhary, Anubhav; Jha, Ranjit Kumar; Shreevastava, Ajit Kumar; Kumar, Yogendra

2017-01-01

The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (P) and runoff (Q) datasets of United States watersheds, is evaluated using a large dataset of natural storm events from 27 agricultural plots in India. On the whole, the CN estimates from the National Engineering Handbook (chapter 4) tables do not match those derived from the observed P and Q datasets. As a result, the runoff prediction using former CNs was poor for the data of 22 (out of 24) plots. However, the match was little better for higher CN values, consistent with the general notion that the existing SCS-CN method performs better for high rainfall-runoff (high CN) events. Infiltration capacity (fc) was the main explanatory variable for runoff (or CN) production in study plots as it exhibited the expected inverse relationship between CN and fc. The plot-data optimization yielded initial abstraction coefficient (λ) values from 0 to 0.659 for the ordered dataset and 0 to 0.208 for the natural dataset (with 0 as the most frequent value). Mean and median λ values were, respectively, 0.030 and 0 for the natural rainfall-runoff dataset and 0.108 and 0 for the ordered rainfall-runoff dataset. Runoff estimation was very sensitive to λ and it improved consistently as λ changed from 0.2 to 0.03.

Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.

PubMed

Dimitriadis, S I; Liparas, Dimitris; Tsolaki, Magda N

2018-05-15

In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI. Based on preprocessed MRI images from the organizers of a neuroimaging challenge, 3 we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme. In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition. The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature. Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD. Copyright © 2017 Elsevier B.V. All rights reserved.

Default values for assessment of potential dermal exposure of the hands to industrial chemicals in the scope of regulatory risk assessments.

PubMed

Marquart, Hans; Warren, Nicholas D; Laitinen, Juha; van Hemmen, Joop J

2006-07-01

Dermal exposure needs to be addressed in regulatory risk assessment of chemicals. The models used so far are based on very limited data. The EU project RISKOFDERM has gathered a large number of new measurements on dermal exposure to industrial chemicals in various work situations, together with information on possible determinants of exposure. These data and information, together with some non-RISKOFDERM data were used to derive default values for potential dermal exposure of the hands for so-called 'TGD exposure scenarios'. TGD exposure scenarios have similar values for some very important determinant(s) of dermal exposure, such as amount of substance used. They form narrower bands within the so-called 'RISKOFDERM scenarios', which cluster exposure situations according to the same purpose of use of the products. The RISKOFDERM scenarios in turn are narrower bands within the so-called Dermal Exposure Operation units (DEO units) that were defined in the RISKOFDERM project to cluster situations with similar exposure processes and exposure routes. Default values for both reasonable worst case situations and typical situations were derived, both for single datasets and, where possible, for combined datasets that fit the same TGD exposure scenario. The following reasonable worst case potential hand exposures were derived from combined datasets: (i) loading and filling of large containers (or mixers) with large amounts (many litres) of liquids: 11,500 mg per scenario (14 mg cm(-2) per scenario with surface of the hands assumed to be 820 cm(2)); (ii) careful mixing of small quantities (tens of grams in <1l): 4.1 mg per scenario (0.005 mg cm(-2) per scenario); (iii) spreading of (viscous) liquids with a comb on a large surface area: 130 mg per scenario (0.16 mg cm(-2) per scenario); (iv) brushing and rolling of (relatively viscous) liquid products on surfaces: 6500 mg per scenario (8 mg cm(-2) per scenario) and (v) spraying large amounts of liquids (paints, cleaning products) on large areas: 12,000 mg per scenario (14 mg cm(-2) per scenario). These default values are considered useful for estimating exposure for similar substances in similar situations with low uncertainty. Several other default values based on single datasets can also be used, but lead to estimates with a higher uncertainty, due to their more limited basis. Sufficient analogy in all described parameters of the scenario, including duration, is needed to enable proper use of the default values. The default values lead to similar estimates as the RISKOFDERM dermal exposure model that was based on the same datasets, but uses very different parameters. Both approaches are preferred over older general models, such as EASE, that are not based on data from actual dermal exposure situations.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Physiographic Provinces

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This dataset represents the area of each physiographic province (Fenneman and Johnson, 1946) in square meters, compiled for every catchment of NHDPlus for the conterminous United States. The source data are from Fenneman and Johnson's Physiographic Provinces of the United States, which is based on 8 major divisions, 25 provinces, and 86 sections representing distinctive areas having common topography, rock type and structure, and geologic and geomorphic history (Fenneman and Johnson, 1946). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: 30-year average annual maximum temperature, 1971-2000

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the 30-year (1971-2000) average annual maximum temperature in Celsius multiplied by 100 compiled for every catchment of NHDPlus for the conterminous United States. The source data were the United States Average Monthly or Annual Minimum Temperature, 1971 - 2000 raster dataset produced by the PRISM Group at Oregon State University. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Average Annual Daily Minimum Temperature, 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the average monthly minimum temperature in Celsius multiplied by 100 for 2002 compiled for every catchment of NHDPlus for the conterminous United States. The source data were the Near-Real-Time High-Resolution Monthly Average Maximum/Minimum Temperature for the Conterminous United States for 2002 raster dataset produced by the Spatial Climate Analysis Service at Oregon State University. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: 30-Year Average Annual Precipitation, 1971-2000

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the 30-year (1971-2000) average annual precipitation in millimeters multiplied by 100 compiled for every catchment of NHDPlus for the conterminous United States. The source data were the "United States Average Monthly or Annual Precipitation, 1971 - 2000" raster dataset produced by the PRISM Group at Oregon State University. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: 30-Year Average Annual Minimum Temperature, 1971-2000

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the 30-year (1971-2000) average annual minimum temperature in Celsius multiplied by 100 compiled for every catchment of NHDPlus for the conterminous United States. The source data were the "United States Average Monthly or Annual Minimum Temperature, 1971 - 2000" raster dataset produced by the PRISM Group at Oregon State University. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: Average Annual Daily Maximum Temperature, 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the average monthly maximum temperature in Celsius multiplied by 100 for 2002 compiled for every catchment of NHDPlus for the conterminous United States. The source data were the Near-Real-Time High-Resolution Monthly Average Maximum/Minimum Temperature for the Conterminous United States for 2002 raster dataset produced by the Spatial Climate Analysis Service at Oregon State University. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Average Monthly Precipitation, 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the average monthly precipitation in millimeters multiplied by 100 for 2002 compiled for every catchment of NHDPlus for the conterminous United States. The source data were the Near-Real-Time Monthly High-Resolution Precipitation Climate Data Set for the Conterminous United States (2002) raster dataset produced by the Spatial Climate Analysis Service at Oregon State University. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

How robust is the pre-1931 National Climatic Data Center—climate divisional dataset? Examples from Georgia and Louisiana

NASA Astrophysics Data System (ADS)

Allard, Jason; Thompson, Clint; Keim, Barry D.

2015-04-01

The National Climatic Data Center's climate divisional dataset (CDD) is commonly used in climate change analyses. This dataset is a spatially continuous dataset for the conterminous USA from 1895 to the present. The CDD since 1931 is computed by averaging all available representative cooperative weather station data into a single monthly value for each of the 344 climate divisions of the conterminous USA, while pre-1931 data for climate divisions are derived from statewide averages using regression equations. This study examines the veracity of these pre-1931 data. All available Cooperative Observer Program (COOP) stations within each climate division in Georgia and Louisiana were averaged into a single monthly value for each month and each climate division from 1897 to 1930 to generate a divisional dataset (COOP DD), using similar methods to those used by the National Climatic Data Center to generate the post-1931 CDD. The reliability of the official CDD—derived from statewide averages—to produce temperature and precipitation means and trends prior to 1931 are then evaluated by comparing that dataset with the COOP DD with difference-of-means tests, correlations, and linear regression techniques. The CDD and the COOP DD are also compared to a divisional dataset derived from the United States Historical Climatology Network (USHCN) data (USHCN DD), with difference of means and correlation techniques, to demonstrate potential impacts of inhomogeneities within the CDD and the COOP DD. The statistical results, taken as a whole, not only indicate broad similarities between the CDD and COOP DD but also show that the CDD does not adequately portray pre-1931 temperature and precipitation in certain climate divisions within Georgia and Louisiana. In comparison with the USHCN DD, both the CDD and the COOP DD appear to be subject to biases that probably result from changing stations within climate divisions. As such, the CDD should be used judiciously for long-term studies of climate change, and past studies using the CDD should be evaluated in the context of these new findings.

How to derive biological information from the value of the normalization constant in allometric equations.

PubMed

Kaitaniemi, Pekka

2008-04-09

Allometric equations are widely used in many branches of biological science. The potential information content of the normalization constant b in allometric equations of the form Y = bX(a) has, however, remained largely neglected. To demonstrate the potential for utilizing this information, I generated a large number of artificial datasets that resembled those that are frequently encountered in biological studies, i.e., relatively small samples including measurement error or uncontrolled variation. The value of X was allowed to vary randomly within the limits describing different data ranges, and a was set to a fixed theoretical value. The constant b was set to a range of values describing the effect of a continuous environmental variable. In addition, a normally distributed random error was added to the values of both X and Y. Two different approaches were then used to model the data. The traditional approach estimated both a and b using a regression model, whereas an alternative approach set the exponent a at its theoretical value and only estimated the value of b. Both approaches produced virtually the same model fit with less than 0.3% difference in the coefficient of determination. Only the alternative approach was able to precisely reproduce the effect of the environmental variable, which was largely lost among noise variation when using the traditional approach. The results show how the value of b can be used as a source of valuable biological information if an appropriate regression model is selected.

The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean

NASA Astrophysics Data System (ADS)

Zittis, G.; Bruggeman, A.; Camera, C.; Hadjinicolaou, P.; Lelieveld, J.

2017-07-01

Climate change is expected to substantially influence precipitation amounts and distribution. To improve simulations of extreme rainfall events, we analyzed the performance of different convection and microphysics parameterizations of the WRF (Weather Research and Forecasting) model at very high horizontal resolutions (12, 4 and 1 km). Our study focused on the eastern Mediterranean climate change hot-spot. Five extreme rainfall events over Cyprus were identified from observations and were dynamically downscaled from the ERA-Interim (EI) dataset with WRF. We applied an objective ranking scheme, using a 1-km gridded observational dataset over Cyprus and six different performance metrics, to investigate the skill of the WRF configurations. We evaluated the rainfall timing and amounts for the different resolutions, and discussed the observational uncertainty over the particular extreme events by comparing three gridded precipitation datasets (E-OBS, APHRODITE and CHIRPS). Simulations with WRF capture rainfall over the eastern Mediterranean reasonably well for three of the five selected extreme events. For these three cases, the WRF simulations improved the ERA-Interim data, which strongly underestimate the rainfall extremes over Cyprus. The best model performance is obtained for the January 1989 event, simulated with an average bias of 4% and a modified Nash-Sutcliff of 0.72 for the 5-member ensemble of the 1-km simulations. We found overall added value for the convection-permitting simulations, especially over regions of high-elevation. Interestingly, for some cases the intermediate 4-km nest was found to outperform the 1-km simulations for low-elevation coastal parts of Cyprus. Finally, we identified significant and inconsistent discrepancies between the three, state of the art, gridded precipitation datasets for the tested events, highlighting the observational uncertainty in the region.

Clinical Value of Dual-energy CT in Detection of Pancreatic Adenocarcinoma: Investigation of the Best Pancreatic Tumor Contrast to Noise Ratio.

PubMed

He, Yong-Lan; Zhang, Da-Ming; Xue, Hua-Dan; Jin, Zheng-Yu

2013-01-01

Objective To quantitatively compare and determine the best pancreatic tumor contrast to noise ratio (CNR) in different dual-energy derived datasets. Methods In this retrospective, single center study, 16 patients (9 male, 7 female, average age 59.4±13.2 years) with pathologically diagnosed pancreatic cancer were enrolled. All patients received an abdominal scan using a dual source CT scanner 7 to 31 days before biopsy or surgery. After injection of iodine contrast agent, arterial and pancreatic parenchyma phase were scanned consequently, using a dual-energy scan mode (100 kVp/230 mAs and Sn 140 kVp/178 mAs) in the pancreatic parenchyma phase. A series of derived dual-energy datasets were evaluated including non-liner blending (non-linear blending width 0-500 HU; blending center -500 to 500 HU), mono-energetic (40-190 keV), 100 kVp and 140 kVp. On each datasets, mean CT values of the pancreatic parenchyma and tumor, as well as standard deviation CT values of subcutaneous fat and psoas muscle were measured. Regions of interest of cutaneous fat and major psoas muscle of 100 kVp and 140 kVp images were calculated. Best CNR of subcutaneous fat (CNRF) and CNR of the major psoas muscle (CNRM) of non-liner blending and mono-energetic datasets were calculated with the optimal mono-energetic keV setting and the optimal blending center/width setting for the best CNR. One Way ANOVA test was used for comparison of best CNR between different dual-energy derived datasets. Results The best CNRF (4.48±1.29) was obtained from the non-liner blending datasets at blending center -16.6±103.9 HU and blending width 12.3±10.6 HU. The best CNRF (3.28±0.97) was obtained from the mono-energetic datasets at 73.3±4.3 keV. CNRF in the 100 kVp and 140 kVp were 3.02±0.91 and 1.56±0.56 respectively. Using fat as the noise background, all of these images series showed significant differences (P<0.01) except best CNRF of mono-energetic image sets vs. CNRF of 100 kVp image (P=0.460). Similar results were found using muscle as the noise background (mono-energetic image vs. 100 kVp image: P=0.246; mono-energetic image vs. non-liner blending image: P=0.044; others: P<0.01). Conclusion Compared with mono-energetic datasets and low kVp datasets, non-linear blending image at automatically chosen blending width/window provides better tumor to the pancreas CNR, which might be beneficial for better detection of pancreatic tumors.

Spatio-temporal Root Zone Soil Moisture Estimation for Indo - Gangetic Basin from Satellite Derived (AMSR-2 and SMOS) Surface Soil Moisture

NASA Astrophysics Data System (ADS)

Sure, A.; Dikshit, O.

2017-12-01

Root zone soil moisture (RZSM) is an important element in hydrology and agriculture. The estimation of RZSM provides insight in selecting the appropriate crops for specific soil conditions (soil type, bulk density, etc.). RZSM governs various vadose zone phenomena and subsequently affects the groundwater processes. With various satellite sensors dedicated to estimating surface soil moisture at different spatial and temporal resolutions, estimation of soil moisture at root zone level for Indo - Gangetic basin which inherits complex heterogeneous environment, is quite challenging. This study aims at estimating RZSM and understand its variation at the level of Indo - Gangetic basin with changing land use/land cover, topography, crop cycles, soil properties, temperature and precipitation patterns using two satellite derived soil moisture datasets operating at distinct frequencies with different principles of acquisition. Two surface soil moisture datasets are derived from AMSR-2 (6.9 GHz - `C' Band) and SMOS (1.4 GHz - `L' band) passive microwave sensors with coarse spatial resolution. The Soil Water Index (SWI), accounting for soil moisture from the surface, is derived by considering a theoretical two-layered water balance model and contributes in ascertaining soil moisture at the vadose zone. This index is evaluated against the widely used modelled soil moisture dataset of GLDAS - NOAH, version 2.1. This research enhances the domain of utilising the modelled soil moisture dataset, wherever the ground dataset is unavailable. The coupling between the surface soil moisture and RZSM is analysed for two years (2015-16), by defining a parameter T, the characteristic time length. The study demonstrates that deriving an optimal value of T for estimating SWI at a certain location is a function of various factors such as land, meteorological, and agricultural characteristics.

Evaluation of the Precision of Satellite-Derived Sea Surface Temperature Fields

NASA Astrophysics Data System (ADS)

Wu, F.; Cornillon, P. C.; Guan, L.

2016-02-01

A great deal of attention has been focused on the temporal accuracy of satellite-derived sea surface temperature (SST) fields with little attention being given to their spatial precision. Specifically, the primary measure of the quality of SST fields has been the bias and variance of selected values minus co-located (in space and time) in situ values. Contributing values, determined by the location of the in situ values and the necessity that the satellite-derived values be cloud free, are generally widely separated in space and time hence provide little information related to the pixel-to-pixel uncertainty in the retrievals. But the main contribution to the uncertainty in satellite-derived SST retrievals relates to atmospheric contamination and because the spatial scales of atmospheric features are, in general, large compared with the pixel separation of modern infra-red sensors, the pixel-to-pixel uncertainty is often smaller than the accuracy determined from in situ match-ups. This makes selection of satellite-derived datasets for the study of submesoscale processes, for which the spatial structure of the upper ocean is significant, problematic. In this presentation we present a methodology to characterize the spatial precision of satellite-derived SST fields. The method is based on an examination of the high wavenumber tail of the 2-D spectrum of SST fields in the Sargasso Sea, a low energy region of the ocean close to the track of the MV Oleander, a container ship making weekly roundtrips between New York and Bermuda, with engine intake temperatures sampled every 75 m along track. Important spectral characteristics are the point at which the satellite-derived spectra separate from the Oleander spectra and the spectral slope following separation. In this presentation a number of high resolution 375 m to 10 km SST datasets are evaluated based on this approach.

Robustness of Value-Added Analysis of School Effectiveness. Research Report. ETS RR-08-22

ERIC Educational Resources Information Center

Braun, Henry; Qu, Yanxuan

2008-01-01

This paper reports on a study conducted to investigate the consistency of the results between 2 approaches to estimating school effectiveness through value-added modeling. Estimates of school effects from the layered model employing item response theory (IRT) scaled data are compared to estimates derived from a discrete growth model based on the…

The Disaggregation of Value-Added Test Scores to Assess Learning Outcomes in Economics Courses

ERIC Educational Resources Information Center

Walstad, William B.; Wagner, Jamie

2016-01-01

This study disaggregates posttest, pretest, and value-added or difference scores in economics into four types of economic learning: positive, retained, negative, and zero. The types are derived from patterns of student responses to individual items on a multiple-choice test. The micro and macro data from the "Test of Understanding in College…

Advancing Alzheimer's research: A review of big data promises.

PubMed

Zhang, Rui; Simon, Gyorgy; Yu, Fang

2017-10-01

To review the current state of science using big data to advance Alzheimer's disease (AD) research and practice. In particular, we analyzed the types of research foci addressed, corresponding methods employed and study findings reported using big data in AD. Systematic review was conducted for articles published in PubMed from January 1, 2010 through December 31, 2015. Keywords with AD and big data analytics were used for literature retrieval. Articles were reviewed and included if they met the eligibility criteria. Thirty-eight articles were included in this review. They can be categorized into seven research foci: diagnosing AD or mild cognitive impairment (MCI) (n=10), predicting MCI to AD conversion (n=13), stratifying risks for AD (n=5), mining the literature for knowledge discovery (n=4), predicting AD progression (n=2), describing clinical care for persons with AD (n=3), and understanding the relationship between cognition and AD (n=3). The most commonly used datasets are AD Neuroimaging Initiative (ADNI) (n=16), electronic health records (EHR) (n=11), MEDLINE (n=3), and other research datasets (n=8). Logistic regression (n=9) and support vector machine (n=8) are the most used methods for data analysis. Big data are increasingly used to address AD-related research questions. While existing research datasets are frequently used, other datasets such as EHR data provide a unique, yet under-utilized opportunity for advancing AD research. Copyright © 2017 Elsevier B.V. All rights reserved.

A new marine sediment certified reference material (CRM) for the determination of persistent organic contaminants: IAEA-459.

PubMed

Tolosa, Imma; Cassi, Roberto; Huertas, David

2018-04-11

A new marine sediment certified reference material (IAEA 459) with very low concentrations (μg kg -1 ) for a variety of persistent organic contaminants (POPs) listed by the Stockholm Convention, as well as other POPs and priority substances (PSs) listed in many environmental monitoring programs was developed by the IAEA. The sediment material was collected from the Ham River estuary in South Korea, and the assigned final values were derived from robust statistics on the results provided by selected laboratories which demonstrated technical and quality competence, following the guidance given in ISO Guide 35. The robust mean of the laboratory means was assigned as certified values, for those compounds where the assigned value was derived from at least five datasets and its relative expanded uncertainty was less than 40% of the assigned value (most of the values ranging from 8 to 20%). All the datasets were derived from at least two different analytical techniques which have allowed the assignment of certified concentrations for 22 polychlorinated biphenyl (PCB) congeners, 6 organochlorinated (OC) pesticides, 5 polybrominated diphenyl ethers (PBDEs), and 18 polycyclic aromatic hydrocarbon (PAHs). Mass fractions of compounds that did not fulfill the criteria of certification are considered information values, which include 29 PAHs, 11 PCBs, 16 OC pesticides, and 5 PBDEs. The extensive characterization and associated uncertainties at concentration levels close to the marine sediment quality guidelines will make CRM 459 a valuable matrix reference material for use in marine environmental monitoring programs.

Observed Characteristics and Teacher Quality: Impacts of Sample Selection on a Value Added Model

ERIC Educational Resources Information Center

Winters, Marcus A.; Dixon, Bruce L.; Greene, Jay P.

2012-01-01

We measure the impact of observed teacher characteristics on student math and reading proficiency using a rich dataset from Florida. We expand upon prior work by accounting directly for nonrandom attrition of teachers from the classroom in a sample selection framework. We find evidence that sample selection is present in the estimation of the…

Evaluation of non-negative matrix factorization of grey matter in age prediction.

PubMed

Varikuti, Deepthi P; Genon, Sarah; Sotiras, Aristeidis; Schwender, Holger; Hoffstaedter, Felix; Patil, Kaustubh R; Jockwitz, Christiane; Caspers, Svenja; Moebus, Susanne; Amunts, Katrin; Davatzikos, Christos; Eickhoff, Simon B

2018-06-01

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging. Copyright © 2018 Elsevier Inc. All rights reserved.

Astronaut Photography of the Earth: A Long-Term Dataset for Earth Systems Research, Applications, and Education

NASA Technical Reports Server (NTRS)

Stefanov, William L.

2017-01-01

The NASA Earth observations dataset obtained by humans in orbit using handheld film and digital cameras is freely accessible to the global community through the online searchable database at https://eol.jsc.nasa.gov, and offers a useful compliment to traditional ground-commanded sensor data. The dataset includes imagery from the NASA Mercury (1961) through present-day International Space Station (ISS) programs, and currently totals over 2.6 million individual frames. Geographic coverage of the dataset includes land and oceans areas between approximately 52 degrees North and South latitudes, but is spatially and temporally discontinuous. The photographic dataset includes some significant impediments for immediate research, applied, and educational use: commercial RGB films and camera systems with overlapping bandpasses; use of different focal length lenses, unconstrained look angles, and variable spacecraft altitudes; and no native geolocation information. Such factors led to this dataset being underutilized by the community but recent advances in automated and semi-automated image geolocation, image feature classification, and web-based services are adding new value to the astronaut-acquired imagery. A coupled ground software and on-orbit hardware system for the ISS is in development for planned deployment in mid-2017; this system will capture camera pose information for each astronaut photograph to allow automated, full georegistration of the data. The ground system component of the system is currently in use to fully georeference imagery collected in response to International Disaster Charter activations, and the auto-registration procedures are being applied to the extensive historical database of imagery to add value for research and educational purposes. In parallel, machine learning techniques are being applied to automate feature identification and classification throughout the dataset, in order to build descriptive metadata that will improve search capabilities. It is expected that these value additions will increase interest and use of the dataset by the global community.

Fine-tuning satellite-based rainfall estimates

NASA Astrophysics Data System (ADS)

Harsa, Hastuadi; Buono, Agus; Hidayat, Rahmat; Achyar, Jaumil; Noviati, Sri; Kurniawan, Roni; Praja, Alfan S.

2018-05-01

Rainfall datasets are available from various sources, including satellite estimates and ground observation. The locations of ground observation scatter sparsely. Therefore, the use of satellite estimates is advantageous, because satellite estimates can provide data on places where the ground observations do not present. However, in general, the satellite estimates data contain bias, since they are product of algorithms that transform the sensors response into rainfall values. Another cause may come from the number of ground observations used by the algorithms as the reference in determining the rainfall values. This paper describe the application of bias correction method to modify the satellite-based dataset by adding a number of ground observation locations that have not been used before by the algorithm. The bias correction was performed by utilizing Quantile Mapping procedure between ground observation data and satellite estimates data. Since Quantile Mapping required mean and standard deviation of both the reference and the being-corrected data, thus the Inverse Distance Weighting scheme was applied beforehand to the mean and standard deviation of the observation data in order to provide a spatial composition of them, which were originally scattered. Therefore, it was possible to provide a reference data point at the same location with that of the satellite estimates. The results show that the new dataset have statistically better representation of the rainfall values recorded by the ground observation than the previous dataset.

Coseismic stresses indicated by pseudotachylytes in the Outer Hebrides Fault Zone, UK.

NASA Astrophysics Data System (ADS)

Campbell, Lucy; Lloyd, Geoffrey; Phillips, Richard; Holdsworth, Robert; Walcott, Rachel

2015-04-01

During the few seconds of earthquake slip, dynamic behaviour is predicted for stress, slip velocity, friction and temperature, amongst other properties. Fault-derived pseudotachylyte is a coseismic frictional melt and provides a unique snapshot of the rupture environment. Exhumation of ancient fault zones to seismogenic depths can reveal the structure and distribution of seismic slip as pseudotachylyte bearing fault planes. An example lies in NW Scotland along the Outer Hebrides Fault Zone (OHFZ) - this long-lived fault zone displays a suite of fault rocks developed under evolving kinematic regimes, including widespread pseudotachylyte veining which is distributed both on and away from the major faults. This study adds data derived from the OHFZ pseudotachylytes to published datasets from well-constrained fault zones, in order to explore the use of existing methodologies on more complex faults and to compare the calculated results. Temperature, stress and pressure are calculated from individual fault veins and added to existing datasets. The results pose questions on the physical meaning of the derived trends, the distribution of seismic energy release across scattered cm-scale faults and the range of earthquake magnitudes calculated from faults across any given fault zone.

Soil Physicochemical Characteristics from Ice Wedge Polygons, Barrow, Alaska, Ver. 1

DOE Data Explorer

Chowdhury, Taniya; Graham, David

2013-12-08

This dataset provides details about soil cores (active layer and permafrost) collected from ice-wedge polygons during field expeditions to Barrow Environmental Observatory, Alaska in April, 2012 and 2013. Core information available are exact core locations; soil horizon descriptions and characteristics; and fundamental soil physico-chemical properties. In February 2016, two columns (carbon and carbon:nitrogen in soil layer) were added to the data but no existing data values changed. See documentation. The new filename is version 2. In July 2016, data for two soil cores were added. The new filename is version 3.

Structural Covariance of the Default Network in Healthy and Pathological Aging

PubMed Central

Turner, Gary R.

2013-01-01

Significant progress has been made uncovering functional brain networks, yet little is known about the corresponding structural covariance networks. The default network's functional architecture has been shown to change over the course of healthy and pathological aging. We examined cross-sectional and longitudinal datasets to reveal the structural covariance of the human default network across the adult lifespan and through the progression of Alzheimer's disease (AD). We used a novel approach to identify the structural covariance of the default network and derive individual participant scores that reflect the covariance pattern in each brain image. A seed-based multivariate analysis was conducted on structural images in the cross-sectional OASIS (N = 414) and longitudinal Alzheimer's Disease Neuroimaging Initiative (N = 434) datasets. We reproduced the distributed topology of the default network, based on a posterior cingulate cortex seed, consistent with prior reports of this intrinsic connectivity network. Structural covariance of the default network scores declined in healthy and pathological aging. Decline was greatest in the AD cohort and in those who progressed from mild cognitive impairment to AD. Structural covariance of the default network scores were positively associated with general cognitive status, reduced in APOEε4 carriers versus noncarriers, and associated with CSF biomarkers of AD. These findings identify the structural covariance of the default network and characterize changes to the network's gray matter integrity across the lifespan and through the progression of AD. The findings provide evidence for the large-scale network model of neurodegenerative disease, in which neurodegeneration spreads through intrinsically connected brain networks in a disease specific manner. PMID:24048852

The Effect of Working Condition on Math Teacher Effectiveness: Value-Added Scores and Student Satisfaction in Teaching

ERIC Educational Resources Information Center

Ye, Yincheng; Singh, Kusum

2017-01-01

The purpose of this study is to better understand how math teachers' effectiveness as measured by value-added scores and student satisfaction with teaching is influenced by school's working conditions. The data for the study were derived from 2009 to 2010 Teacher Working Condition Survey and Student Perception Survey in Measures of Effective…

SIMOcean: Maritime Open Data and Services Platform for Portuguese Institutions

NASA Astrophysics Data System (ADS)

Almeida, Nuno; Grosso, Nuno; Catarino, Nuno; Gutierrez, Antonio; Lamas, Luísa; Alves, Margarida; Almeida, Sara; Deus, Ricardo; Oliveira, Paulo

2016-04-01

Portugal is the country with the largest EEZ in the EU and the 10th largest EEZ in the world, at 3,877,408 km2, rendering the existence of an integrated management of Portuguese marine system crucial to monitor a wide range of interdependent domains. A system like this assimilates data and information from different thematic areas, ranging from ocean and atmosphere state variables to higher level datasets describing human activities and related environmental, social and economic impacts. Currently, these datasets are collected by a wide number of public and private institutions with very diverse purposes (e.g., monitoring, research, recreation, vigilance) leading to dataset duplication, inexistence of common data and metadata standards across organizations, and the propagation of closed information systems with different implementation solutions. This lack of coordination and visibility hinders the marine management, monitoring and vigilance capabilities, not only by making it more difficult to access, or even be aware of, the existence of certain datasets, but also by minimizing the ability to create added value products or services through dataset integration from different sources. Adopting Open Data approach will bring significant benefits by reducing the cost of information exchange and data integration, promoting the extensive use of this data. SIMOcean (System for Integrated Monitoring of the Ocean), co-funded by the EEA Grants Programme, is integrated in the initiative of the Portuguese Government to develop a set of coordinated systems providing access to national marine data. These systems aim to improve the Portuguese marine management, monitoring and vigilance capabilities, aggregating different data, including specific human activities datasets (vessel traffic, fishing records, oil spills), and environment variables (waves, currents, wind). Those datasets, currently scattered among different departments of the Portuguese Meteorological (IPMA) and the Navy's Hydrographic (IH) Institutes, will be brought together in the SIMOcean Open Data system. The SIMOcean system will also exploit this data in the following three flagship value added services: 1) Characterisation of Fishing Areas; 2) Wave Alerts for Sea Ports; and 3) Support to Search and Rescue Missions. These services will be driven by end users such as Civil Protection Authorities, Port Authorities and Fishing Associations, where these new products will lead to a significant positive impact in their operations. SIMOcean will be based on open source web based GIS interoperable solutions, compliant with OGC and INSPIRE directive standards to support the evolution of a set of open interfaces and protocols in the development of a common European spatial data infrastructure. The Catalogue solution (based on ckan) will consider the Portuguese Metadata Profile for the Sea developed by SNIM@R project, the guidelines provided by the directive 2013/37/EU and the Goldbook provided by the European Data portal. The system will be based on SenSyF approach of a scalable Cloud Computing system, providing authorised entities a single access point system for data catalogue, visualisation, processing and value added service deployment. It will be used by the two of the main Portuguese sea data providers with operational responsibilities in marine management, monitoring and vigilance.

Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products

PubMed Central

Guo, Xiaoyi; Zhang, Hongyan; Wu, Zhengfang; Zhao, Jianjun; Zhang, Zhengxiang

2017-01-01

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics. PMID:28587266

Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products.

PubMed

Guo, Xiaoyi; Zhang, Hongyan; Wu, Zhengfang; Zhao, Jianjun; Zhang, Zhengxiang

2017-06-06

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics.

The Effects of Fraternity and Sorority Membership in the Fourth Year of College: A Detrimental or Value-Added Component of Undergraduate Education?

ERIC Educational Resources Information Center

Hevel, Michael S.; Martin, Georgianna L.; Weeden, Dustin D.; Pascarella, Ernest T.

2015-01-01

We use a longitudinal national dataset to explore the direct and conditional effects of fraternity/sorority membership on students' educational outcomes in the 4th year of college. Controlling for a variety of potentially confounding variables, including pretest measures of the outcomes, we find no direct effect of fraternity/sorority membership…

Biomimetic bio-inspired biomorph sustainable? An attempt to classify and clarify biology-derived technical developments.

PubMed

Speck, Olga; Speck, David; Horn, Rafael; Gantner, Johannes; Sedlbauer, Klaus Peter

2017-01-24

Over the last few decades, the systematic approach of knowledge transfer from biological concept generators to technical applications has received increasing attention, particularly because marketable bio-derived developments are often described as sustainable. The objective of this paper is to rationalize and refine the discussion about bio-derived developments also with respect to sustainability by taking descriptive, normative and emotional aspects into consideration. In the framework of supervised learning, a dataset of 70 biology-derived and technology-derived developments characterised by 9 different attributes together with their respective values and assigned to one of 17 classes was created. On the basis of the dataset a decision tree was generated which can be used as a straightforward classification tool to identify biology-derived and technology-derived developments. The validation of the applied learning procedure achieved an average accuracy of 90.0%. Additional extraordinary qualities of technical applications are generally discussed by means of selected biology-derived and technology-derived examples with reference to normative (contribution to sustainability) and emotional aspects (aesthetics and symbolic character). In the context of a case study from the building sector, all aspects are critically discussed.

Estimation of the linear mixed integrated Ornstein–Uhlenbeck model

PubMed Central

Hughes, Rachael A.; Kenward, Michael G.; Sterne, Jonathan A. C.; Tilling, Kate

2017-01-01

ABSTRACT The linear mixed model with an added integrated Ornstein–Uhlenbeck (IOU) process (linear mixed IOU model) allows for serial correlation and estimation of the degree of derivative tracking. It is rarely used, partly due to the lack of available software. We implemented the linear mixed IOU model in Stata and using simulations we assessed the feasibility of fitting the model by restricted maximum likelihood when applied to balanced and unbalanced data. We compared different (1) optimization algorithms, (2) parameterizations of the IOU process, (3) data structures and (4) random-effects structures. Fitting the model was practical and feasible when applied to large and moderately sized balanced datasets (20,000 and 500 observations), and large unbalanced datasets with (non-informative) dropout and intermittent missingness. Analysis of a real dataset showed that the linear mixed IOU model was a better fit to the data than the standard linear mixed model (i.e. independent within-subject errors with constant variance). PMID:28515536

Dose-Fractionation Sensitivity of Prostate Cancer Deduced From Radiotherapy Outcomes of 5,969 Patients in Seven International Institutional Datasets: {alpha}/{beta} = 1.4 (0.9-2.2) Gy

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

Miralbell, Raymond, E-mail: Raymond.Miralbell@hcuge.ch; Institut Oncologic Teknon, Barcelona; Roberts, Stephen A.

2012-01-01

Purpose: There are reports of a high sensitivity of prostate cancer to radiotherapy dose fractionation, and this has prompted several trials of hypofractionation schedules. It remains unclear whether hypofractionation will provide a significant therapeutic benefit in the treatment of prostate cancer, and whether there are different fractionation sensitivities for different stages of disease. In order to address this, multiple primary datasets have been collected for analysis. Methods and Materials: Seven datasets were assembled from institutions worldwide. A total of 5969 patients were treated using external beams with or without androgen deprivation (AD). Standard fractionation (1.8-2.0 Gy per fraction) was usedmore » for 40% of the patients, and hypofractionation (2.5-6.7 Gy per fraction) for the remainder. The overall treatment time ranged from 1 to 8 weeks. Low-risk patients comprised 23% of the total, intermediate-risk 44%, and high-risk 33%. Direct analysis of the primary data for tumor control at 5 years was undertaken, using the Phoenix criterion of biochemical relapse-free survival, in order to calculate values in the linear-quadratic equation of k (natural log of the effective target cell number), {alpha} (dose-response slope using very low doses per fraction), and the ratio {alpha}/{beta} that characterizes dose-fractionation sensitivity. Results: There was no significant difference between the {alpha}/{beta} value for the three risk groups, and the value of {alpha}/{beta} for the pooled data was 1.4 (95% CI = 0.9-2.2) Gy. Androgen deprivation improved the bNED outcome index by about 5% for all risk groups, but did not affect the {alpha}/{beta} value. Conclusions: The overall {alpha}/{beta} value was consistently low, unaffected by AD deprivation, and lower than the appropriate values for late normal-tissue morbidity. Hence the fractionation sensitivity differential (tumor/normal tissue) favors the use of hypofractionated radiotherapy schedules for all risk groups, which is also very beneficial logistically in limited-resource settings.« less

Estimation of Satellite-Based SO42- and NH4+ Composition of Ambient Fine Particulate Matter Over China Using Chemical Transport Model

NASA Astrophysics Data System (ADS)

Si, Y.; Li, S.; Chen, L.; Yu, C.; Zhu, W.

2018-04-01

Epidemiologic and health impact studies have examined the chemical composition of ambient PM2.5 in China but have been constrained by the paucity of long-term ground measurements. Using the GEOS-Chem chemical transport model and satellite-derived PM2.5 data, sulfate and ammonium levels were estimated over China from 2004 to 2014. A comparison of the satellite-estimated dataset with model simulations based on ground measurements obtained from the literature indicated our results are more accurate. Using satellite-derived PM2.5 data with a spatial resolution of 0.1° × 0.1°, we further presented finer satellite-estimated sulfate and ammonium concentrations in anthropogenic polluted regions, including the NCP (the North China Plain), the SCB (the Sichuan Basin) and the PRD (the Pearl River Delta). Linear regression results obtained on a national scale yielded an r value of 0.62, NMB of -35.9 %, NME of 48.2 %, ARB_50 % of 53.68 % for sulfate and an r value of 0.63, slope of 0.67, and intercept of 5.14 for ammonium. In typical regions, the satellite-derived dataset was significantly robust. Based on the satellite-derived dataset, the spatial-temporal variation of 11-year annual average satellite-derived SO42- and NH4+ concentrations and time series of monthly average concentrations were also investigated. On a national scale, both exhibited a downward trend each year between 2004 and 2014 (SO42-: -0.61 %; NH4+: -0.21 %), large values were mainly concentrated in the NCP and SCB. For regions captured at a finer resolution, the inter-annual variation trends presented a positive trend over the periods 2004-2007 and 2008-2011, followed by a negative trend over the period 2012-2014, and sulfate concentrations varied appreciably. Moreover, the seasonal distributions of the 11-year satellite-derived dataset over China were presented. The distribution of both sulfate and ammonium concentrations exhibited seasonal characteristics, with the seasonal concentrations ranking as follows: winter > summer > autumn > spring. High concentrations of these species were concentrated in the NCP and SCB, originating from coal-fired power plants and agricultural activities, respectively. Efforts to reduce sulfur dioxide (SO2) emissions have yielded remarkable results since the government has adopted stricter control measures in recent years. Moreover, ammonia emissions should be controlled while reducing the concentration of sulfur, nitrogen and particulate matter. This study provides an assessment of the population's exposure to certain chemical components.

Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer's Disease Stigma on Twitter.

PubMed

Oscar, Nels; Fox, Pamela A; Croucher, Racheal; Wernick, Riana; Keune, Jessica; Hooker, Karen

2017-09-01

Social scientists need practical methods for harnessing large, publicly available datasets that inform the social context of aging. We describe our development of a semi-automated text coding method and use a content analysis of Alzheimer's disease (AD) and dementia portrayal on Twitter to demonstrate its use. The approach improves feasibility of examining large publicly available datasets. Machine learning techniques modeled stigmatization expressed in 31,150 AD-related tweets collected via Twitter's search API based on 9 AD-related keywords. Two researchers manually coded 311 random tweets on 6 dimensions. This input from 1% of the dataset was used to train a classifier against the tweet text and code the remaining 99% of the dataset. Our automated process identified that 21.13% of the AD-related tweets used AD-related keywords to perpetuate public stigma, which could impact stereotypes and negative expectations for individuals with the disease and increase "excess disability". This technique could be applied to questions in social gerontology related to how social media outlets reflect and shape attitudes bearing on other developmental outcomes. Recommendations for the collection and analysis of large Twitter datasets are discussed. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multi-crosswell profile 3D imaging and method

DOEpatents

Washbourne, John K.; Rector, III, James W.; Bube, Kenneth P.

2002-01-01

Characterizing the value of a particular property, for example, seismic velocity, of a subsurface region of ground is described. In one aspect, the value of the particular property is represented using at least one continuous analytic function such as a Chebychev polynomial. The seismic data may include data derived from at least one crosswell dataset for the subsurface region of interest and may also include other data. In either instance, data may simultaneously be used from a first crosswell dataset in conjunction with one or more other crosswell datasets and/or with the other data. In another aspect, the value of the property is characterized in three dimensions throughout the region of interest using crosswell and/or other data. In still another aspect, crosswell datasets for highly deviated or horizontal boreholes are inherently useful. The method is performed, in part, by fitting a set of vertically spaced layer boundaries, represented by an analytic function such as a Chebychev polynomial, within and across the region encompassing the boreholes such that a series of layers is defined between the layer boundaries. Initial values of the particular property are then established between the layer boundaries and across the subterranean region using a series of continuous analytic functions. The continuous analytic functions are then adjusted to more closely match the value of the particular property across the subterranean region of ground to determine the value of the particular property for any selected point within the region.

Spatially Complete Global Spectral Surface Albedos: Value-Added Datasets Derived from Terra MODIS Land Products

NASA Technical Reports Server (NTRS)

Moody, Eric G.; King, Michael D.; Platnick, Steven; Schaaf, Crystal B.; Gao, Feng

2004-01-01

Land surface albedo is an important parameter in describing the radiative properties of the earth s surface as it represents the amount of incoming solar radiation that is reflected from the surface. The amount and type of vegetation of the surface dramatically alters the amount of radiation that is reflected; for example, croplands that contain leafy vegetation will reflect radiation very differently than blacktop associated with urban areas. In addition, since vegetation goes through a growth, or phenological, cycle, the amount of radiation that is reflected changes over the course of a year. As a result, albedo is both temporally and spatially dependant upon global location as there is a distribution of vegetated surface types and growing conditions. Land surface albedo is critical for a wide variety of earth system research projects including but not restricted to remote sensing of atmospheric aerosol and cloud properties from space, ground-based analysis of aerosol optical properties from surface-based sun/sky radiometers, biophysically-based land surface modeling of the exchange of energy, water, momentum, and carbon for various land use categories, and surface energy balance studies. These projects require proper representation of the surface albedo s spatial, spectral, and temporal variations, however, these representations are often lacking in datasets prior to the latest generation of land surface albedo products.

Attributes for NHDPlus Catchments (Version 1.1) in the Conterminous United States: Artificial Drainage (1992) and Irrigation Types (1997)

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This tabular dataset represents the estimated area of artificial drainage for the year 1992 and irrigation types for the year 1997 compiled for every catchment of NHDPlus for the conterminous United States. The source datasets were derived from tabular National Resource Inventory (NRI) datasets created by the National Resources Conservation Service (NRCS, U.S. Department of Agriculture, 1995, 1997). Artificial drainage is defined as subsurface drains and ditches. Irrigation types are defined as gravity and pressure. Subsurface drains are described as conduits, such as corrugated plastic tubing, tile, or pipe, installed beneath the ground surface to collect and/or convey drainage. Surface drainage field ditches are described as graded ditches for collecting excess water. Gravity irrigation source is described as irrigation delivered to the farm and/or field by canals or pipelines open to the atmosphere; and water is distributed by the force of gravity down the field by: (1) A surface irrigation system (border, basin, furrow, corrugation, wild flooding, etc.) or (2) Sub-surface irrigation pipelines or ditches. Pressure irrigation source is described as irrigation delivered to the farm and/or field in pump or elevation-induced pressure pipelines, and water is distributed across the field by: (1) Sprinkle irrigation (center pivot, linear move, traveling gun, side roll, hand move, big gun, or fixed set sprinklers), or (2) Micro irrigation (drip emitters, continuous tube bubblers, micro spray or micro sprinklers). NRI data do not include Federal lands and are thus excluded from this dataset. The tabular data for drainage were spatially apportioned to the National Land Cover Dataset (NLCD, Kerie Hitt, written commun., 2005) and the tabular data for irrigation were spatially apportioned to an enhanced version of the National Land Cover Dataset (NLCDe, Nakagaki and others 2007) The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geological Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Determination and validation of an aquatic Maximum Acceptable Concentration-Environmental Quality Standard (MAC-EQS) value for the agricultural fungicide azoxystrobin.

PubMed

Rodrigues, Elsa Teresa; Pardal, Miguel Ângelo; Gante, Cristiano; Loureiro, João; Lopes, Isabel

2017-02-01

The main goal of the present study was to determine and validate an aquatic Maximum Acceptable Concentration-Environmental Quality Standard (MAC-EQS) value for the agricultural fungicide azoxystrobin (AZX). Assessment factors were applied to short-term toxicity data using the lowest EC 50 and after the Species Sensitivity Distribution (SSD) method. Both ways of EQS generation were applied to a freshwater toxicity dataset for AZX based on available data, and to marine toxicity datasets for AZX and Ortiva ® (a commercial formulation of AZX) obtained by the present study. A high interspecific variability in AZX sensitivity was observed in all datasets, being the copepoda Eudiaptomus graciloides (LC 50,48h  = 38 μg L -1 ) and the gastropod Gibbula umbilicalis (LC 50,96h  = 13 μg L -1 ) the most sensitive freshwater and marine species, respectively. MAC-EQS values derived using the lowest EC 50 (≤0.38 μg L -1 ) were more protective than those derived using the SSD method (≤3.2 μg L -1 ). After comparing the MAC-EQS values estimated in the present study to the smallest AA-EQS available, which protect against the occurrence of prolonged exposure of AZX, the MAC-EQS values derived using the lowest EC 50 were considered overprotective and a MAC-EQS of 1.8 μg L -1 was validated and recommended for AZX for the water column. This value was derived from marine toxicity data, which highlights the importance of testing marine organisms. Moreover, Ortiva affects the most sensitive marine species to a greater extent than AZX, and marine species are more sensitive than freshwater species to AZX. A risk characterization ratio higher than one allowed to conclude that AZX might pose a high risk to the aquatic environment. Also, in a wider conclusion, before new pesticides are approved, we suggest to improve the Tier 1 prospective Ecological Risk Assessment by increasing the number of short-term data, and apply the SSD approach, in order to ensure the safety of aquatic organisms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Attributes for NHDPlus Catchments (Version 1.1) in the Conterminous United States: Bedrock Geology

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the area of bedrock geology types in square meters compiled for every catchment of NHDPlus for the conterminous United States. The source data set is the "Geology of the Conterminous United States at 1:2,500,000 Scale--A Digital Representation of the 1974 P.B. King and H.M. Beikman Map" (Schuben and others, 1994). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Level 3 Ecoregions

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the estimated area of level 3 ecological landscape regions (ecoregions), as defined by Omernik (1987), compiled for every catchment of NHDPlus for the conterminous United States. The source data set is Level III Ecoregions of the Continental United States (U.S. Environmental Protection Agency, 2003). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Hydrologic Landscape Regions

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the area of Hydrologic Landscape Regions (HLR) compiled for every catchment of NHDPlus for the conterminous United States. The source data set is a 100-meter version of Hydrologic Landscape Regions of the United States (Wolock, 2003). HLR groups watersheds on the basis of similarities in land-surface form, geologic texture, and climate characteristics. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1): Level 3 Nutrient Ecoregions, 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the area of each level 3 nutrient ecoregion in square meters, compiled for every catchment of NHDPlus for the conterminous United States. The source data are from the 2002 version of the U.S. Environmental Protection Agency's (USEPA) Aggregations of Level III Ecoregions for National Nutrient Assessment & Management Strategy (USEPA, 2002). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1): Basin Characteristics, 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents basin characteristics, compiled for every catchment in NHDPlus for the conterminous United States. These characteristics are basin shape index, stream density, sinuosity, mean elevation, mean slope, and number of road-stream crossings. The source data sets are the U.S. Environmental Protection Agency's NHDPlus and the U.S. Census Bureau's TIGER/Line Files. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Base-Flow Index

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This tabular data set represents the mean base-flow index expressed as a percent, compiled for every catchment in NHDPlus for the conterminous United States. Base flow is the component of streamflow that can be attributed to ground-water discharge into streams. The source data set is Base-Flow Index for the Conterminous United States (Wolock, 2003). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Mean Annual R-factor, 1971-2000

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the average annual R-factor, rainfall-runoff erosivity measure, compiled for every catchment of NHDPlus for the conterminous United States. The source data are from Christopher Daly of the Spatial Climate Analysis Service, Oregon State University, and George Taylor of the Oregon Climate Service, Oregon State University (2002), who developed spatially distributed estimates of R-factor for the period 1971-2000 for the conterminous United States. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Average Atmospheric (Wet) Deposition of Inorganic Nitrogen, 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the average atmospheric (wet) deposition, in kilograms per square kilometer, of inorganic nitrogen for the year 2002 compiled for every catchment of NHDPlus for the conterminous United States. The source data set for wet deposition was from the USGS's raster data set atmospheric (wet) deposition of inorganic nitrogen for 2002 (Gronberg, 2005). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years (2007-2008), an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Nutrient Inputs from Fertilizer and Manure, Nitrogen and Phosphorus (N&P), 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the estimated amount of nitrogen and phosphorus in kilograms for the year 2002, compiled for every catchment of NHDPlus for the conterminous United States. The source data set is County-Level Estimates of Nutrient Inputs to the Land Surface of the Conterminous United States, 1982-2001 (Ruddy and others, 2006). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus catchments (Version 1.1) for the conterminous United States: STATSGO soil characteristics

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents estimated soil variables compiled for every catchment of NHDPlus for the conterminous United States. The variables included are cation exchange capacity, percent calcium carbonate, slope, water-table depth, soil thickness, hydrologic soil group, soil erodibility (k-factor), permeability, average water capacity, bulk density, percent organic material, percent clay, percent sand, and percent silt. The source data set is the State Soil ( STATSGO ) Geographic Database (Wolock, 1997). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Estimated Mean Annual Natural Groundwater Recharge, 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the mean annual natural groundwater recharge, in millimeters, compiled for every catchment of NHDPlus for the conterminous United States. The source data set is Estimated Mean Annual Natural Ground-Water Recharge in the Conterminous United States (Wolock, 2003). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, containing NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDplus Catchments (Version 1.1) for the Conterminous United States: Population Density, 2000

USGS Publications Warehouse

Wieczorek, Michael; LaMottem, Andrew E.

2010-01-01

This data set represents the average population density, in number of people per square kilometer multiplied by 10 for the year 2000, compiled for every catchment of NHDPlus for the conterminous United States. The source data set is the 2000 Population Density by Block Group for the Conterminous United States (Hitt, 2003). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Nutrient Application (Phosphorus and Nitrogen ) for Fertilizer and Manure Applied to Crops (Cropsplit), 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the estimated amount of phosphorus and nitrogen fertilizers applied to selected crops for the year 2002, compiled for every catchment of NHDPlus for the conterminous United States. The source data set is based on 2002 fertilizer data (Ruddy and others, 2006) and tabulated by crop type per county (Alexander and others, 2007). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Cloud Compute for Global Climate Station Summaries

NASA Astrophysics Data System (ADS)

Baldwin, R.; May, B.; Cogbill, P.

2017-12-01

Global Climate Station Summaries are simple indicators of observational normals which include climatic data summarizations and frequency distributions. These typically are statistical analyses of station data over 5-, 10-, 20-, 30-year or longer time periods. The summaries are computed from the global surface hourly dataset. This dataset totaling over 500 gigabytes is comprised of 40 different types of weather observations with 20,000 stations worldwide. NCEI and the U.S. Navy developed these value added products in the form of hourly summaries from many of these observations. Enabling this compute functionality in the cloud is the focus of the project. An overview of approach and challenges associated with application transition to the cloud will be presented.

Deep-learning-based classification of FDG-PET data for Alzheimer's disease categories

NASA Astrophysics Data System (ADS)

Singh, Shibani; Srivastava, Anant; Mi, Liang; Caselli, Richard J.; Chen, Kewei; Goradia, Dhruman; Reiman, Eric M.; Wang, Yalin

2017-11-01

Fluorodeoxyglucose (FDG) positron emission tomography (PET) measures the decline in the regional cerebral metabolic rate for glucose, offering a reliable metabolic biomarker even on presymptomatic Alzheimer's disease (AD) patients. PET scans provide functional information that is unique and unavailable using other types of imaging. However, the computational efficacy of FDG-PET data alone, for the classification of various Alzheimers Diagnostic categories, has not been well studied. This motivates us to correctly discriminate various AD Diagnostic categories using FDG-PET data. Deep learning has improved state-of-the-art classification accuracies in the areas of speech, signal, image, video, text mining and recognition. We propose novel methods that involve probabilistic principal component analysis on max-pooled data and mean-pooled data for dimensionality reduction, and multilayer feed forward neural network which performs binary classification. Our experimental dataset consists of baseline data of subjects including 186 cognitively unimpaired (CU) subjects, 336 mild cognitive impairment (MCI) subjects with 158 Late MCI and 178 Early MCI, and 146 AD patients from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We measured F1-measure, precision, recall, negative and positive predictive values with a 10-fold cross validation scheme. Our results indicate that our designed classifiers achieve competitive results while max pooling achieves better classification performance compared to mean-pooled features. Our deep model based research may advance FDG-PET analysis by demonstrating their potential as an effective imaging biomarker of AD.

Clinical high-resolution mapping of the proteoglycan-bound water fraction in articular cartilage of the human knee joint.

PubMed

Bouhrara, Mustapha; Reiter, David A; Sexton, Kyle W; Bergeron, Christopher M; Zukley, Linda M; Spencer, Richard G

2017-11-01

We applied our recently introduced Bayesian analytic method to achieve clinically-feasible in-vivo mapping of the proteoglycan water fraction (PgWF) of human knee cartilage with improved spatial resolution and stability as compared to existing methods. Multicomponent driven equilibrium single-pulse observation of T 1 and T 2 (mcDESPOT) datasets were acquired from the knees of two healthy young subjects and one older subject with previous knee injury. Each dataset was processed using Bayesian Monte Carlo (BMC) analysis incorporating a two-component tissue model. We assessed the performance and reproducibility of BMC and of the conventional analysis of stochastic region contraction (SRC) in the estimation of PgWF. Stability of the BMC analysis of PgWF was tested by comparing independent high-resolution (HR) datasets from each of the two young subjects. Unlike SRC, the BMC-derived maps from the two HR datasets were essentially identical. Furthermore, SRC maps showed substantial random variation in estimated PgWF, and mean values that differed from those obtained using BMC. In addition, PgWF maps derived from conventional low-resolution (LR) datasets exhibited partial volume and magnetic susceptibility effects. These artifacts were absent in HR PgWF images. Finally, our analysis showed regional variation in PgWF estimates, and substantially higher values in the younger subjects as compared to the older subject. BMC-mcDESPOT permits HR in-vivo mapping of PgWF in human knee cartilage in a clinically-feasible acquisition time. HR mapping reduces the impact of partial volume and magnetic susceptibility artifacts compared to LR mapping. Finally, BMC-mcDESPOT demonstrated excellent reproducibility in the determination of PgWF. Published by Elsevier Inc.

[Establishing a noninvasive prediction model for type 2 diabetes mellitus based on a rural Chinese population].

PubMed

Zhang, H Y; Shi, W H; Zhang, M; Yin, L; Pang, C; Feng, T P; Zhang, L; Ren, Y C; Wang, B Y; Yang, X Y; Zhou, J M; Han, C Y; Zhao, Y; Zhao, J Z; Hu, D S

2016-05-01

To provide a noninvasive type 2 diabetes mellitus (T2DM) prediction model for a rural Chinese population. From July to August, 2007 and July to August, 2008, a total of 20 194 participants aged ≥18 years were selected by cluster sampling technique from a rural population in two townships of Henan province, China. Data were collected by questionnaire interview, anthropometric measurement, and fasting plasma glucose and lipid profile examination. A total 17 265 participants were followed up from July to August, 2013 and July to October, 2014. Finally, 12 285 participants were selected for analysis. Data for these participants were randomly divided into a derivation group (derivation dataset, n= 6 143) and validation group (validation dataset, n=6 142) by 1∶1, respectively. Randomization was carried out by the use of computer-generated random numbers. A Cox regression model was used to analyze risk factors of T2DM in the derivation dataset. A T2DM prediction model was established by multiplying β by 10 for each significant variable. After the total score was calculated by the model, analysis of the receiver operating characteristic (ROC) curve was performed. The area under the ROC curve (AUC) was used for evaluating model predictability. Furthermore, the model's predictability was validated in the validation dataset and compared with the Finnish Diabetes Risk Score (FINDRISC) model. A total 779 of 12 285 participants developed T2DM during the 6-year study period. The incidence rate was 6.12% in the derivation dataset (n=376) and 6.56% in the validation dataset (n=403). The difference was not statistically significant (χ(2)=1.00, P=0.316). A total of four noninvasive T2DM prediction models were established using the Cox regression model. The ROCs of the risk score calculated by the prediction models indicated that the AUCs of these models were similar (0.67-0.70). The AUC and Youden index of model 4 was the highest. The optimal cut-off value, sensitivity, specificity, and Youden index were scores of 25, 65.96%, 66.47%, and 0.32, respectively. Age, sleep time, BMI, waist circumference, and hypertension were selected as predictive variables. Using age<30 years as reference, β values were 1.07, 1.58, and 1.67 and assigned scores were 11, 16, and 17 for age groups 30-44, 45-59, and ≥60 years, respectively. Using sleep time<8.0 h/d as reference, the β value and assigned score were 0.27 and 3, respectively, for sleep time ≥10.0 h/d. Using BMI 18.5-23.9 kg/m(2) as reference, β values were 0.53 and 1.00 and assigned scores 5 and 10, respectively, for BMI 24.0-27.9 kg/m(2), and ≥28.0 kg/m(2). Using waist circumference <85 cm for males/< 80 cm for females as reference, β values were 0.44 and 0.65 and assigned scores 4 and 7, respectively, for 85 cm ≤ waist circumference <90 cm for males/80 cm≤ waist circumference <85 cm for females, and waist circumference ≥90 cm for males/≥85 cm for females. Using nonhypertension as reference, the respective β value and assigned score were 0.34 and 3 for hypertension. The AUC performance of this model and the FINDRISC model was 0.66 and 0.64 (P=0.135), respectively, in the validation dataset. Based on this cohort study, a noninvasive prediction model that included age, sleep time, BMI, waist circumference, and hypertension was established, which is equivalent to the FINDRISC model and applicable to a rural Chinese population.

Concerted Perturbation Observed in a Hub Network in Alzheimer’s Disease

PubMed Central

Liang, Dapeng; Han, Guangchun; Feng, Xuemei; Sun, Jiya; Duan, Yong; Lei, Hongxing

2012-01-01

Alzheimer’s disease (AD) is a progressive neurodegenerative disease involving the alteration of gene expression at the whole genome level. Genome-wide transcriptional profiling of AD has been conducted by many groups on several relevant brain regions. However, identifying the most critical dys-regulated genes has been challenging. In this work, we addressed this issue by deriving critical genes from perturbed subnetworks. Using a recent microarray dataset on six brain regions, we applied a heaviest induced subgraph algorithm with a modular scoring function to reveal the significantly perturbed subnetwork in each brain region. These perturbed subnetworks were found to be significantly overlapped with each other. Furthermore, the hub genes from these perturbed subnetworks formed a connected hub network consisting of 136 genes. Comparison between AD and several related diseases demonstrated that the hub network was robustly and specifically perturbed in AD. In addition, strong correlation between the expression level of these hub genes and indicators of AD severity suggested that this hub network can partially reflect AD progression. More importantly, this hub network reflected the adaptation of neurons to the AD-specific microenvironment through a variety of adjustments, including reduction of neuronal and synaptic activities and alteration of survival signaling. Therefore, it is potentially useful for the development of biomarkers and network medicine for AD. PMID:22815752

Automatic CDR Estimation for Early Glaucoma Diagnosis

PubMed Central

Sarmiento, A.; Sanchez-Morillo, D.; Jiménez, S.; Alemany, P.

2017-01-01

Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE L∗a∗b∗ colour space with CIE94 colour distance. In addition, we consider spatial information retaining these colour derivatives and the original CIE L∗a∗b∗ values of the pixel and adding other characteristics such as its distance to the OD centre. The proposed strategy is robust due to a simple structure that does not need neither initial segmentation nor removal of the vascular tree or detection of vessel bends. The method has been extensively validated with two datasets (one public and one private), each one comprising 60 images of high variability of appearances. Achieved class-wise-averaged accuracy of 95.02% and 81.19% demonstrates that this automated approach could support physicians in the diagnosis of glaucoma in its early stage, and therefore, it could be seen as an opportunity for developing low-cost solutions for mass screening programs. PMID:29279773

On piecewise interpolation techniques for estimating solar radiation missing values in Kedah

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

Saaban, Azizan; Zainudin, Lutfi; Bakar, Mohd Nazari Abu

2014-12-04

This paper discusses the use of piecewise interpolation method based on cubic Ball and Bézier curves representation to estimate the missing value of solar radiation in Kedah. An hourly solar radiation dataset is collected at Alor Setar Meteorology Station that is taken from Malaysian Meteorology Deparment. The piecewise cubic Ball and Bézier functions that interpolate the data points are defined on each hourly intervals of solar radiation measurement and is obtained by prescribing first order derivatives at the starts and ends of the intervals. We compare the performance of our proposed method with existing methods using Root Mean Squared Errormore » (RMSE) and Coefficient of Detemination (CoD) which is based on missing values simulation datasets. The results show that our method is outperformed the other previous methods.« less

Updated population metadata for United States historical climatology network stations

USGS Publications Warehouse

Owen, T.W.; Gallo, K.P.

2000-01-01

The United States Historical Climatology Network (HCN) serial temperature dataset is comprised of 1221 high-quality, long-term climate observing stations. The HCN dataset is available in several versions, one of which includes population-based temperature modifications to adjust urban temperatures for the "heat-island" effect. Unfortunately, the decennial population metadata file is not complete as missing values are present for 17.6% of the 12 210 population values associated with the 1221 individual stations during the 1900-90 interval. Retrospective grid-based populations. Within a fixed distance of an HCN station, were estimated through the use of a gridded population density dataset and historically available U.S. Census county data. The grid-based populations for the HCN stations provide values derived from a consistent methodology compared to the current HCN populations that can vary as definitions of the area associated with a city change over time. The use of grid-based populations may minimally be appropriate to augment populations for HCN climate stations that lack any population data, and are recommended when consistent and complete population data are required. The recommended urban temperature adjustments based on the HCN and grid-based methods of estimating station population can be significantly different for individual stations within the HCN dataset.

Two-pass imputation algorithm for missing value estimation in gene expression time series.

PubMed

Tsiporkova, Elena; Boeva, Veselka

2007-10-01

Gene expression microarray experiments frequently generate datasets with multiple values missing. However, most of the analysis, mining, and classification methods for gene expression data require a complete matrix of gene array values. Therefore, the accurate estimation of missing values in such datasets has been recognized as an important issue, and several imputation algorithms have already been proposed to the biological community. Most of these approaches, however, are not particularly suitable for time series expression profiles. In view of this, we propose a novel imputation algorithm, which is specially suited for the estimation of missing values in gene expression time series data. The algorithm utilizes Dynamic Time Warping (DTW) distance in order to measure the similarity between time expression profiles, and subsequently selects for each gene expression profile with missing values a dedicated set of candidate profiles for estimation. Three different DTW-based imputation (DTWimpute) algorithms have been considered: position-wise, neighborhood-wise, and two-pass imputation. These have initially been prototyped in Perl, and their accuracy has been evaluated on yeast expression time series data using several different parameter settings. The experiments have shown that the two-pass algorithm consistently outperforms, in particular for datasets with a higher level of missing entries, the neighborhood-wise and the position-wise algorithms. The performance of the two-pass DTWimpute algorithm has further been benchmarked against the weighted K-Nearest Neighbors algorithm, which is widely used in the biological community; the former algorithm has appeared superior to the latter one. Motivated by these findings, indicating clearly the added value of the DTW techniques for missing value estimation in time series data, we have built an optimized C++ implementation of the two-pass DTWimpute algorithm. The software also provides for a choice between three different initial rough imputation methods.

The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap.

PubMed

Mann, Richard P; Mushtaq, Faisal; White, Alan D; Mata-Cervantes, Gabriel; Pike, Tom; Coker, Dalton; Murdoch, Stuart; Hiles, Tim; Smith, Clare; Berridge, David; Hinchliffe, Suzanne; Hall, Geoff; Smye, Stephen; Wilkie, Richard M; Lodge, J Peter A; Mon-Williams, Mark

2016-01-01

Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public's perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit "big data."

HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2017-01-15

Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseases on an individual basis. The driving assumption behind the vast majority of the existing methodologies is that a single imaging pattern can distinguish between healthy and diseased populations, or between two subgroups of patients (e.g., progressors vs. non-progressors). This assumption effectively ignores the ample evidence for the heterogeneous nature of brain diseases. Neurodegenerative, neuropsychiatric and neurodevelopmental disorders are largely characterized by high clinical heterogeneity, which likely stems in part from underlying neuroanatomical heterogeneity of various pathologies. Detecting and characterizing heterogeneity may deepen our understanding of disease mechanisms and lead to patient-specific treatments. However, few approaches tackle disease subtype discovery in a principled machine learning framework. To address this challenge, we present a novel non-linear learning algorithm for simultaneous binary classification and subtype identification, termed HYDRA (Heterogeneity through Discriminative Analysis). Neuroanatomical subtypes are effectively captured by multiple linear hyperplanes, which form a convex polytope that separates two groups (e.g., healthy controls from pathologic samples); each face of this polytope effectively defines a disease subtype. We validated HYDRA on simulated and clinical data. In the latter case, we applied the proposed method independently to the imaging and genetic datasets of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) study. The imaging dataset consisted of T1-weighted volumetric magnetic resonance images of 123 AD patients and 177 controls. The genetic dataset consisted of single nucleotide polymorphism information of 103 AD patients and 139 controls. We identified 3 reproducible subtypes of atrophy in AD relative to controls: (1) diffuse and extensive atrophy, (2) precuneus and extensive temporal lobe atrophy, as well some prefrontal atrophy, (3) atrophy pattern very much confined to the hippocampus and the medial temporal lobe. The genetics dataset yielded two subtypes of AD characterized mainly by the presence/absence of the apolipoprotein E (APOE) ε4 genotype, but also involving differential presence of risk alleles of CD2AP, SPON1 and LOC39095 SNPs that were associated with differences in the respective patterns of brain atrophy, especially in the precuneus. The results demonstrate the potential of the proposed approach to map disease heterogeneity in neuroimaging and genetic studies. Copyright © 2016 Elsevier Inc. All rights reserved.

Integration of Heterogenous Digital Surface Models

NASA Astrophysics Data System (ADS)

Boesch, R.; Ginzler, C.

2011-08-01

The application of extended digital surface models often reveals, that despite an acceptable global accuracy for a given dataset, the local accuracy of the model can vary in a wide range. For high resolution applications which cover the spatial extent of a whole country, this can be a major drawback. Within the Swiss National Forest Inventory (NFI), two digital surface models are available, one derived from LiDAR point data and the other from aerial images. Automatic photogrammetric image matching with ADS80 aerial infrared images with 25cm and 50cm resolution is used to generate a surface model (ADS-DSM) with 1m resolution covering whole switzerland (approx. 41000 km2). The spatially corresponding LiDAR dataset has a global point density of 0.5 points per m2 and is mainly used in applications as interpolated grid with 2m resolution (LiDAR-DSM). Although both surface models seem to offer a comparable accuracy from a global view, local analysis shows significant differences. Both datasets have been acquired over several years. Concerning LiDAR-DSM, different flight patterns and inconsistent quality control result in a significantly varying point density. The image acquisition of the ADS-DSM is also stretched over several years and the model generation is hampered by clouds, varying illumination and shadow effects. Nevertheless many classification and feature extraction applications requiring high resolution data depend on the local accuracy of the used surface model, therefore precise knowledge of the local data quality is essential. The commercial photogrammetric software NGATE (part of SOCET SET) generates the image based surface model (ADS-DSM) and delivers also a map with figures of merit (FOM) of the matching process for each calculated height pixel. The FOM-map contains matching codes like high slope, excessive shift or low correlation. For the generation of the LiDAR-DSM only first- and last-pulse data was available. Therefore only the point distribution can be used to derive a local accuracy measure. For the calculation of a robust point distribution measure, a constrained triangulation of local points (within an area of 100m2) has been implemented using the Open Source project CGAL. The area of each triangle is a measure for the spatial distribution of raw points in this local area. Combining the FOM-map with the local evaluation of LiDAR points allows an appropriate local accuracy evaluation of both surface models. The currently implemented strategy ("partial replacement") uses the hypothesis, that the ADS-DSM is superior due to its better global accuracy of 1m. If the local analysis of the FOM-map within the 100m2 area shows significant matching errors, the corresponding area of the triangulated LiDAR points is analyzed. If the point density and distribution is sufficient, the LiDAR-DSM will be used in favor of the ADS-DSM at this location. If the local triangulation reflects low point density or the variance of triangle areas exceeds a threshold, the investigated location will be marked as NODATA area. In a future implementation ("anisotropic fusion") an anisotropic inverse distance weighting (IDW) will be used, which merges both surface models in the point data space by using FOM-map and local triangulation to derive a quality weight for each of the interpolation points. The "partial replacement" implementation and the "fusion" prototype for the anisotropic IDW make use of the Open Source projects CGAL (Computational Geometry Algorithms Library), GDAL (Geospatial Data Abstraction Library) and OpenCV (Open Source Computer Vision).

A Robust Post-Processing Workflow for Datasets with Motion Artifacts in Diffusion Kurtosis Imaging

PubMed Central

Li, Xianjun; Yang, Jian; Gao, Jie; Luo, Xue; Zhou, Zhenyu; Hu, Yajie; Wu, Ed X.; Wan, Mingxi

2014-01-01

Purpose The aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI). Materials and methods The proposed workflow consisted of brain extraction, rigid registration, distortion correction, artifacts rejection, spatial smoothing and tensor estimation. Rigid registration was utilized to correct misalignments. Motion artifacts were rejected by using local Pearson correlation coefficient (LPCC). The performance of LPCC in characterizing relative differences between artifacts and artifact-free images was compared with that of the conventional correlation coefficient in 10 randomly selected DKI datasets. The influence of rejected artifacts with information of gradient directions and b values for the parameter estimation was investigated by using mean square error (MSE). The variance of noise was used as the criterion for MSEs. The clinical practicality of the proposed workflow was evaluated by the image quality and measurements in regions of interest on 36 DKI datasets, including 18 artifact-free (18 pediatric subjects) and 18 motion-corrupted datasets (15 pediatric subjects and 3 essential tremor patients). Results The relative difference between artifacts and artifact-free images calculated by LPCC was larger than that of the conventional correlation coefficient (p<0.05). It indicated that LPCC was more sensitive in detecting motion artifacts. MSEs of all derived parameters from the reserved data after the artifacts rejection were smaller than the variance of the noise. It suggested that influence of rejected artifacts was less than influence of noise on the precision of derived parameters. The proposed workflow improved the image quality and reduced the measurement biases significantly on motion-corrupted datasets (p<0.05). Conclusion The proposed post-processing workflow was reliable to improve the image quality and the measurement precision of the derived parameters on motion-corrupted DKI datasets. The workflow provided an effective post-processing method for clinical applications of DKI in subjects with involuntary movements. PMID:24727862

A robust post-processing workflow for datasets with motion artifacts in diffusion kurtosis imaging.

PubMed

Li, Xianjun; Yang, Jian; Gao, Jie; Luo, Xue; Zhou, Zhenyu; Hu, Yajie; Wu, Ed X; Wan, Mingxi

2014-01-01

The aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI). The proposed workflow consisted of brain extraction, rigid registration, distortion correction, artifacts rejection, spatial smoothing and tensor estimation. Rigid registration was utilized to correct misalignments. Motion artifacts were rejected by using local Pearson correlation coefficient (LPCC). The performance of LPCC in characterizing relative differences between artifacts and artifact-free images was compared with that of the conventional correlation coefficient in 10 randomly selected DKI datasets. The influence of rejected artifacts with information of gradient directions and b values for the parameter estimation was investigated by using mean square error (MSE). The variance of noise was used as the criterion for MSEs. The clinical practicality of the proposed workflow was evaluated by the image quality and measurements in regions of interest on 36 DKI datasets, including 18 artifact-free (18 pediatric subjects) and 18 motion-corrupted datasets (15 pediatric subjects and 3 essential tremor patients). The relative difference between artifacts and artifact-free images calculated by LPCC was larger than that of the conventional correlation coefficient (p<0.05). It indicated that LPCC was more sensitive in detecting motion artifacts. MSEs of all derived parameters from the reserved data after the artifacts rejection were smaller than the variance of the noise. It suggested that influence of rejected artifacts was less than influence of noise on the precision of derived parameters. The proposed workflow improved the image quality and reduced the measurement biases significantly on motion-corrupted datasets (p<0.05). The proposed post-processing workflow was reliable to improve the image quality and the measurement precision of the derived parameters on motion-corrupted DKI datasets. The workflow provided an effective post-processing method for clinical applications of DKI in subjects with involuntary movements.

Using Individualized Brain Network for Analyzing Structural Covariance of the Cerebral Cortex in Alzheimer's Patients.

PubMed

Kim, Hee-Jong; Shin, Jeong-Hyeon; Han, Cheol E; Kim, Hee Jin; Na, Duk L; Seo, Sang Won; Seong, Joon-Kyung

2016-01-01

Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are "small world." There were significant difference between NC and AD group in characteristic path lengths (z = -2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC dataset.

Geospatial datasets for watershed delineation and characterization used in the Hawaii StreamStats web application

USGS Publications Warehouse

Rea, Alan; Skinner, Kenneth D.

2012-01-01

The U.S. Geological Survey Hawaii StreamStats application uses an integrated suite of raster and vector geospatial datasets to delineate and characterize watersheds. The geospatial datasets used to delineate and characterize watersheds on the StreamStats website, and the methods used to develop the datasets are described in this report. The datasets for Hawaii were derived primarily from 10 meter resolution National Elevation Dataset (NED) elevation models, and the National Hydrography Dataset (NHD), using a set of procedures designed to enforce the drainage pattern from the NHD into the NED, resulting in an integrated suite of elevation-derived datasets. Additional sources of data used for computing basin characteristics include precipitation, land cover, soil permeability, and elevation-derivative datasets. The report also includes links for metadata and downloads of the geospatial datasets.

Monitoring and mapping leaf area index of rubber and oil palm in small watershed area

NASA Astrophysics Data System (ADS)

Rusli, N.; Majid, M. R.

2014-02-01

Existing conventional methods to determine LAI are tedious and time consuming for implementation in small or large areas. Thus, raster LAI data which are available free were downloaded for 4697.60 km2 of Sungai Muar watershed area in Johor. The aim of this study is to monitor and map LAI changes of rubber and oil palm throughout the years from 2002 to 2008. Raster datasets of LAI value were obtained from the National Aeronautics and Space Administration (NASA) website of available years from 2002 to year 2008. These data, were mosaicked and subset utilizing ERDAS Imagine 9.2. Next, the LAI raster dataset was multiplied by a scale factor of 0.1 to derive the final LAI value. Afterwards, to determine LAI values of rubber and oil palms, the boundaries of each crop from land cover data of the years 2002, 2006 and 2008 were exploited to overlay with LAI raster dataset. A total of 5000 sample points were generated utilizing the Hawths Tool (extension in ARcGIS 9.2) within these boundaries area and utilized for extracting LAI value of oil palm and rubber. In integration, a wide range of literature review was conducted as a guideline to derive LAI value of oil palm and rubber which range from 0 to 6. The results show, an overall mean LAI value from year 2002 to 2008 as decremented from 4.12 to 2.5 due to land cover transition within these years. In 2002, the mean LAI value of rubber and oil palm is 2.65 and 2.53 respectively. Meanwhile in 2006, the mean LAI value for rubber and oil palm is 2.54 and 2.82 respectively. In 2008, the mean LAI value for both crops is 0.85 for rubber and 1.04 for oil palm. In conclusion, apart from the original function of LAI which is related to the growth and metabolism of vegetation, the changes of LAI values from year 2002 to 2008 also capable to explain the process of land cover changes in a watershed area.

ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition.

PubMed

Sharma, Hemant; Sharma, K K

2018-06-01

Monitoring of the respiration using the electrocardiogram (ECG) is desirable for the simultaneous study of cardiac activities and the respiration in the aspects of comfort, mobility, and cost of the healthcare system. This paper proposes a new approach for deriving the respiration from single-lead ECG based on the iterated Hilbert transform (IHT) and the Hilbert vibration decomposition (HVD). The ECG signal is first decomposed into the multicomponent sinusoidal signals using the IHT technique. Afterward, the lower order amplitude components obtained from the IHT are filtered using the HVD to extract the respiration information. Experiments are performed on the Fantasia and Apnea-ECG datasets. The performance of the proposed ECG-derived respiration (EDR) approach is compared with the existing techniques including the principal component analysis (PCA), R-peak amplitudes (RPA), respiratory sinus arrhythmia (RSA), slopes of the QRS complex, and R-wave angle. The proposed technique showed the higher median values of correlation (first and third quartile) for both the Fantasia and Apnea-ECG datasets as 0.699 (0.55, 0.82) and 0.57 (0.40, 0.73), respectively. Also, the proposed algorithm provided the lowest values of the mean absolute error and the average percentage error computed from the EDR and reference (recorded) respiration signals for both the Fantasia and Apnea-ECG datasets as 1.27 and 9.3%, and 1.35 and 10.2%, respectively. In the experiments performed over different age group subjects of the Fantasia dataset, the proposed algorithm provided effective results in the younger population but outperformed the existing techniques in the case of elderly subjects. The proposed EDR technique has the advantages over existing techniques in terms of the better agreement in the respiratory rates and specifically, it reduces the need for an extra step required for the detection of fiducial points in the ECG for the estimation of respiration which makes the process effective and less-complex. The above performance results obtained from two different datasets validate that the proposed approach can be used for monitoring of the respiration using single-lead ECG.

Experimental formation enthalpies for intermetallic phases and other inorganic compounds

PubMed Central

Kim, George; Meschel, S. V.; Nash, Philip; Chen, Wei

2017-01-01

The standard enthalpy of formation of a compound is the energy associated with the reaction to form the compound from its component elements. The standard enthalpy of formation is a fundamental thermodynamic property that determines its phase stability, which can be coupled with other thermodynamic data to calculate phase diagrams. Calorimetry provides the only direct method by which the standard enthalpy of formation is experimentally measured. However, the measurement is often a time and energy intensive process. We present a dataset of enthalpies of formation measured by high-temperature calorimetry. The phases measured in this dataset include intermetallic compounds with transition metal and rare-earth elements, metal borides, metal carbides, and metallic silicides. These measurements were collected from over 50 years of calorimetric experiments. The dataset contains 1,276 entries on experimental enthalpy of formation values and structural information. Most of the entries are for binary compounds but ternary and quaternary compounds are being added as they become available. The dataset also contains predictions of enthalpy of formation from first-principles calculations for comparison. PMID:29064466

A model-based 3D phase unwrapping algorithm using Gegenbauer polynomials.

PubMed

Langley, Jason; Zhao, Qun

2009-09-07

The application of a two-dimensional (2D) phase unwrapping algorithm to a three-dimensional (3D) phase map may result in an unwrapped phase map that is discontinuous in the direction normal to the unwrapped plane. This work investigates the problem of phase unwrapping for 3D phase maps. The phase map is modeled as a product of three one-dimensional Gegenbauer polynomials. The orthogonality of Gegenbauer polynomials and their derivatives on the interval [-1, 1] are exploited to calculate the expansion coefficients. The algorithm was implemented using two well-known Gegenbauer polynomials: Chebyshev polynomials of the first kind and Legendre polynomials. Both implementations of the phase unwrapping algorithm were tested on 3D datasets acquired from a magnetic resonance imaging (MRI) scanner. The first dataset was acquired from a homogeneous spherical phantom. The second dataset was acquired using the same spherical phantom but magnetic field inhomogeneities were introduced by an external coil placed adjacent to the phantom, which provided an additional burden to the phase unwrapping algorithm. Then Gaussian noise was added to generate a low signal-to-noise ratio dataset. The third dataset was acquired from the brain of a human volunteer. The results showed that Chebyshev implementation and the Legendre implementation of the phase unwrapping algorithm give similar results on the 3D datasets. Both implementations of the phase unwrapping algorithm compare well to PRELUDE 3D, 3D phase unwrapping software well recognized for functional MRI.

Dataset on predictive compressive strength model for self-compacting concrete.

PubMed

Ofuyatan, O M; Edeki, S O

2018-04-01

The determination of compressive strength is affected by many variables such as the water cement (WC) ratio, the superplasticizer (SP), the aggregate combination, and the binder combination. In this dataset article, 7, 28, and 90-day compressive strength models are derived using statistical analysis. The response surface methodology is used toinvestigate the effect of the parameters: Varying percentages of ash, cement, WC, and SP on hardened properties-compressive strengthat 7,28 and 90 days. Thelevels of independent parameters are determinedbased on preliminary experiments. The experimental values for compressive strengthat 7, 28 and 90 days and modulus of elasticity underdifferent treatment conditions are also discussed and presented.These dataset can effectively be used for modelling and prediction in concrete production settings.

Description of the National Hydrologic Model for use with the Precipitation-Runoff Modeling System (PRMS)

USGS Publications Warehouse

Regan, R. Steven; Markstrom, Steven L.; Hay, Lauren E.; Viger, Roland J.; Norton, Parker A.; Driscoll, Jessica M.; LaFontaine, Jacob H.

2018-01-08

This report documents several components of the U.S. Geological Survey National Hydrologic Model of the conterminous United States for use with the Precipitation-Runoff Modeling System (PRMS). It provides descriptions of the (1) National Hydrologic Model, (2) Geospatial Fabric for National Hydrologic Modeling, (3) PRMS hydrologic simulation code, (4) parameters and estimation methods used to compute spatially and temporally distributed default values as required by PRMS, (5) National Hydrologic Model Parameter Database, and (6) model extraction tool named Bandit. The National Hydrologic Model Parameter Database contains values for all PRMS parameters used in the National Hydrologic Model. The methods and national datasets used to estimate all the PRMS parameters are described. Some parameter values are derived from characteristics of topography, land cover, soils, geology, and hydrography using traditional Geographic Information System methods. Other parameters are set to long-established default values and computation of initial values. Additionally, methods (statistical, sensitivity, calibration, and algebraic) were developed to compute parameter values on the basis of a variety of nationally-consistent datasets. Values in the National Hydrologic Model Parameter Database can periodically be updated on the basis of new parameter estimation methods and as additional national datasets become available. A companion ScienceBase resource provides a set of static parameter values as well as images of spatially-distributed parameters associated with PRMS states and fluxes for each Hydrologic Response Unit across the conterminuous United States.

Using CT Data to Improve the Quantitative Analysis of 18F-FBB PET Neuroimages

PubMed Central

Segovia, Fermín; Sánchez-Vañó, Raquel; Górriz, Juan M.; Ramírez, Javier; Sopena-Novales, Pablo; Testart Dardel, Nathalie; Rodríguez-Fernández, Antonio; Gómez-Río, Manuel

2018-01-01

18F-FBB PET is a neuroimaging modality that is been increasingly used to assess brain amyloid deposits in potential patients with Alzheimer's disease (AD). In this work, we analyze the usefulness of these data to distinguish between AD and non-AD patients. A dataset with 18F-FBB PET brain images from 94 subjects diagnosed with AD and other disorders was evaluated by means of multiple analyses based on t-test, ANOVA, Fisher Discriminant Analysis and Support Vector Machine (SVM) classification. In addition, we propose to calculate amyloid standardized uptake values (SUVs) using only gray-matter voxels, which can be estimated using Computed Tomography (CT) images. This approach allows assessing potential brain amyloid deposits along with the gray matter loss and takes advantage of the structural information provided by most of the scanners used for PET examination, which allow simultaneous PET and CT data acquisition. The results obtained in this work suggest that SUVs calculated according to the proposed method allow AD and non-AD subjects to be more accurately differentiated than using SUVs calculated with standard approaches. PMID:29930505

A GEOS-Based OSSE for the "MISTiC Winds" Concept

NASA Technical Reports Server (NTRS)

McCarty, W.; Blaisdell, J.; Fuentes, M.; Carvalho, D.; Errico, R.; Gelaro, R.; Kouvaris, L.; Moradi, I.; Pawson, S.; Prive, N.;

Evidence for widespread, severe brain copper deficiency in Alzheimer's dementia.

PubMed

Xu, Jingshu; Church, Stephanie J; Patassini, Stefano; Begley, Paul; Waldvogel, Henry J; Curtis, Maurice A; Faull, Richard L M; Unwin, Richard D; Cooper, Garth J S

2017-08-16

Datasets comprising simultaneous measurements of many essential metals in Alzheimer's disease (AD) brain are sparse, and available studies are not entirely in agreement. To further elucidate this matter, we employed inductively-coupled-plasma mass spectrometry to measure post-mortem levels of 8 essential metals and selenium, in 7 brain regions from 9 cases with AD (neuropathological severity Braak IV-VI), and 13 controls who had normal ante-mortem mental function and no evidence of brain disease. Of the regions studied, three undergo severe neuronal damage in AD (hippocampus, entorhinal cortex and middle-temporal gyrus); three are less-severely affected (sensory cortex, motor cortex and cingulate gyrus); and one (cerebellum) is relatively spared. Metal concentrations in the controls differed among brain regions, and AD-associated perturbations in most metals occurred in only a few: regions more severely affected by neurodegeneration generally showed alterations in more metals, and cerebellum displayed a distinctive pattern. By contrast, copper levels were substantively decreased in all AD-brain regions, to 52.8-70.2% of corresponding control values, consistent with pan-cerebral copper deficiency. This copper deficiency could be pathogenic in AD, since levels are lowered to values approximating those in Menkes' disease, an X-linked recessive disorder where brain-copper deficiency is the accepted cause of severe brain damage. Our study reinforces others reporting deficient brain copper in AD, and indicates that interventions aimed at safely and effectively elevating brain copper could provide a new experimental-therapeutic approach.

EnviroAtlas - Austin, TX - BenMAP Results by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset demonstrates the effect of changes in pollution concentration on local populations in 750 block groups in Austin, Texas. The US EPA's Environmental Benefits Mapping and Analysis Program (BenMAP) was used to estimate the incidence of adverse health effects (i.e., mortality and morbidity) and associated monetary value that result from changes in pollution concentrations for Travis and Williamson Counties, TX. Incidence and value estimates for the block groups are calculated using i-Tree models (www.itreetools.org), local weather data, pollution data, and U.S. Census derived population data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

A reappraisal of instrumental magnetic measurements made in Western Europe before AD 1750: confronting historical geomagnetism and archeomagnetism

NASA Astrophysics Data System (ADS)

Le Goff, Maxime; Gallet, Yves

2017-02-01

We present a new compilation and analysis of historical geomagnetic measurements made in Western Europe before AD 1750. The dataset in its ensemble provides a coherent evolution of magnetic field directions. Several data points excluded from previous analyses actually appear very consistent with most of the present compilation. A new average historical curve is computed for Paris, which is in very good agreement with the archeomagnetic data obtained in France, while significantly differing from the directional curve expected for Paris before AD 1675 based on the gufm1 model (Jackson et al. in Philos Trans R Soc Lond A 358:957-990, 2000). This finding suggests that the older segment of the gufm1 model lacks reliability and should be improved. Similarly, the historical part of the regional geomagnetic field model built for Europe by Pavón-Carrasco et al. (Geochem Geophys Geosyst 10:Q03013, 2009) should be revised because it erroneously incorporates directions derived from the gufm1 model.

A new method to generate a high-resolution global distribution map of lake chlorophyll

USGS Publications Warehouse

Sayers, Michael J; Grimm, Amanda G.; Shuchman, Robert A.; Deines, Andrew M.; Bunnell, David B.; Raymer, Zachary B; Rogers, Mark W.; Woelmer, Whitney; Bennion, David; Brooks, Colin N.; Whitley, Matthew A.; Warner, David M.; Mychek-Londer, Justin G.

2015-01-01

A new method was developed, evaluated, and applied to generate a global dataset of growing-season chlorophyll-a (chl) concentrations in 2011 for freshwater lakes. Chl observations from freshwater lakes are valuable for estimating lake productivity as well as assessing the role that these lakes play in carbon budgets. The standard 4 km NASA OceanColor L3 chlorophyll concentration products generated from MODIS and MERIS sensor data are not sufficiently representative of global chl values because these can only resolve larger lakes, which generally have lower chl concentrations than lakes of smaller surface area. Our new methodology utilizes the 300 m-resolution MERIS full-resolution full-swath (FRS) global dataset as input and does not rely on the land mask used to generate standard NASA products, which masks many lakes that are otherwise resolvable in MERIS imagery. The new method produced chl concentration values for 78,938 and 1,074 lakes in the northern and southern hemispheres, respectively. The mean chl for lakes visible in the MERIS composite was 19.2 ± 19.2, the median was 13.3, and the interquartile range was 3.90–28.6 mg m−3. The accuracy of the MERIS-derived values was assessed by comparison with temporally near-coincident and globally distributed in situmeasurements from the literature (n = 185, RMSE = 9.39, R2 = 0.72). This represents the first global-scale dataset of satellite-derived chl estimates for medium to large lakes.

Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI.

PubMed

Hering, Jan; Wolf, Ivo; Maier-Hein, Klaus H

2016-10-01

Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b -values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in vivo datasets. In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b -values (3000 s ·mm -2 and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general.

Online Visualization and Value Added Services of MERRA-2 Data at GES DISC

NASA Technical Reports Server (NTRS)

Shen, Suhung; Ostrenga, Dana M.; Vollmer, Bruce E.; Hegde, Mahabaleshwa S.; Wei, Jennifer C.; Bosilovich, Michael G.

2017-01-01

NASA climate reanalysis datasets from MERRA-2, distributed at the Goddard Earth Sciences Data and Information Services Center (GES DISC), have been used in broad research areas, such as climate variations, extreme weather, agriculture, renewable energy, and air quality, etc. The datasets contain numerous variables for atmosphere, land, and ocean, grouped into 95 products. The total archived volume is approximately 337 TB ( approximately 562K files) at the end of October 2017. Due to the large number of products and files, and large data volumes, it may be a challenge for a user to find and download the data of interest. The support team at GES DISC, working closely with the MERRA-2 science team, has created and is continuing to work on value added data services to best meet the needs of a broad user community. This presentation, using aerosol over Asia Monsoon as an example, provides an overview of the MERRA-2 data services at GES DISC, including: How to find the data? How many data access methods are provided? What are the best data access methods for me? How do download the subsetted (parameter, spatial, temporal) data and save in preferred spatial resolution and data format? How to visualize and explore the data online? In addition, we introduce a future online analytic tool designed for supporting application research, focusing on long-term hourly time-series data access and analysis.

Correlation between Cognition and Function across the Spectrum of Alzheimer's Disease.

PubMed

Liu-Seifert, H; Siemers, E; Selzler, K; Sundell, K; Aisen, P; Cummings, J; Raskin, J; Mohs, R

2016-01-01

Both cognitive and functional deterioration are characteristic of the clinical progression of Alzheimer's disease (AD). To systematically assess correlations between widely used measures of cognition and function across the spectrum of AD. Spearman rank correlations were calculated for cognitive and functional measures across datasets from various AD patient populations. Post-hoc analysis from existing databases. Pooled data from placebo-treated patients with mild (MMSE score ≥20 and ≤26) and moderate (MMSE score ≥16 and ≤19) AD dementia from two Phase 3 solanezumab (EXPEDITION/2) and two semagecesatat (IDENTITY/2) studies and normal, late mild cognitive impairment (LMCI) and mild AD patients from the Alzheimer's Disease Neuroimaging Initiative 2-Grand Opportunity (ADNI-2/GO). Intervention (if any): Placebo (EXPEDITION/2 and IDENTITY/2 subjects). Cognitive and functional abilities were measured in all datasets. Data were collected at baseline and every three months for 18 months in EXPEDITION and IDENTITY studies; and at baseline, 6, 12, and 24 months in the ADNI dataset. The relationship of cognition and function became stronger over time as AD patients progressed from preclinical to moderate dementia disease stages, with the magnitude of correlations dependent on disease stage and the complexity of functional task. The correlations were minimal in the normal control population, but became stronger with disease progression. This analysis found that measures of cognition and function become more strongly correlated with disease progression from preclinical to moderate dementia across multiple datasets. These findings improve the understanding of the relationship between cognitive and functional clinical measures during the course of AD progression and how cognition and function measures relate to each other in AD clinical trials.

Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering.

PubMed

Nahid, Abdullah-Al; Mehrabi, Mohamad Ali; Kong, Yinan

2018-01-01

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F -Measure value is achieved on both the 40x and 100x datasets.

Pooled solifenacin overactive bladder trial data: Creation, validation and analysis of an integrated database.

PubMed

Chapple, Christopher R; Cardozo, Linda; Snijder, Robert; Siddiqui, Emad; Herschorn, Sender

2016-12-15

Patient-level data are available for 11 randomized, controlled, Phase III/Phase IV solifenacin clinical trials. Meta-analyses were conducted to interrogate the data, to broaden knowledge about solifenacin and overactive bladder (OAB) in general. Before integrating data, datasets from individual studies were mapped to a single format using methodology developed by the Clinical Data Interchange Standards Consortium (CDISC). Initially, the data structure was harmonized, to ensure identical categorization, using the CDISC Study Data Tabulation Model (SDTM). To allow for patient level meta-analysis, data were integrated and mapped to analysis datasets. Mapping included adding derived and categorical variables and followed standards described as the Analysis Data Model (ADaM). Mapping to both SDTM and ADaM was performed twice by two independent programming teams, results compared, and inconsistencies corrected in the final output. ADaM analysis sets included assignments of patients to the Safety Analysis Set and the Full Analysis Set. There were three analysis groupings: Analysis group 1 (placebo-controlled, monotherapy, fixed-dose studies, n = 3011); Analysis group 2 (placebo-controlled, monotherapy, pooled, fixed- and flexible-dose, n = 5379); Analysis group 3 (all solifenacin monotherapy-treated patients, n = 6539). Treatment groups were: solifenacin 5 mg fixed dose, solifenacin 5/10 mg flexible dose, solifenacin 10 mg fixed dose and overall solifenacin. Patient were similar enough for data pooling to be acceptable. Creating ADaM datasets provided significant information about individual studies and the derivation decisions made in each study; validated ADaM datasets now exist for medical history, efficacy and AEs. Results from these meta-analyses were similar over time.

a Free and Open Source Tool to Assess the Accuracy of Land Cover Maps: Implementation and Application to Lombardy Region (italy)

NASA Astrophysics Data System (ADS)

Bratic, G.; Brovelli, M. A.; Molinari, M. E.

2018-04-01

The availability of thematic maps has significantly increased over the last few years. Validation of these maps is a key factor in assessing their suitability for different applications. The evaluation of the accuracy of classified data is carried out through a comparison with a reference dataset and the generation of a confusion matrix from which many quality indexes can be derived. In this work, an ad hoc free and open source Python tool was implemented to automatically compute all the matrix confusion-derived accuracy indexes proposed by literature. The tool was integrated into GRASS GIS environment and successfully applied to evaluate the quality of three high-resolution global datasets (GlobeLand30, Global Urban Footprint, Global Human Settlement Layer Built-Up Grid) in the Lombardy Region area (Italy). In addition to the most commonly used accuracy measures, e.g. overall accuracy and Kappa, the tool allowed to compute and investigate less known indexes such as the Ground Truth and the Classification Success Index. The promising tool will be further extended with spatial autocorrelation analysis functions and made available to researcher and user community.

On the Discrepancy in Simultaneous Observations of the Structure Parameter of Temperature Using Scintillometers and Unmanned Aircraft

NASA Astrophysics Data System (ADS)

Braam, Miranda; Beyrich, Frank; Bange, Jens; Platis, Andreas; Martin, Sabrina; Maronga, Björn; Moene, Arnold F.

2016-02-01

We elaborate on the preliminary results presented in Beyrich et al. (in Boundary-Layer Meteorol 144:83-112, 2012), who compared the structure parameter of temperature ({CT^2}_{}) obtained with the unmanned meteorological mini aerial vehicle (M2 AV) versus {CT^2}_{} obtained with two large-aperture scintillometers (LASs) for a limited dataset from one single experiment (LITFASS-2009). They found that {CT^2}_{} obtained from the M2 AV data is significantly larger than that obtained from the LAS data. We investigate if similar differences can be found for the flights on the other six days during LITFASS-2009 and LITFASS-2010, and whether these differences can be reduced or explained through a more elaborate processing of both the LAS data and the M2 AV data. This processing includes different corrections and measures to reduce the differences between the spatial and temporal averaging of the datasets. We conclude that the differences reported in Beyrich et al. can be found for other days as well. For the LAS-derived values the additional processing steps that have the largest effect are the saturation correction and the humidity correction. For the M2 AV -derived values the most important step is the application of the scintillometer path-weighting function. Using the true air speed of the M2 AV to convert from a temporal to a spatial structure function rather than the ground speed (as in Beyrich et al.) does not change the mean discrepancy, but it does affect {CT^2}_{} values for individual flights. To investigate whether {CT^2}_{} derived from the M2 AV data depends on the fact that the underlying temperature dataset combines spatial and temporal sampling, we used large-eddy simulation data to analyze {CT^2}_{} from virtual flights with different mean ground speeds. This analysis shows that {CT^2}_{} does only slightly depends on the true air speed when averaged over many flights.

Glacier-specific elevation changes in western Alaska

NASA Astrophysics Data System (ADS)

Paul, Frank; Le Bris, Raymond

2013-04-01

Deriving glacier-specific elevation changes from DEM differencing and digital glacier outlines is rather straight-forward if the required datasets are available. Calculating such changes over large regions and including glaciers selected for mass balance measurements in the field, provides a possibility to determine the representativeness of the changes observed at these glaciers for the entire region. The related comparison of DEM-derived values for these glaciers with the overall mean avoids the rather error-prone conversion of volume to mass changes (e.g. due to unknown densities) and gives unit-less correction factors for upscaling the field measurements to a larger region. However, several issues have to be carefully considered, such as proper co-registration of the two DEMs, date and accuracy of the datasets compared, as well as source data used for DEM creation and potential artefacts (e.g. voids). In this contribution we present an assessment of the representativeness of the two mass balance glaciers Gulkana and Wolverine for the overall changes of nearly 3200 glaciers in western Alaska over a ca. 50-year time period. We use an elevation change dataset from a study by Berthier et al. (2010) that was derived from the USGS DEM of the 1960s (NED) and a more recent DEM derived from SPOT5 data for the SPIRIT project. Additionally, the ASTER GDEM was used as a more recent DEM. Historic glacier outlines were taken from the USGS digital line graph (DLG) dataset, corrected with the digital raster graph (DRG) maps from USGS. Mean glacier specific elevation changes were derived based on drainage divides from a recently created inventory. Land-terminating, lake-calving and tidewater glaciers were marked in the attribute table to determine their changes separately. We also investigated the impact of handling potential DEM artifacts in three different ways and compared elevation changes with altitude. The mean elevation changes of Gulkana and Wolverine glaciers (about -0.65 m / year) are very similar to the mean of the lake-calving and tidewater glaciers (about -0.6 m / year), but much more negative than for the land-terminating glaciers (about -0.24 m / year). The two mass balance glaciers are thus well representative for the entire region, but not for their own class. The different ways of considering positive elevation changes (e.g. setting them to zero or no data) influence the total values, but has otherwise little impact on the results (e.g. the correction factors are similar). The massive elevation loss of Columbia Glacier (-2.8 m / year) is exceptional and strongly influences the statistics when area-weighting is used to determine the regional mean. For the entire region this method yields more negative values for land-terminating and tidewater glaciers than the arithmetically averaged values, but for the lake-calving glaciers both are about the same.

Attributes for NHDPlus catchments (version 1.1) for the conterminous United States: normalized atmospheric deposition for 2002, Total Inorganic Nitrogen

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the average normalized atmospheric (wet) deposition, in kilograms, of Total Inorganic Nitrogen for the year 2002 compiled for every catchment of NHDPlus for the conterminous United States. Estimates of Total Inorganic Nitrogen deposition are based on National Atmospheric Deposition Program (NADP) measurements (B. Larsen, U.S. Geological Survey, written commun., 2007). De-trending methods applied to the year 2002 are described in Alexander and others, 2001. NADP site selection met the following criteria: stations must have records from 1995 to 2002 and have a minimum of 30 observations. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Normalized Atmospheric Deposition for 2002, Ammonium (NH4)

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the average normalized atmospheric (wet) deposition, in kilograms, of Ammonium (NH4) for the year 2002 compiled for every catchment of NHDPlus for the conterminous United States. Estimates of NH4 deposition are based on National Atmospheric Deposition Program (NADP) measurements (B. Larsen, U.S. Geological Survey, written commun., 2007). De-trending methods applied to the year 2002 are described in Alexander and others, 2001. NADP site selection met the following criteria: stations must have records from 1995 to 2002 and have a minimum of 30 observations. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Normalized Atmospheric Deposition for 2002, Nitrate (NO3)

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the average normalized atmospheric (wet) deposition, in kilograms, of Nitrate (NO3) for the year 2002 compiled for every catchment of NHDPlus for the conterminous United States. Estimates of NO3 deposition are based on National Atmospheric Deposition Program (NADP) measurements (B. Larsen, U.S. Geological Survey, written commun., 2007). De-trending methods applied to the year 2002 are described in Alexander and others, 2001. NADP site selection met the following criteria: stations must have records from 1995 to 2002 and have a minimum of 30 observations. The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Adsorption of glyphosate on variable-charge, volcanic ash-derived soils.

PubMed

Cáceres-Jensen, L; Gan, J; Báez, M; Fuentes, R; Escudey, M

2009-01-01

Glyphosate (N-phosphonometylglycine) is widely used due to its broad spectrum of activity and nonselective mode of action. In Chile it is the most used herbicide, but its adsorption behavior in the abundant and widespread variable charge soils is not well understood. In this study, three volcanic ash-derived soils were selected, including Andisols (Nueva Braunau and Diguillin) and Ultisols (Collipulli), to evaluate the adsorption kinetics, equilibrium isotherms, and the effect of pH in glyphosate adsorption. The influence of glyphosate on soil phosphorus retention was also studied. Glyphosate was rapidly and strongly adsorbed on the selected soils, and adsorption isotherms were well described by the Freundlich relationship with strong nonlinearity (n(fads) < 0.5). The n(fads) values were consistently higher than n(fdes) values, suggesting strong hysteresis. Adsorption (K(ads)) increased strongly when pH decreased. The presence of glyphosate (3200 mug mL(-1)) changed the adsorption behavior of phosphate at its maximum adsorption capacity. Andisol soils without the addition of glyphosate had similar mean K(ads) values for Nueva Braunau (5.68) and Diguillin (7.38). Collipulli had a mean K(ads) value of 31.58. During the successive desorption steps, glyphosate at the highest level increased K(ads) values for phosphate in the Andisol soils but had little effect in the Ultisol soil. This different behavior was probably due to the irreversible occupation of some adsorption sites by glyphosate in the Ultisol soil attributed to the dominant Kaolinite mineral. Results from this study suggest that in the two types of volcanic soils, different mechanisms are involved in glyphosate and phosphate adsorption and that long-term use of glyphosate may impose different effects on the retention and availability of phosphorus. Volcanic ash-derived soils have a particular environmental behavior in relation to the retention of organic contaminants, representing an environmental substrate that may become highly polluted over time due to intensive agronomic uses.

Global QSAR modeling of logP values of phenethylamines acting as adrenergic alpha-1 receptor agonists.

PubMed

Yadav, Mukesh; Joshi, Shobha; Nayarisseri, Anuraj; Jain, Anuja; Hussain, Aabid; Dubey, Tushar

2013-06-01

Global QSAR models predict biological response of molecular structures which are generic in particular class. A global QSAR dataset admits structural features derived from larger chemical space, intricate to model but more applicable in medicinal chemistry. The present work is global in either sense of structural diversity in QSAR dataset or large number of descriptor input. Forty phenethylamine structure derivatives were selected from a large pool (904) of similar phenethylamines available in Pubchem database. LogP values of selected candidates were collected from physical properties database (PHYSPROP) determined in identical set of conditions. Attempts to model logP value have produced significant QSAR models. MLR aided linear one-variable and two-variable QSAR models with their respective R(2) (0.866, 0.937), R(2)A (0.862, 0.932), F-stat (181.936, 199.812) and Standard Error (0.365, 0.255) are statistically fit and found predictive after internal validation and external validation. The descriptors chosen after improvisation and optimization reveal mechanistic part of work in terms of Verhaar model of Fish base-line toxicity from MLOGP, i.e. (BLTF96) and 3D-MoRSE -signal 15 /unweighted molecular descriptor calculated by summing atom weights viewed by a different angular scattering function (Mor15u) are crucial in regulation of logP values of phenethylamines.

Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks

PubMed Central

Hwang, Seong Jae; Adluru, Nagesh; Collins, Maxwell D.; Ravi, Sathya N.; Bendlin, Barbara B.; Johnson, Sterling C.; Singh, Vikas

2016-01-01

There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function. To do so, one typically performs so-called tractography procedures on diffusion MR brain images and derives measures of brain connectivity expressed as graphs. The nodes correspond to distinct brain regions and the edges encode the strength of the connection. The scientific interest is in characterizing the evolution of these graphs over time or from healthy individuals to diseased. We pose this important question in terms of the Laplacian of the connectivity graphs derived from various longitudinal or disease time points — quantifying its progression is then expressed in terms of coupling the harmonic bases of a full set of Laplacians. We derive a coupled system of generalized eigenvalue problems (and corresponding numerical optimization schemes) whose solution helps characterize the full life cycle of brain connectivity evolution in a given dataset. Finally, we show a set of results on a diffusion MR imaging dataset of middle aged people at risk for Alzheimer’s disease (AD), who are cognitively healthy. In such asymptomatic adults, we find that a framework for characterizing brain connectivity evolution provides the ability to predict cognitive scores for individual subjects, and for estimating the progression of participant’s brain connectivity into the future. PMID:27812274

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort.

PubMed

St John-Williams, Lisa; Blach, Colette; Toledo, Jon B; Rotroff, Daniel M; Kim, Sungeun; Klavins, Kristaps; Baillie, Rebecca; Han, Xianlin; Mahmoudiandehkordi, Siamak; Jack, John; Massaro, Tyler J; Lucas, Joseph E; Louie, Gregory; Motsinger-Reif, Alison A; Risacher, Shannon L; Saykin, Andrew J; Kastenmüller, Gabi; Arnold, Matthias; Koal, Therese; Moseley, M Arthur; Mangravite, Lara M; Peters, Mette A; Tenenbaum, Jessica D; Thompson, J Will; Kaddurah-Daouk, Rima

2017-10-17

Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort

PubMed Central

St John-Williams, Lisa; Blach, Colette; Toledo, Jon B.; Rotroff, Daniel M.; Kim, Sungeun; Klavins, Kristaps; Baillie, Rebecca; Han, Xianlin; Mahmoudiandehkordi, Siamak; Jack, John; Massaro, Tyler J.; Lucas, Joseph E.; Louie, Gregory; Motsinger-Reif, Alison A.; Risacher, Shannon L.; Saykin, Andrew J.; Kastenmüller, Gabi; Arnold, Matthias; Koal, Therese; Moseley, M. Arthur; Mangravite, Lara M.; Peters, Mette A.; Tenenbaum, Jessica D.; Thompson, J. Will; Kaddurah-Daouk, Rima

2017-01-01

Alzheimer’s disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes. PMID:29039849

Synthesis, spectroscopic analysis and theoretical study of new pyrrole-isoxazoline derivatives

NASA Astrophysics Data System (ADS)

Rawat, Poonam; Singh, R. N.; Baboo, Vikas; Niranjan, Priydarshni; Rani, Himanshu; Saxena, Rajat; Ahmad, Sartaj

2017-02-01

In the present work, we have efficiently synthesized the pyrrole-isoxazoline derivatives (4a-d) by cyclization of substituted 4-chalconylpyrrole (3a-d) with hydroxylamine hydrochloride. The reactivity of substituted 4-chalconylpyrrole (3a-d), towards nucleophiles hydroxylamine hydrochloride was evaluated on the basis of electrophilic reactivity descriptors (fk+, sk+, ωk+) and they were found to be high at unsaturated β carbon of chalconylpyrrole indicating its more proneness to nucleophilic attack and thereby favoring the formation of reported new pyrrole-isoxazoline compounds (4a-d). The structures of newly synthesized pyrrole-isoxazoline derivatives were derived from IR, 1H NMR, Mass, UV-Vis and elemental analysis. All experimental spectral data corroborate well with the calculated spectral data. The FT-IR analysis shows red shifts in vN-H and vC = O stretching due to dimer formation through intermolecular hydrogen bonding. On basis set superposition error correction, the intermolecular interaction energy for (4a-d) is found to be 10.10, 9.99, 10.18, 11.01 and 11.19 kcal/mol respectively. The calculated first hyperpolarizability (β0) values of (4a-d) molecules are in the range of 7.40-9.05 × 10-30 esu indicating their suitability for non-linear optical (NLO) applications. Experimental spectral results, theoretical data, analysis of chalcone intermediates and pyrrole-isoxazolines find usefulness in advancement of pyrrole-azole chemistry.

A Statistical Selection Strategy for Normalization Procedures in LC-MS Proteomics Experiments through Dataset Dependent Ranking of Normalization Scaling Factors

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

Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Jacobs, Jon M.

2011-12-01

Quantification of LC-MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run-to-run of the instrument due to both technical and biological variation. Thus, normalization of peak intensities across a LC-MS proteomics dataset is a fundamental step in pre-processing. However, the downstream analysis of LC-MS proteomics data can be dramatically affected by the normalization method selected . Current normalization procedures for LC-MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If theymore » are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, which will affect downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, where a normalization strategy includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between-group variance structure in order to identify candidate normalization strategies that improve the structure of the data without introducing bias into the normalized peak intensities.« less

Large Dataset of Acute Oral Toxicity Data Created for Testing ...

EPA Pesticide Factsheets

Acute toxicity data is a common requirement for substance registration in the US. Currently only data derived from animal tests are accepted by regulatory agencies, and the standard in vivo tests use lethality as the endpoint. Non-animal alternatives such as in silico models are being developed due to animal welfare and resource considerations. We compiled a large dataset of oral rat LD50 values to assess the predictive performance currently available in silico models. Our dataset combines LD50 values from five different sources: literature data provided by The Dow Chemical Company, REACH data from eChemportal, HSDB (Hazardous Substances Data Bank), RTECS data from Leadscope, and the training set underpinning TEST (Toxicity Estimation Software Tool). Combined these data sources yield 33848 chemical-LD50 pairs (data points), with 23475 unique data points covering 16439 compounds. The entire dataset was loaded into a chemical properties database. All of the compounds were registered in DSSTox and 59.5% have publically available structures. Compounds without a structure in DSSTox are currently having their structures registered. The structural data will be used to evaluate the predictive performance and applicable chemical domains of three QSAR models (TIMES, PROTOX, and TEST). Future work will combine the dataset with information from ToxCast assays, and using random forest modeling, assess whether ToxCast assays are useful in predicting acute oral toxicity. Pre

Spectral relative standard deviation: a practical benchmark in metabolomics.

PubMed

Parsons, Helen M; Ekman, Drew R; Collette, Timothy W; Viant, Mark R

2009-03-01

Metabolomics datasets, by definition, comprise of measurements of large numbers of metabolites. Both technical (analytical) and biological factors will induce variation within these measurements that is not consistent across all metabolites. Consequently, criteria are required to assess the reproducibility of metabolomics datasets that are derived from all the detected metabolites. Here we calculate spectrum-wide relative standard deviations (RSDs; also termed coefficient of variation, CV) for ten metabolomics datasets, spanning a variety of sample types from mammals, fish, invertebrates and a cell line, and display them succinctly as boxplots. We demonstrate multiple applications of spectral RSDs for characterising technical as well as inter-individual biological variation: for optimising metabolite extractions, comparing analytical techniques, investigating matrix effects, and comparing biofluids and tissue extracts from single and multiple species for optimising experimental design. Technical variation within metabolomics datasets, recorded using one- and two-dimensional NMR and mass spectrometry, ranges from 1.6 to 20.6% (reported as the median spectral RSD). Inter-individual biological variation is typically larger, ranging from as low as 7.2% for tissue extracts from laboratory-housed rats to 58.4% for fish plasma. In addition, for some of the datasets we confirm that the spectral RSD values are largely invariant across different spectral processing methods, such as baseline correction, normalisation and binning resolution. In conclusion, we propose spectral RSDs and their median values contained herein as practical benchmarks for metabolomics studies.

Isolation of levoglucosan from pyrolysis oil derived from cellulose

DOEpatents

Moens, Luc

1994-01-01

High purity levoglucosan is obtained from pyrolysis oil derived from cellulose by: mixing pyrolysis oil with water and a basic metal hydroxide, oxide, or salt in amount sufficient to elevate pH values to a range of from about 12 to about 12.5, and adding an amount of the hydroxide, oxide, or salt in excess of the amount needed to obtain the pH range until colored materials of impurities from the oil are removed and a slurry is formed; drying the slurry azeotropically with methyl isobutyl ketone solvent to form a residue, and further drying the residue by evaporation; reducing the residue into a powder; continuously extracting the powder residue with ethyl acetate to provide a levoglucosan-rich extract; and concentrating the extract by removing ethyl acetate to provide crystalline levoglucosan. Preferably, Ca(OH).sub.2 is added to adjust the pH to the elevated values, and then Ca(OH).sub.2 is added in an excess amount needed.

Isolation of levoglucosan from pyrolysis oil derived from cellulose

DOEpatents

Moens, L.

1994-12-06

High purity levoglucosan is obtained from pyrolysis oil derived from cellulose by: mixing pyrolysis oil with water and a basic metal hydroxide, oxide, or salt in amount sufficient to elevate pH values to a range of from about 12 to about 12.5, and adding an amount of the hydroxide, oxide, or salt in excess of the amount needed to obtain the pH range until colored materials of impurities from the oil are removed and a slurry is formed; drying the slurry azeotropically with methyl isobutyl ketone solvent to form a residue, and further drying the residue by evaporation; reducing the residue into a powder; continuously extracting the powder residue with ethyl acetate to provide a levoglucosan-rich extract; and concentrating the extract by removing ethyl acetate to provide crystalline levoglucosan. Preferably, Ca(OH)[sub 2] is added to adjust the pH to the elevated values, and then Ca(OH)[sub 2] is added in an excess amount needed. 3 figures.

Declining functional connectivity and changing hub locations in Alzheimer's disease: an EEG study.

PubMed

Engels, Marjolein M A; Stam, Cornelis J; van der Flier, Wiesje M; Scheltens, Philip; de Waal, Hanneke; van Straaten, Elisabeth C W

2015-08-20

EEG studies have shown that patients with Alzheimer's disease (AD) have weaker functional connectivity than controls, especially in higher frequency bands. Furthermore, active regions seem more prone to AD pathology. How functional connectivity is affected in AD subgroups of disease severity and how network hubs (highly connected brain areas) change is not known. We compared AD patients with different disease severity and controls in terms of functional connections, hub strength and hub location. We studied routine 21-channel resting-state electroencephalography (EEG) of 318 AD patients (divided into tertiles based on disease severity: mild, moderate and severe AD) and 133 age-matched controls. Functional connectivity between EEG channels was estimated with the Phase Lag Index (PLI). From the PLI-based connectivity matrix, the minimum spanning tree (MST) was derived. For each node (EEG channel) in the MST, the betweenness centrality (BC) was computed, a measure to quantify the relative importance of a node within the network. Then we derived color-coded head plots based on BC values and calculated the center of mass (the exact middle had x and y values of 0). A shifting of the hub locations was defined as a shift of the center of mass on the y-axis across groups. Multivariate general linear models with PLI or BC values as dependent variables and the groups as continuous variables were used in the five conventional frequency bands. We found that functional connectivity decreases with increasing disease severity in the alpha band. All, except for posterior, regions showed increasing BC values with increasing disease severity. The center of mass shifted from posterior to more anterior regions with increasing disease severity in the higher frequency bands, indicating a loss of relative functional importance of the posterior brain regions. In conclusion, we observed decreasing functional connectivity in the posterior regions, together with a shifted hub location from posterior to central regions with increasing AD severity. Relative hub strength decreases in posterior regions while other regions show a relative rise with increasing AD severity, which is in accordance with the activity-dependent degeneration theory. Our results indicate that hubs are disproportionally affected in AD.

Georeferencing UAS Derivatives Through Point Cloud Registration with Archived Lidar Datasets

NASA Astrophysics Data System (ADS)

Magtalas, M. S. L. Y.; Aves, J. C. L.; Blanco, A. C.

2016-10-01

Georeferencing gathered images is a common step before performing spatial analysis and other processes on acquired datasets using unmanned aerial systems (UAS). Methods of applying spatial information to aerial images or their derivatives is through onboard GPS (Global Positioning Systems) geotagging, or through tying of models through GCPs (Ground Control Points) acquired in the field. Currently, UAS (Unmanned Aerial System) derivatives are limited to meter-levels of accuracy when their generation is unaided with points of known position on the ground. The use of ground control points established using survey-grade GPS or GNSS receivers can greatly reduce model errors to centimeter levels. However, this comes with additional costs not only with instrument acquisition and survey operations, but also in actual time spent in the field. This study uses a workflow for cloud-based post-processing of UAS data in combination with already existing LiDAR data. The georeferencing of the UAV point cloud is executed using the Iterative Closest Point algorithm (ICP). It is applied through the open-source CloudCompare software (Girardeau-Montaut, 2006) on a `skeleton point cloud'. This skeleton point cloud consists of manually extracted features consistent on both LiDAR and UAV data. For this cloud, roads and buildings with minimal deviations given their differing dates of acquisition are considered consistent. Transformation parameters are computed for the skeleton cloud which could then be applied to the whole UAS dataset. In addition, a separate cloud consisting of non-vegetation features automatically derived using CANUPO classification algorithm (Brodu and Lague, 2012) was used to generate a separate set of parameters. Ground survey is done to validate the transformed cloud. An RMSE value of around 16 centimeters was found when comparing validation data to the models georeferenced using the CANUPO cloud and the manual skeleton cloud. Cloud-to-cloud distance computations of CANUPO and manual skeleton clouds were obtained with values for both equal to around 0.67 meters at 1.73 standard deviation.

Characterizing the microcirculation of atopic dermatitis using angiographic optical coherence tomography

NASA Astrophysics Data System (ADS)

Byers, R. A.; Maiti, R.; Danby, S. G.; Pang, E. J.; Mitchell, B.; Carré, M. J.; Lewis, R.; Cork, M. J.; Matcher, S. J.

2017-02-01

Background and Aim: With inflammatory skin conditions such as atopic dermatitis (AD), epidermal thickness is mediated by both pathological hyperplasia and atrophy such as that resulting from corticosteroid treatment. Such changes are likely to influence the depth and shape of the underlying microcirculation. Optical coherence tomography (OCT) provides a non-invasive view into the tissue, however structural measures of epidermal thickness are made challenging due to the lack of a delineated dermal-epidermal junction in AD patients. Instead, angiographic extensions to OCT may allow for direct measurement of vascular depth, potentially presenting a more robust method of estimating the degree of epidermal thickening. Methods and results: To investigate microcirculatory changes within AD patients, volumes of angiographic OCT data were collected from 5 healthy volunteers and compared to that of 5 AD patients. Test sites included the cubital and popliteal fossa, which are commonly affected by AD. Measurements of the capillary loop and superficial arteriolar plexus (SAP) depth were acquired and used to estimate the lower and upper bounds of the undulating basement membrane of the dermal-epidermal junction. Furthermore, quantitative parameters such as vessel density and diameter were derived from each dataset and compared between groups. Capillary loop depth increased slightly for AD patients at the poplitial fossa and SAP was found to be measurably deeper in AD patients at both sites, likely due to localized epidermal hyperplasia.

Filling the voids in the SRTM elevation model — A TIN-based delta surface approach

NASA Astrophysics Data System (ADS)

Luedeling, Eike; Siebert, Stefan; Buerkert, Andreas

The Digital Elevation Model (DEM) derived from NASA's Shuttle Radar Topography Mission is the most accurate near-global elevation model that is publicly available. However, it contains many data voids, mostly in mountainous terrain. This problem is particularly severe in the rugged Oman Mountains. This study presents a method to fill these voids using a fill surface derived from Russian military maps. For this we developed a new method, which is based on Triangular Irregular Networks (TINs). For each void, we extracted points around the edge of the void from the SRTM DEM and the fill surface. TINs were calculated from these points and converted to a base surface for each dataset. The fill base surface was subtracted from the fill surface, and the result added to the SRTM base surface. The fill surface could then seamlessly be merged with the SRTM DEM. For validation, we compared the resulting DEM to the original SRTM surface, to the fill DEM and to a surface calculated by the International Center for Tropical Agriculture (CIAT) from the SRTM data. We calculated the differences between measured GPS positions and the respective surfaces for 187,500 points throughout the mountain range (ΔGPS). Comparison of the means and standard deviations of these values showed that for the void areas, the fill surface was most accurate, with a standard deviation of the ΔGPS from the mean ΔGPS of 69 m, and only little accuracy was lost by merging it to the SRTM surface (standard deviation of 76 m). The CIAT model was much less accurate in these areas (standard deviation of 128 m). The results show that our method is capable of transferring the relative vertical accuracy of a fill surface to the void areas in the SRTM model, without introducing uncertainties about the absolute elevation of the fill surface. It is well suited for datasets with varying altitude biases, which is a common problem of older topographic information.

Development of an organ-specific insert phantom generated using a 3D printer for investigations of cardiac computed tomography protocols.

PubMed

Abdullah, Kamarul A; McEntee, Mark F; Reed, Warren; Kench, Peter L

2018-04-30

An ideal organ-specific insert phantom should be able to simulate the anatomical features with appropriate appearances in the resultant computed tomography (CT) images. This study investigated a 3D printing technology to develop a novel and cost-effective cardiac insert phantom derived from volumetric CT image datasets of anthropomorphic chest phantom. Cardiac insert volumes were segmented from CT image datasets, derived from an anthropomorphic chest phantom of Lungman N-01 (Kyoto Kagaku, Japan). These segmented datasets were converted to a virtual 3D-isosurface of heart-shaped shell, while two other removable inserts were included using computer-aided design (CAD) software program. This newly designed cardiac insert phantom was later printed by using a fused deposition modelling (FDM) process via a Creatbot DM Plus 3D printer. Then, several selected filling materials, such as contrast media, oil, water and jelly, were loaded into designated spaces in the 3D-printed phantom. The 3D-printed cardiac insert phantom was positioned within the anthropomorphic chest phantom and 30 repeated CT acquisitions performed using a multi-detector scanner at 120-kVp tube potential. Attenuation (Hounsfield Unit, HU) values were measured and compared to the image datasets of real-patient and Catphan ® 500 phantom. The output of the 3D-printed cardiac insert phantom was a solid acrylic plastic material, which was strong, light in weight and cost-effective. HU values of the filling materials were comparable to the image datasets of real-patient and Catphan ® 500 phantom. A novel and cost-effective cardiac insert phantom for anthropomorphic chest phantom was developed using volumetric CT image datasets with a 3D printer. Hence, this suggested the printing methodology could be applied to generate other phantoms for CT imaging studies. © 2018 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.

A Comparison of Growth Percentile and Value-Added Models of Teacher Performance. Working Paper #39

ERIC Educational Resources Information Center

Guarino, Cassandra M.; Reckase, Mark D.; Stacy, Brian W.; Wooldridge, Jeffrey M.

2014-01-01

School districts and state departments of education frequently must choose between a variety of methods to estimating teacher quality. This paper examines under what circumstances the decision between estimators of teacher quality is important. We examine estimates derived from student growth percentile measures and estimates derived from commonly…

Biorefining of by-product streams from sunflower-based biodiesel production plants for integrated synthesis of microbial oil and value-added co-products.

PubMed

Leiva-Candia, D E; Tsakona, S; Kopsahelis, N; García, I L; Papanikolaou, S; Dorado, M P; Koutinas, A A

2015-08-01

This study focuses on the valorisation of crude glycerol and sunflower meal (SFM) from conventional biodiesel production plants for the separation of value-added co-products (antioxidant-rich extracts and protein isolate) and for enhancing biodiesel production through microbial oil synthesis. Microbial oil production was evaluated using three oleaginous yeast strains (Rhodosporidium toruloides, Lipomyces starkeyi and Cryptococcus curvatus) cultivated on crude glycerol and nutrient-rich hydrolysates derived from either whole SFM or SFM fractions that remained after separation of value-added co-products. Fed-batch bioreactor cultures with R. toruloides led to the production of 37.4gL(-1) of total dry weight with a microbial oil content of 51.3% (ww(-1)) when a biorefinery concept based on SFM fractionation was employed. The estimated biodiesel properties conformed with the limits set by the EN 14214 and ASTM D 6751 standards. The estimated cold filter plugging point (7.3-8.6°C) of the lipids produced by R. toruloides is closer to that of biodiesel derived from palm oil. Copyright © 2015 Elsevier Ltd. All rights reserved.

Derived crop management data for the LandCarbon Project

USGS Publications Warehouse

Schmidt, Gail; Liu, Shu-Guang; Oeding, Jennifer

2011-01-01

The LandCarbon project is assessing potential carbon pools and greenhouse gas fluxes under various scenarios and land management regimes to provide information to support the formulation of policies governing climate change mitigation, adaptation and land management strategies. The project is unique in that spatially explicit maps of annual land cover and land-use change are created at the 250-meter pixel resolution. The project uses vast amounts of data as input to the models, including satellite, climate, land cover, soil, and land management data. Management data have been obtained from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and USDA Economic Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and cover crop (U.S. Department of Agriculture, 2011a, b, c). The LandCarbon team queried the USDA databases to pull historic crop-related management data relative to the needs of the project. The data obtained was in table form with the County or State Federal Information Processing Standard (FIPS) and the year as the primary and secondary keys. Future projections were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) scenarios using the historic data values along with coefficients generated by the project. The PBL Netherlands Environmental Assessment Agency (PBL) Integrated Model to Assess the Global Environment (IMAGE) modeling framework (Integrated Model to Assess the Global Environment, 2006) was used to develop coefficients for each IPCC SRES scenario, which were applied to the historic management data to produce future land management practice projections. The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, manure, fertilizer, tillage, and cover crop products are used as input to the LandCarbon models to represent the historic and the future scenario management data. The overall algorithm to generate each of the gridded management products is based on the land cover and the derived crop type. For each year in the land cover dataset, the algorithm loops through each 250-meter pixel in the ecoregion. If the current pixel in the land cover dataset is an agriculture pixel, then the crop type is determined. Once the crop type is derived, then the crop harvest, manure, fertilizer, tillage, and cover crop values are derived independently for that crop type. The following is the overall algorithm used for the set of derived grids. The specific algorithm to generate each management dataset is discussed in the respective section for that dataset, along with special data handling and a description of the output product.

Cloud Properties and Radiative Heating Rates for TWP

DOE Data Explorer

Comstock, Jennifer

2013-11-07

A cloud properties and radiative heating rates dataset is presented where cloud properties retrieved using lidar and radar observations are input into a radiative transfer model to compute radiative fluxes and heating rates at three ARM sites located in the Tropical Western Pacific (TWP) region. The cloud properties retrieval is a conditional retrieval that applies various retrieval techniques depending on the available data, that is if lidar, radar or both instruments detect cloud. This Combined Remote Sensor Retrieval Algorithm (CombRet) produces vertical profiles of liquid or ice water content (LWC or IWC), droplet effective radius (re), ice crystal generalized effective size (Dge), cloud phase, and cloud boundaries. The algorithm was compared with 3 other independent algorithms to help estimate the uncertainty in the cloud properties, fluxes, and heating rates (Comstock et al. 2013). The dataset is provided at 2 min temporal and 90 m vertical resolution. The current dataset is applied to time periods when the MMCR (Millimeter Cloud Radar) version of the ARSCL (Active Remotely-Sensed Cloud Locations) Value Added Product (VAP) is available. The MERGESONDE VAP is utilized where temperature and humidity profiles are required. Future additions to this dataset will utilize the new KAZR instrument and its associated VAPs.

Adding Albedo and Atmospheres

ERIC Educational Resources Information Center

LoPresto, Michael C.

2013-01-01

In a previous article in this journal, we reported on a laboratory activity in which students used a derivation from the Stefan-Boltzmann law to calculate planetary temperatures and compare them to measured values from various (mostly online) sources. The calculated temperatures matched observed values very well with the exceptions of Venus and…

Novel 2-pheynlbenzofuran derivatives as selective butyrylcholinesterase inhibitors for Alzheimer's disease.

PubMed

Kumar, Amit; Pintus, Francesca; Di Petrillo, Amalia; Medda, Rosaria; Caria, Paola; Matos, Maria João; Viña, Dolores; Pieroni, Enrico; Delogu, Francesco; Era, Benedetta; Delogu, Giovanna L; Fais, Antonella

2018-03-13

Alzheimer's disease (AD) is a neurodegenerative disorder representing the leading cause of dementia and is affecting nearly 44 million people worldwide. AD is characterized by a progressive decline in acetylcholine levels in the cholinergic systems, which results in severe memory loss and cognitive impairments. Expression levels and activity of butyrylcholinesterase (BChE) enzyme has been noted to increase significantly in the late stages of AD, thus making it a viable drug target. A series of hydroxylated 2-phenylbenzofurans compounds were designed, synthesized and their inhibitory activities toward acetylcholinesterase (AChE) and BChE enzymes were evaluated. Two compounds (15 and 17) displayed higher inhibitory activity towards BChE with IC 50 values of 6.23 μM and 3.57 μM, and a good antioxidant activity with EC 50 values 14.9 μM and 16.7 μM, respectively. The same compounds further exhibited selective inhibitory activity against BChE over AChE. Computational studies were used to compare protein-binding pockets and evaluate the interaction fingerprints of the compound. Molecular simulations showed a conserved protein residue interaction network between the compounds, resulting in similar interaction energy values. Thus, combination of biochemical and computational approaches could represent rational guidelines for further structural modification of these hydroxy-benzofuran derivatives as future drugs for treatment of AD.

Fast randomization of large genomic datasets while preserving alteration counts.

PubMed

Gobbi, Andrea; Iorio, Francesco; Dawson, Kevin J; Wedge, David C; Tamborero, David; Alexandrov, Ludmil B; Lopez-Bigas, Nuria; Garnett, Mathew J; Jurman, Giuseppe; Saez-Rodriguez, Julio

2014-09-01

Studying combinatorial patterns in cancer genomic datasets has recently emerged as a tool for identifying novel cancer driver networks. Approaches have been devised to quantify, for example, the tendency of a set of genes to be mutated in a 'mutually exclusive' manner. The significance of the proposed metrics is usually evaluated by computing P-values under appropriate null models. To this end, a Monte Carlo method (the switching-algorithm) is used to sample simulated datasets under a null model that preserves patient- and gene-wise mutation rates. In this method, a genomic dataset is represented as a bipartite network, to which Markov chain updates (switching-steps) are applied. These steps modify the network topology, and a minimal number of them must be executed to draw simulated datasets independently under the null model. This number has previously been deducted empirically to be a linear function of the total number of variants, making this process computationally expensive. We present a novel approximate lower bound for the number of switching-steps, derived analytically. Additionally, we have developed the R package BiRewire, including new efficient implementations of the switching-algorithm. We illustrate the performances of BiRewire by applying it to large real cancer genomics datasets. We report vast reductions in time requirement, with respect to existing implementations/bounds and equivalent P-value computations. Thus, we propose BiRewire to study statistical properties in genomic datasets, and other data that can be modeled as bipartite networks. BiRewire is available on BioConductor at http://www.bioconductor.org/packages/2.13/bioc/html/BiRewire.html. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

The impact of the resolution of meteorological datasets on catchment-scale drought studies

NASA Astrophysics Data System (ADS)

Hellwig, Jost; Stahl, Kerstin

2017-04-01

Gridded meteorological datasets provide the basis to study drought at a range of scales, including catchment scale drought studies in hydrology. They are readily available to study past weather conditions and often serve real time monitoring as well. As these datasets differ in spatial/temporal coverage and spatial/temporal resolution, for most studies there is a tradeoff between these features. Our investigation examines whether biases occur when studying drought on catchment scale with low resolution input data. For that, a comparison among the datasets HYRAS (covering Central Europe, 1x1 km grid, daily data, 1951 - 2005), E-OBS (Europe, 0.25° grid, daily data, 1950-2015) and GPCC (whole world, 0.5° grid, monthly data, 1901 - 2013) is carried out. Generally, biases in precipitation increase with decreasing resolution. Most important variations are found during summer. In low mountain range of Central Europe the datasets of sparse resolution (E-OBS, GPCC) overestimate dry days and underestimate total precipitation since they are not able to describe high spatial variability. However, relative measures like the correlation coefficient reveal good consistencies of dry and wet periods, both for absolute precipitation values and standardized indices like the Standardized Precipitation Index (SPI) or Standardized Precipitation Evaporation Index (SPEI). Particularly the most severe droughts derived from the different datasets match very well. These results indicate that absolute values of sparse resolution datasets applied to catchment scale might be critical to use for an assessment of the hydrological drought at catchment scale, whereas relative measures for determining periods of drought are more trustworthy. Therefore, studies on drought, that downscale meteorological data, should carefully consider their data needs and focus on relative measures for dry periods if sufficient for the task.

The CIRCORT database: Reference ranges and seasonal changes in diurnal salivary cortisol derived from a meta-dataset comprised of 15 field studies

PubMed Central

Miller, Robert; Stalder, Tobias; Jarczok, Marc; Almeida, David M.; Badrick, Ellena; Bartels, Meike; Boomsma, Dorret I.; Coe, Christopher L.; Dekker, Marieke C. J.; Donzella, Bonny; Fischer, Joachim E.; Gunnar, Megan R.; Kumari, Meena; Lederbogen, Florian; Oldehinkel, Albertine J.; Power, Christine; Rosmalen, Judith G.; Ryff, Carol D.; Subramanian, S V; Tiemeier, Henning; Watamura, Sarah E.; Kirschbaum, Clemens

2016-01-01

Diurnal salivary cortisol profiles are valuable indicators of adrenocortical functioning in epidemiological research and clinical practice. However, normative reference values derived from a large number of participants and across a wide age range are still missing. To fill this gap, data were compiled from 15 independently conducted field studies with a total of 104,623 salivary cortisol samples obtained from 18,698 unselected individuals (mean age: 48.3 years, age range: 0.5 to 98.5 years, 39% females). Besides providing a descriptive analysis of the complete dataset, we also performed mixed-effects growth curve modeling of diurnal salivary cortisol (i.e., 1 to 16 hours after awakening). Cortisol decreased significantly across the day and was influenced by both, age and sex. Intriguingly, we also found a pronounced impact of sampling season with elevated diurnal cortisol in spring and decreased levels in autumn. However, the majority of variance was accounted for by between-participant and between-study variance components. Based on these analyses, reference ranges (LC/MS-MS calibrated) for cortisol concentrations in saliva were derived for different times across the day, with more specific reference ranges generated for males and females in different age categories. This integrative summary provides important reference values on salivary cortisol to aid basic scientists and clinicians in interpreting deviations from the normal diurnal cycle. PMID:27448524

EnviroAtlas - Woodbine, IA - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 1 block group in Woodbine, Iowa. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Pittsburgh, PA - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 1,089 block groups in Pittsburgh, Pennsylvania. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Portland, OR - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 1176 block groups in Portland, Oregon. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (http:/www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Fresno, CA - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 405 block groups in Fresno, California. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - New Bedford, MA - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 128 block group in New Bedford, Massachusetts. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Tampa, FL - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 1,833 block groups in Tampa Bay, Florida. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Minneapolis/St. Paul, MN - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 1,772 block groups in Minneapolis/St. Paul, Minnesota. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Cleveland, OH - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 1,442 block groups in Cleveland, Ohio. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Milwaukee, WI - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 1,175 block groups in Milwaukee, Wisconsin. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Portland, ME - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 146 block groups in Portland, Maine. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Memphis, TN - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 703 block groups in Memphis, Tennessee. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Green Bay, WI - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 155 block groups in Green Bay, Wisconsin. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

EnviroAtlas - Austin, TX - Ecosystem Services by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset presents environmental benefits of the urban forest in 750 block groups in Austin, Texas. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Preventing the link between SES and high-risk behaviors: "value-added" education, drug use and delinquency in high-risk, urban schools.

PubMed

Tobler, Amy L; Komro, Kelli A; Dabroski, Alexis; Aveyard, Paul; Markham, Wolfgang A

2011-06-01

We examined whether schools achieving better than expected educational outcomes for their students influence the risk of drug use and delinquency among urban, racial/ethnic minority youth. Adolescents (n = 2,621), who were primarily African American and Hispanic and enrolled in Chicago public schools (n = 61), completed surveys in 6th (aged 12) and 8th (aged 14) grades. Value-added education was derived from standardized residuals of regression equations predicting school-level academic achievement and attendance from students' sociodemographic profiles and defined as having higher academic achievement and attendance than that expected given the sociodemographic profile of the schools' student composition. Multilevel logistic regression estimated the effects of value-added education on students' drug use and delinquency. After considering initial risk behavior, value-added education was associated with lower incidence of alcohol, cigarette and marijuana use; stealing; and participating in a group-against-group fight. Significant beneficial effects of value-added education remained for cigarette and marijuana use, stealing and participating in a group-against-group fight after adjustment for individual- and school-level covariates. Alcohol use (past month and heavy episodic) showed marginally significant trends in the hypothesized direction after these adjustments. Inner-city schools may break the links between social disadvantage, drug use and delinquency. Identifying the processes related to value-added education in order to improve school environments is warranted given the high costs associated with individual-level interventions.

Downscaling global precipitation for local applications - a case for the Rhine basin

NASA Astrophysics Data System (ADS)

Sperna Weiland, Frederiek; van Verseveld, Willem; Schellekens, Jaap

2017-04-01

Within the EU FP7 project eartH2Observe a global Water Resources Re-analysis (WRR) is being developed. This re-analysis consists of meteorological and hydrological water balance variables with global coverage, spanning the period 1979-2014 at 0.25 degrees resolution (Schellekens et al., 2016). The dataset can be of special interest in regions with limited in-situ data availability, yet for local scale analysis particularly in mountainous regions, a resolution of 0.25 degrees may be too coarse and downscaling the data to a higher resolution may be required. A downscaling toolbox has been made that includes spatial downscaling of precipitation based on the global WorldClim dataset that is available at 1 km resolution as a monthly climatology (Hijmans et al., 2005). The input of the down-scaling tool are either the global eartH2Observe WRR1 and WRR2 datasets based on the WFDEI correction methodology (Weedon et al., 2014) or the global Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset (Beck et al., 2016). Here we present a validation of the datasets over the Rhine catchment by means of a distributed hydrological model (wflow, Schellekens et al., 2014) using a number of precipitation scenarios. (1) We start by running the model using the local reference dataset derived by spatial interpolation of gauge observations. Furthermore we use (2) the MSWEP dataset at the native 0.25-degree resolution followed by (3) MSWEP downscaled with the WorldClim dataset and final (4) MSWEP downscaled with the local reference dataset. The validation will be based on comparison of the modeled river discharges as well as rainfall statistics. We expect that down-scaling the MSWEP dataset with the WorldClim data to higher resolution will increase its performance. To test the performance of the down-scaling routine we have added a run with MSWEP data down-scaled with the local dataset and compare this with the run based on the local dataset itself. - Beck, H. E. et al., 2016. MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-236, accepted for final publication. - Hijmans, R.J. et al., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. - Schellekens, J. et al., 2016. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data Discuss., doi:10.5194/essd-2016-55, under review. - Schellekens, J. et al., 2014. Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model. Environmental Modelling&Software - Weedon, G.P. et al., 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resources Research, 50, doi:10.1002/2014WR015638.

Integrated Analysis of Alzheimer's Disease and Schizophrenia Dataset Revealed Different Expression Pattern in Learning and Memory.

PubMed

Li, Wen-Xing; Dai, Shao-Xing; Liu, Jia-Qian; Wang, Qian; Li, Gong-Hua; Huang, Jing-Fei

2016-01-01

Alzheimer's disease (AD) and schizophrenia (SZ) are both accompanied by impaired learning and memory functions. This study aims to explore the expression profiles of learning or memory genes between AD and SZ. We downloaded 10 AD and 10 SZ datasets from GEO-NCBI for integrated analysis. These datasets were processed using RMA algorithm and a global renormalization for all studies. Then Empirical Bayes algorithm was used to find the differentially expressed genes between patients and controls. The results showed that most of the differentially expressed genes were related to AD whereas the gene expression profile was little affected in the SZ. Furthermore, in the aspects of the number of differentially expressed genes, the fold change and the brain region, there was a great difference in the expression of learning or memory related genes between AD and SZ. In AD, the CALB1, GABRA5, and TAC1 were significantly downregulated in whole brain, frontal lobe, temporal lobe, and hippocampus. However, in SZ, only two genes CRHBP and CX3CR1 were downregulated in hippocampus, and other brain regions were not affected. The effect of these genes on learning or memory impairment has been widely studied. It was suggested that these genes may play a crucial role in AD or SZ pathogenesis. The different gene expression patterns between AD and SZ on learning and memory functions in different brain regions revealed in our study may help to understand the different mechanism between two diseases.

Evaluating the Contribution of NASA Remotely-Sensed Data Sets on a Convection-Allowing Forecast Model

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley T.; Case, Jonathan L.; Molthan, Andrew L.

2012-01-01

The Short-term Prediction Research and Transition (SPoRT) Center is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service forecast offices. SPoRT provides real-time NASA products and capabilities to help its partners address specific operational forecast challenges. One challenge that forecasters face is using guidance from local and regional deterministic numerical models configured at convection-allowing resolution to help assess a variety of mesoscale/convective-scale phenomena such as sea-breezes, local wind circulations, and mesoscale convective weather potential on a given day. While guidance from convection-allowing models has proven valuable in many circumstances, the potential exists for model improvements by incorporating more representative land-water surface datasets, and by assimilating retrieved temperature and moisture profiles from hyper-spectral sounders. In order to help increase the accuracy of deterministic convection-allowing models, SPoRT produces real-time, 4-km CONUS forecasts using a configuration of the Weather Research and Forecasting (WRF) model (hereafter SPoRT-WRF) that includes unique NASA products and capabilities including 4-km resolution soil initialization data from the Land Information System (LIS), 2-km resolution SPoRT SST composites over oceans and large water bodies, high-resolution real-time Green Vegetation Fraction (GVF) composites derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and retrieved temperature and moisture profiles from the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). NCAR's Model Evaluation Tools (MET) verification package is used to generate statistics of model performance compared to in situ observations and rainfall analyses for three months during the summer of 2012 (June-August). Detailed analyses of specific severe weather outbreaks during the summer will be presented to assess the potential added-value of the SPoRT datasets and data assimilation methodology compared to a WRF configuration without the unique datasets and data assimilation.

Analysis of spontaneous EEG activity in Alzheimer's disease using cross-sample entropy and graph theory.

PubMed

Gomez, Carlos; Poza, Jesus; Gomez-Pilar, Javier; Bachiller, Alejandro; Juan-Cruz, Celia; Tola-Arribas, Miguel A; Carreres, Alicia; Cano, Monica; Hornero, Roberto

2016-08-01

The aim of this pilot study was to analyze spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) by means of Cross-Sample Entropy (Cross-SampEn) and two local measures derived from graph theory: clustering coefficient (CC) and characteristic path length (PL). Five minutes of EEG activity were recorded from 37 patients with dementia due to AD and 29 elderly controls. Our results showed that Cross-SampEn values were lower in the AD group than in the control one for all the interactions among EEG channels. This finding indicates that EEG activity in AD is characterized by a lower statistical dissimilarity among channels. Significant differences were found mainly for fronto-central interactions (p <; 0.01, permutation test). Additionally, the application of graph theory measures revealed diverse neural network changes, i.e. lower CC and higher PL values in AD group, leading to a less efficient brain organization. This study suggests the usefulness of our approach to provide further insights into the underlying brain dynamics associated with AD.

MRI measurements of intracranial pressure in the upright posture: The effect of the hydrostatic pressure gradient.

PubMed

Alperin, Noam; Lee, Sang H; Bagci, Ahmet M

2015-10-01

To add the hydrostatic component of the cerebrospinal fluid (CSF) pressure to magnetic resonance imaging (MRI)-derived intracranial pressure (ICP) measurements in the upright posture for derivation of pressure value in a central cranial location often used in invasive ICP measurements. Additional analyses were performed using data previously collected from 10 healthy subjects scanned in supine and sitting positions with a 0.5T vertical gap MRI scanner (GE Medical). Pulsatile blood and CSF flows to and from the brain were quantified using cine phase-contrast. Intracranial compliance and pressure were calculated using a previously described method. The vertical distance between the location of the CSF flow measurement and a central cranial location was measured manually in the mid-sagittal T1 -weighted image obtained in the upright posture. The hydrostatic pressure gradient of a CSF column with similar height was then added to the MR-ICP value. After adjustment for the hydrostatic component, the mean ICP value was reduced by 7.6 mmHg. Mean ICP referenced to the central cranial level was -3.4 ± 1.7 mmHg compared to the unadjusted value of +4.3 ± 1.8 mmHg. In the upright posture, the hydrostatic pressure component needs to be added to the MRI-derived ICP values for compatibility with invasive ICP at a central cranial location. © 2015 Wiley Periodicals, Inc.

ATP binding cassette (ABC) transporters: expression and clinical value in glioblastoma.

PubMed

Dréan, Antonin; Rosenberg, Shai; Lejeune, François-Xavier; Goli, Larissa; Nadaradjane, Aravindan Arun; Guehennec, Jérémy; Schmitt, Charlotte; Verreault, Maïté; Bielle, Franck; Mokhtari, Karima; Sanson, Marc; Carpentier, Alexandre; Delattre, Jean-Yves; Idbaih, Ahmed

2018-03-08

ATP-binding cassette transporters (ABC transporters) regulate traffic of multiple compounds, including chemotherapeutic agents, through biological membranes. They are expressed by multiple cell types and have been implicated in the drug resistance of some cancer cells. Despite significant research in ABC transporters in the context of many diseases, little is known about their expression and clinical value in glioblastoma (GBM). We analyzed expression of 49 ABC transporters in both commercial and patient-derived GBM cell lines as well as from 51 human GBM tumor biopsies. Using The Cancer Genome Atlas (TCGA) cohort as a training dataset and our cohort as a validation dataset, we also investigated the prognostic value of these ABC transporters in newly diagnosed GBM patients, treated with the standard of care. In contrast to commercial GBM cell lines, GBM-patient derived cell lines (PDCL), grown as neurospheres in a serum-free medium, express ABC transporters similarly to parental tumors. Serum appeared to slightly increase resistance to temozolomide correlating with a tendency for an increased expression of ABCB1. Some differences were observed mainly due to expression of ABC transporters by microenvironmental cells. Together, our data suggest that the efficacy of chemotherapeutic agents may be misestimated in vitro if they are the targets of efflux pumps whose expression can be modulated by serum. Interestingly, several ABC transporters have prognostic value in the TCGA dataset. In our cohort of 51 GBM patients treated with radiation therapy with concurrent and adjuvant temozolomide, ABCA13 overexpression is associated with a decreased progression free survival in univariate (p < 0.01) and multivariate analyses including MGMT promoter methylation (p = 0.05) suggesting reduced sensitivity to temozolomide in ABCA13 overexpressing GBM. Expression of ABC transporters is: (i) detected in GBM and microenvironmental cells and (ii) better reproduced in GBM-PDCL. ABCA13 expression is an independent prognostic factor in newly diagnosed GBM patients. Further prospective studies are warranted to investigate whether ABCA13 expression can be used to further personalize treatments for GBM.

The role of observational reference data for climate downscaling: Insights from the VALUE COST Action

NASA Astrophysics Data System (ADS)

Kotlarski, Sven; Gutiérrez, José M.; Boberg, Fredrik; Bosshard, Thomas; Cardoso, Rita M.; Herrera, Sixto; Maraun, Douglas; Mezghani, Abdelkader; Pagé, Christian; Räty, Olle; Stepanek, Petr; Soares, Pedro M. M.; Szabo, Peter

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of downscaling methods. Such assessments can be expected to crucially depend on the existence of accurate and reliable observational reference data. In dynamical downscaling, observational data can influence model development itself and, later on, model evaluation, parameter calibration and added value assessment. In empirical-statistical downscaling, observations serve as predictand data and directly influence model calibration with corresponding effects on downscaled climate change projections. We here present a comprehensive assessment of the influence of uncertainties in observational reference data and of scale-related issues on several of the above-mentioned aspects. First, temperature and precipitation characteristics as simulated by a set of reanalysis-driven EURO-CORDEX RCM experiments are validated against three different gridded reference data products, namely (1) the EOBS dataset (2) the recently developed EURO4M-MESAN regional re-analysis, and (3) several national high-resolution and quality-controlled gridded datasets that recently became available. The analysis reveals a considerable influence of the choice of the reference data on the evaluation results, especially for precipitation. It is also illustrated how differences between the reference data sets influence the ranking of RCMs according to a comprehensive set of performance measures.

50 CFR 679.45 - IFQ cost recovery program.

Code of Federal Regulations, 2014 CFR

2014-10-01

... as GAF is added to the value of the IFQ permit holder's landed IFQ, and the sum is multiplied by the... ADMINISTRATION, DEPARTMENT OF COMMERCE (CONTINUED) FISHERIES OF THE EXCLUSIVE ECONOMIC ZONE OFF ALASKA Individual... recovery liability based on the value of all landed IFQ and GAF derived from the permit holder's IFQ permit...

Osteoporosis in adult patients with atopic dermatitis: A nationwide population-based study

PubMed Central

Lu, Chun-Ching; Su, Yu-Feng; Tsai, Tai-Hsin; Wu, Chieh-Hsin

2017-01-01

The aim of this study was to investigate osteoporosis risk in atopic dermatitis (AD) patients. This study included patients in the Taiwan National Health Insurance Research dataset. The population-based study included all patients aged 20–49 years who had been diagnosed with AD during 1996–2010. In total, 35,229 age and gender-matched patients without AD in a 1:1 ratio were randomly selected as the non-AD group. Cox proportional-hazards regression and Kaplan–Meier analyses were used to measure the hazard ratios and the cumulative incidences of osteoporosis, respectively. During the follow-up period, 360(1.02%) AD patients and 127(0.36%) non-AD patients developed osteoporosis. The overall incidence of osteoporosis was4.72-fold greater in the AD patients compared to the non-AD patients (1.82 vs. 0.24 per 1,000 person-years, respectively) after adjusting for potential confounding factors. Osteoporosis risk factors included female gender, age, advanced Charlson Comorbidity Index, depression and use of corticosteroids. The dataset analysis showed that AD was significantly associated with subsequent risk of osteoporosis. PMID:28207767

An empirical description of the dispersion of 5th and 95th percentiles in worldwide anthropometric data applied to estimating accommodation with unknown correlation values.

PubMed

Albin, Thomas J; Vink, Peter

2015-01-01

Anthropometric data are assumed to have a Gaussian (Normal) distribution, but if non-Gaussian, accommodation estimates are affected. When data are limited, users may choose to combine anthropometric elements by Combining Percentiles (CP) (adding or subtracting), despite known adverse effects. This study examined whether global anthropometric data are Gaussian distributed. It compared the Median Correlation Method (MCM) of combining anthropometric elements with unknown correlations to CP to determine if MCM provides better estimates of percentile values and accommodation. Percentile values of 604 male and female anthropometric data drawn from seven countries worldwide were expressed as standard scores. The standard scores were tested to determine if they were consistent with a Gaussian distribution. Empirical multipliers for determining percentile values were developed.In a test case, five anthropometric elements descriptive of seating were combined in addition and subtraction models. Percentile values were estimated for each model by CP, MCM with Gaussian distributed data, or MCM with empirically distributed data. The 5th and 95th percentile values of a dataset of global anthropometric data are shown to be asymmetrically distributed. MCM with empirical multipliers gave more accurate estimates of 5th and 95th percentiles values. Anthropometric data are not Gaussian distributed. The MCM method is more accurate than adding or subtracting percentiles.

STAMMEX high resolution gridded daily precipitation dataset over Germany: a new potential for regional precipitation climate research

NASA Astrophysics Data System (ADS)

Zolina, Olga; Simmer, Clemens; Kapala, Alice; Mächel, Hermann; Gulev, Sergey; Groisman, Pavel

2014-05-01

We present new high resolution precipitation daily grids developed at Meteorological Institute, University of Bonn and German Weather Service (DWD) under the STAMMEX project (Spatial and Temporal Scales and Mechanisms of Extreme Precipitation Events over Central Europe). Daily precipitation grids have been developed from the daily-observing precipitation network of DWD, which runs one of the World's densest rain gauge networks comprising more than 7500 stations. Several quality-controlled daily gridded products with homogenized sampling were developed covering the periods 1931-onwards (with 0.5 degree resolution), 1951-onwards (0.25 degree and 0.5 degree), and 1971-2000 (0.1 degree). Different methods were tested to select the best gridding methodology that minimizes errors of integral grid estimates over hilly terrain. Besides daily precipitation values with uncertainty estimates (which include standard estimates of the kriging uncertainty as well as error estimates derived by a bootstrapping algorithm), the STAMMEX data sets include a variety of statistics that characterize temporal and spatial dynamics of the precipitation distribution (quantiles, extremes, wet/dry spells, etc.). Comparisons with existing continental-scale daily precipitation grids (e.g., CRU, ECA E-OBS, GCOS) which include considerably less observations compared to those used in STAMMEX, demonstrate the added value of high-resolution grids for extreme rainfall analyses. These data exhibit spatial variability pattern and trends in precipitation extremes, which are missed or incorrectly reproduced over Central Europe from coarser resolution grids based on sparser networks. The STAMMEX dataset can be used for high-quality climate diagnostics of precipitation variability, as a reference for reanalyses and remotely-sensed precipitation products (including the upcoming Global Precipitation Mission products), and for input into regional climate and operational weather forecast models. We will present numerous application of the STAMMEX grids spanning from case studies of the major Central European floods to long-term changes in different precipitation statistics, including those accounting for the alternation of dry and wet periods and precipitation intensities associated with prolonged rainy episodes.

EarthServer: Visualisation and use of uncertainty as a data exploration tool

NASA Astrophysics Data System (ADS)

Walker, Peter; Clements, Oliver; Grant, Mike

2013-04-01

The Ocean Science/Earth Observation community generates huge datasets from satellite observation. Until recently it has been difficult to obtain matching uncertainty information for these datasets and to apply this to their processing. In order to make use of uncertainty information when analysing "Big Data" we need both the uncertainty itself (attached to the underlying data) and a means of working with the combined product without requiring the entire dataset to be downloaded. The European Commission FP7 project EarthServer (http://earthserver.eu) is addressing the problem of accessing and ad-hoc analysis of extreme-size Earth Science data using cutting-edge Array Database technology. The core software (Rasdaman) and web services wrapper (Petascope) allow huge datasets to be accessed using Open Geospatial Consortium (OGC) standard interfaces including the well established standards, Web Coverage Service (WCS) and Web Map Service (WMS) as well as the emerging standard, Web Coverage Processing Service (WCPS). The WCPS standard allows the running of ad-hoc queries on any of the data stored within Rasdaman, creating an infrastructure where users are not restricted by bandwidth when manipulating or querying huge datasets. The ESA Ocean Colour - Climate Change Initiative (OC-CCI) project (http://www.esa-oceancolour-cci.org/), is producing high-resolution, global ocean colour datasets over the full time period (1998-2012) where high quality observations were available. This climate data record includes per-pixel uncertainty data for each variable, based on an analytic method that classifies how much and which types of water are present in a pixel, and assigns uncertainty based on robust comparisons to global in-situ validation datasets. These uncertainty values take two forms, Root Mean Square (RMS) and Bias uncertainty, respectively representing the expected variability and expected offset error. By combining the data produced through the OC-CCI project with the software from the EarthServer project we can produce a novel data offering that allows the use of traditional exploration and access mechanisms such as WMS and WCS. However the real benefits can be seen when utilising WCPS to explore the data . We will show two major benefits to this infrastructure. Firstly we will show that the visualisation of the combined chlorophyll and uncertainty datasets through a web based GIS portal gives users the ability to instantaneously assess the quality of the data they are exploring using traditional web based plotting techniques as well as through novel web based 3 dimensional visualisation. Secondly we will showcase the benefits available when combining these data with the WCPS standard. The uncertainty data can be utilised in queries using the standard WCPS query language. This allows selection of data either for download or use within the query, based on the respective uncertainty values as well as the possibility of incorporating both the chlorophyll data and uncertainty data into complex queries to produce additional novel data products. By filtering with uncertainty at the data source rather than the client we can minimise traffic over the network allowing huge datasets to be worked on with a minimal time penalty.

Librarians and Scientists: Combining Forces for Better Metrics

NASA Astrophysics Data System (ADS)

Rots, Arnold H.; Winkelman, Sherry

2015-08-01

Traditionally, observatory bibliographies mainly rely on two parameters derived from the carefully compiled lists of publications associated, in a well-defined way, with the observatories contribution to the advancement of science: numbers of articles and numbers of citations - in addition to the bibliographic metadata relating to those articles. The information that can be extracted from metrics based on these parameters is limited. This is a realization that is not just typical to astronomy and astrophysics, but one that is felt across many disciplines.Relating articles with very specific datasets allows us to join those datasets' metadata with the bibliographic metadata which opens a much richer field of information to mine for knowledge concerning the performance, not only of the observatory as a whole, but also its parts: instruments, types of observations, length of observations, etc. We have experimented extensively with such new metrics in the Chandra Data Archive in the Chandra X-ray Center at SAO.The linking of articles with individual datasets requires a level of scientific expertise that is usually not in the, otherwise extensive, skill set of the librarians, but is something that is crucial on the road to more informative bibliographic metrics.This talk is a plea for librarians and research scientists to join forces to make this happen. The added benefit of such a collaboration is a powerful research tool for navigating data and literature through a single interface.This work has been supported by NASA under contract NAS 8-03060 to the Smithsonian Astrophysical Observatory for operation of the Chandra X-ray Center. It depends critically on the services provided by the ADS, which is funded by NASA Grant NNX12AG54G.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2018-04-01

Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. Copyright © 2018 Elsevier B.V. All rights reserved.

High Resolution Stratigraphic Mapping in Complex Terrain: A Comparison of Traditional Remote Sensing Techniques with Unmanned Aerial Vehicle - Structure from Motion Photogrammetry

NASA Astrophysics Data System (ADS)

Nesbit, P. R.; Hugenholtz, C.; Durkin, P.; Hubbard, S. M.; Kucharczyk, M.; Barchyn, T.

2016-12-01

Remote sensing and digital mapping have started to revolutionize geologic mapping in recent years as a result of their realized potential to provide high resolution 3D models of outcrops to assist with interpretation, visualization, and obtaining accurate measurements of inaccessible areas. However, in stratigraphic mapping applications in complex terrain, it is difficult to acquire information with sufficient detail at a wide spatial coverage with conventional techniques. We demonstrate the potential of a UAV and Structure from Motion (SfM) photogrammetric approach for improving 3D stratigraphic mapping applications within a complex badland topography. Our case study is performed in Dinosaur Provincial Park (Alberta, Canada), mapping late Cretaceous fluvial meander belt deposits of the Dinosaur Park formation amidst a succession of steeply sloping hills and abundant drainages - creating a challenge for stratigraphic mapping. The UAV-SfM dataset (2 cm spatial resolution) is compared directly with a combined satellite and aerial LiDAR dataset (30 cm spatial resolution) to reveal advantages and limitations of each dataset before presenting a unique workflow that utilizes the dense point cloud from the UAV-SfM dataset for analysis. The UAV-SfM dense point cloud minimizes distortion, preserves 3D structure, and records an RGB attribute - adding potential value in future studies. The proposed UAV-SfM workflow allows for high spatial resolution remote sensing of stratigraphy in complex topographic environments. This extended capability can add value to field observations and has the potential to be integrated with subsurface petroleum models.

Form factors and differential branching ratio of B →K μ+μ- in AdS/QCD

NASA Astrophysics Data System (ADS)

Momeni, S.; Khosravi, R.

2018-03-01

The holographic distribution amplitudes (DAs) for the K pseudoscalar meson are derived. For this aim, the light-front wave function (LFWF) of the K meson is extracted within the framework of the anti-de Sitter/quantum chromodynamics (AdS/QCD) correspondence. We consider a momentum-dependent (dynamical) helicity wave function that contains the dynamical spin effects. We use the LFWF to predict the radius and the electromagnetic form factor of the kaon and compare them with the experimental values. Then, the holographic twist-2 DA of K meson ϕK(α ,μ ) is investigated and compared with the result of the light-cone sum rules (LCSR). The transition form factors of the semileptonic B →K ℓ+ℓ- decays are derived from the holographic DAs of the kaon. With the help of these form factors, the differential branching ratio of the B →K μ+μ- on q2 is plotted. A comparison is made between our prediction in AdS/QCD and the results obtained from two models including the LCSR and the lattice QCD as well as the experimental values.

Hybrid-fusion SPECT/CT systems in parathyroid adenoma: Technological improvements and added clinical diagnostic value.

PubMed

Wong, K K; Chondrogiannis, S; Bowles, H; Fuster, D; Sánchez, N; Rampin, L; Rubello, D

Nuclear medicine traditionally employs planar and single photon emission computed tomography (SPECT) imaging techniques to depict the biodistribution of radiotracers for the diagnostic investigation of a range of disorders of endocrine gland function. The usefulness of combining functional information with anatomy derived from computed tomography (CT), magnetic resonance imaging (MRI), and high resolution ultrasound (US), has long been appreciated, either using visual side-by-side correlation, or software-based co-registration. The emergence of hybrid SPECT/CT camera technology now allows the simultaneous acquisition of combined multi-modality imaging, with seamless fusion of 3D volume datasets. Thus, it is not surprising that there is growing literature describing the many advantages that contemporary SPECT/CT technology brings to radionuclide investigation of endocrine disorders, showing potential advantages for the pre-operative locating of the parathyroid adenoma using a minimally invasive surgical approach, especially in the presence of ectopic glands and in multiglandular disease. In conclusion, hybrid SPECT/CT imaging has become an essential tool to ensure the most accurate diagnostic in the management of patients with hyperparathyroidism. Copyright © 2016 Elsevier España, S.L.U. y SEMNIM. All rights reserved.

Region 9 - Social Vulnerability Index

EPA Pesticide Factsheets

The Social Vulnerability Index is derived from the 2000 US Census data. The fields included are percent minority, median household income, age (under 18 and over 64), population without a high school diploma, linguistically isolated households, and single female head of households with own children under 18 (single moms). The data is at the block group level. Each field for each block group is assigned an index score of 0-3, based on whether the value of that dataset falls in the top quartile (score=3), second quartile (score=2), third quartile (score=1), or bottom quartile (score=0). The scores for each field are then added together to assign a comprehensive score to each block group (0-21). The highest scores are block groups that have the highest percentage of sensitive populations (highest percent minority, lowest per capita income, highest percent of population under 18 and over 64, highest percentage of population without a high school degree, highest percent of linguistically isolated households, and highest percent of single female head of households). Zoe Heller of the US EPA Region 9's Communities and Ecosystems Division, is responsible for the design and development of the Social Vulnerability Index data set.

Arizona - Social Vulnerability Index

EPA Pesticide Factsheets

The Social Vulnerability Index is derived from the 2000 US Census data. The fields included are percent minority, median household income, age (under 18 and over 64), population without a high school diploma, linguistically isolated households, and single female head of households with own children under 18 (single moms). The data is at the block group level. Each field for each block group is assigned an index score of 0-3, based on whether the value of that dataset falls in the top quartile (score=3), second quartile (score=2), third quartile (score=1), or bottom quartile (score=0). The scores for each field are then added together to assign a comprehensive score to each block group (0-21). The highest scores are block groups that have the highest percentage of sensitive populations (highest percent minority, lowest per capita income, highest percent of population under 18 and over 64, highest percentage of population without a high school degree, highest percent of linguistically isolated households, and highest percent of single female head of households). Zoe Heller of the US EPA Region 9's Communities and Ecosystems Division, is responsible for the design and development of the Social Vulnerability Index data set.

Does school ethos explain the relationship between value-added education and teenage substance use? A cohort study.

PubMed

Markham, Wolfgang A; Young, Robert; Sweeting, Helen; West, Patrick; Aveyard, Paul

2012-07-01

Previous studies found lower substance use in schools achieving better examination and truancy results than expected, given their pupil populations (high value-added schools). This study examines whether these findings are replicated in West Scotland and whether school ethos indicators focussing on pupils' perceptions of schooling (environment, involvement, engagement and teacher-pupil relations) mediate the associations. Teenagers from forty-one schools (S2, aged 13, n = 2268; S4, aged 15, n = 2096) previously surveyed in primary school (aged 11, n = 2482) were surveyed in the late 1990s. School value-added scores were derived from standardised residuals of two regression equations separately predicting from pupils' socio-demographic characteristics (1) proportions of pupils passing five Scottish Standard Grade Examinations, and (2) half-day truancy loss. Outcomes were current smoking, monthly drinking, ever illicit drug use. Random effects logistic regression models adjusted for potential pupil-level confounders were used to assess (1) associations between substance use and school-level value-added scores and (2) whether these associations were mediated by pupils' perceptions of schooling or other school-level factors (school roll, religious denomination and mean aggregated school-level ethos scores). Against expectations, value-added education was positively associated with smoking (Odds Ratios [95% confidence intervals] for one standard deviation increase in value-added scores were 1.28 [1.02-1.61] in S2 and 1.13 [1.00-1.27] in S4) and positively but weakly and non-significantly associated with drinking and drug use. Engagement and positive teacher-pupil relations were strongly and negatively associated with all substance use outcomes at both ages. Other school-level factors appeared weakly and largely non-significantly related to substance use. Value-added scores were unrelated to school ethos measures and no ethos measure mediated associations between value-added education and substance use. We conclude that substance use in Scotland is more likely in high value-added schools, among disengaged students and those with poorer student-teacher relationships. Understanding the underpinning mechanisms is a potentially important public health concern. Copyright © 2012 Elsevier Ltd. All rights reserved.

Does school ethos explain the relationship between value-added education and teenage substance use? A cohort study

PubMed Central

Markham, Wolfgang A.; Young, Robert; Sweeting, Helen; West, Patrick; Aveyard, Paul

2012-01-01

Previous studies found lower substance use in schools achieving better examination and truancy results than expected, given their pupil populations (high value-added schools). This study examines whether these findings are replicated in West Scotland and whether school ethos indicators focussing on pupils' perceptions of schooling (environment, involvement, engagement and teacher–pupil relations) mediate the associations. Teenagers from forty-one schools (S2, aged 13, n = 2268; S4, aged 15, n = 2096) previously surveyed in primary school (aged 11, n = 2482) were surveyed in the late 1990s. School value-added scores were derived from standardised residuals of two regression equations separately predicting from pupils' socio-demographic characteristics (1) proportions of pupils passing five Scottish Standard Grade Examinations, and (2) half-day truancy loss. Outcomes were current smoking, monthly drinking, ever illicit drug use. Random effects logistic regression models adjusted for potential pupil-level confounders were used to assess (1) associations between substance use and school-level value-added scores and (2) whether these associations were mediated by pupils' perceptions of schooling or other school-level factors (school roll, religious denomination and mean aggregated school-level ethos scores). Against expectations, value-added education was positively associated with smoking (Odds Ratios [95% confidence intervals] for one standard deviation increase in value-added scores were 1.28 [1.02–1.61] in S2 and 1.13 [1.00–1.27] in S4) and positively but weakly and non-significantly associated with drinking and drug use. Engagement and positive teacher–pupil relations were strongly and negatively associated with all substance use outcomes at both ages. Other school-level factors appeared weakly and largely non-significantly related to substance use. Value-added scores were unrelated to school ethos measures and no ethos measure mediated associations between value-added education and substance use. We conclude that substance use in Scotland is more likely in high value-added schools, among disengaged students and those with poorer student–teacher relationships. Understanding the underpinning mechanisms is a potentially important public health concern. PMID:22503837

Evaluation of Least-Squares Collocation and the Reduced Point Mass method using the International Association of Geodesy, Joint Study Group 0.3 test data.

NASA Astrophysics Data System (ADS)

Tscherning, Carl Christian; Herceg, Matija

2014-05-01

The methods of Least-Squares Collocation (LSC) and the Reduced Point Mass method (RPM) both uses radial basis-functions for the representation of the anomalous gravity potential (T). LSC uses as many base-functions as the number of observations, while the RPM method uses as many as deemed necessary. Both methods have been evaluated and for some tests compared in the two areas (Central Europe and South-East Pacific). For both areas test data had been generated using EGM2008. As observational data (a) ground gravity disturbances, (b) airborne gravity disturbances, (c) GOCE like Second order radial derivatives and (d) GRACE along-track potential differences were available. The use of these data for the computation of values of (e) T in a grid was the target of the evaluation and comparison investigation. Due to the fact that T in principle can only be computed using global data, the remove-restore procedure was used, with EGM2008 subtracted (and later added to T) up to degree 240 using dataset (a) and (b) and up to degree 36 for datasets (c) and (d). The estimated coefficient error was accounted for when using LSC and in the calculation of error-estimates. The main result is that T was estimated with an error (computed minus control data, (e) from which EGM2008 to degree 240 or 36 had been subtracted ) as found in the table (LSC used): Area Europe Data-set (mgal) (e)-240(a) (b) (e)-36 (c) (d) Mean -0.0 0.0 -0.1 -0.1 -0.3 -1.8 Standard deviation4.1 0.8 2.7 32.6 6.0 19.2 Max. difference 19.9 10.4 16.9 69.9 31.3 47.0 Min.difference -16.2 -3.7 -15.5 -92.1 -27.8 -65.5 Area Pacific Data-set (mgal) (e)-240(a) (b) (e)-36 (c) (d) Mean -0.1 -0.1 -0.1 4.6 -0.2 0.2 Standard deviation4.8 0.2 1.9 49.1 6.7 18.6 Max.difference 22.2 1.8 13.4 115.5 26.9 26.5 Min.difference -28.7 -3.1 -15.7 -106.4 -33.6 22.1 The result using RPM with data-sets (a), (b), (c) gave comparable results. The use of (d) with the RPM method is being implemented. Tests were also done computing dataset (a) from the other datasets. The results here may serve as a bench-mark for other radial basis-function implementations for computing approximations to T. Improvements are certainly possible, e.g. by taking the topography and bathymetry into account.

Reliability of voxel gray values in cone beam computed tomography for preoperative implant planning assessment.

PubMed

Parsa, Azin; Ibrahim, Norliza; Hassan, Bassam; Motroni, Alessandro; van der Stelt, Paul; Wismeijer, Daniel

2012-01-01

To assess the reliability of cone beam computed tomography (CBCT) voxel gray value measurements using Hounsfield units (HU) derived from multislice computed tomography (MSCT) as a clinical reference (gold standard). Ten partially edentulous human mandibular cadavers were scanned by two types of computed tomography (CT) modalities: multislice CT and cone beam CT. On MSCT scans, eight regions of interest (ROI) designating the site for preoperative implant placement were selected in each mandible. The datasets from both CT systems were matched using a three-dimensional (3D) registration algorithm. The mean voxel gray values of the region around the implant sites were compared between MSCT and CBCT. Significant differences between the mean gray values obtained by CBCT and HU by MSCT were found. In all the selected ROIs, CBCT showed higher mean values than MSCT. A strong correlation (R=0.968) between mean voxel gray values of CBCT and mean HU of MSCT was determined. Voxel gray values from CBCT deviate from actual HU units. However, a strong linear correlation exists, which may permit deriving actual HU units from CBCT using linear regression models.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: NLCD 2001 Imperviousness

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the mean percent impervious surface from the Imperviousness Layer of the National Land Cover Dataset 2001 (LaMotte and Wieczorek, 2010), compiled for every catchment of NHDPlus for the conterminous United States. The source data set represents imperviousness for the conterminous United States for 2001. The Imperviousness Layer of the National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: NLCD 2001 Land Use and Land Cover

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the estimated area of land use and land cover from the National Land Cover Dataset 2001 (LaMotte, 2008), compiled for every catchment of NHDPlus for the conterminous United States. The source data set represents land use and land cover for the conterminous United States for 2001. The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Good, better, best? A comprehensive comparison of healthcare providers' performance: An application to physiotherapy practices in primary care.

PubMed

Steenhuis, Sander; Groeneweg, Niels; Koolman, Xander; Portrait, France

2017-12-01

Most payment methods in healthcare stimulate volume-driven care, rather than value-driven care. Value-based payment methods such as Pay-For-Performance have the potential to reduce costs and improve quality of care. Ideally, outcome indicators are used in the assessment of providers' performance. The aim of this paper is to describe the feasibility of assessing and comparing the performances of providers using a comprehensive set of quality and cost data. We had access to unique and extensive datasets containing individual data on PROMs, PREMs and costs of physiotherapy practices in Dutch primary care. We merged these datasets at the patient-level and compared the performances of these practices using case-mix corrected linear regression models. Several significant differences in performance were detected between practices. These results can be used by both physiotherapists, to improve treatment given, and insurers to support their purchasing decisions. The study demonstrates that it is feasible to compare the performance of providers using PROMs and PREMs. However, it would take an extra effort to increase usefulness and it remains unclear under which conditions this effort is cost-effective. Healthcare providers need to be aware of the added value of registering outcomes to improve their quality. Insurers need to facilitate this by designing value-based contracts with the right incentives. Only then can payment methods contribute to value-based healthcare and increase value for patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Recent advances in the production of value added chemicals and lipids utilizing biodiesel industry generated crude glycerol as a substrate - Metabolic aspects, challenges and possibilities: An overview.

PubMed

Vivek, Narisetty; Sindhu, Raveendran; Madhavan, Aravind; Anju, Alphonsa Jose; Castro, Eulogio; Faraco, Vincenza; Pandey, Ashok; Binod, Parameswaran

2017-09-01

One of the major ecological concerns associated with biodiesel production is the generation of waste/crude glycerol during the trans-esterification process. Purification of this crude glycerol is not economically viable. In this context, the development of an efficient and economically viable strategy would be biotransformation reactions converting the biodiesel derived crude glycerol into value added chemicals. Hence the process ensures the sustainability and waste management in biodiesel industry, paving a path to integrated biorefineries. This review addresses a waste to wealth approach for utilization of crude glycerol in the production of value added chemicals, current trends, challenges, future perspectives, metabolic approaches and the genetic tools developed for the improved synthesis over wild type microorganisms were described. Copyright © 2017 Elsevier Ltd. All rights reserved.

Challenges in Collating Spirometry Reference Data for South-Asian Children: An Observational Study

PubMed Central

Lum, Sooky; Bountziouka, Vassiliki; Quanjer, Philip; Sonnappa, Samatha; Wade, Angela; Beardsmore, Caroline; Chhabra, Sunil K.; Chudasama, Rajesh K.; Cook, Derek G.; Harding, Seeromanie; Kuehni, Claudia E.; Prasad, K. V. V.; Whincup, Peter H.; Lee, Simon; Stocks, Janet

2016-01-01

Availability of sophisticated statistical modelling for developing robust reference equations has improved interpretation of lung function results. In 2012, the Global Lung function Initiative(GLI) published the first global all-age, multi-ethnic reference equations for spirometry but these lacked equations for those originating from the Indian subcontinent (South-Asians). The aims of this study were to assess the extent to which existing GLI-ethnic adjustments might fit South-Asian paediatric spirometry data, assess any similarities and discrepancies between South-Asian datasets and explore the feasibility of deriving a suitable South-Asian GLI-adjustment. Methods Spirometry datasets from South-Asian children were collated from four centres in India and five within the UK. Records with transcription errors, missing values for height or spirometry, and implausible values were excluded(n = 110). Results Following exclusions, cross-sectional data were available from 8,124 children (56.3% male; 5–17 years). When compared with GLI-predicted values from White Europeans, forced expired volume in 1s (FEV1) and forced vital capacity (FVC) in South-Asian children were on average 15% lower, ranging from 4–19% between centres. By contrast, proportional reductions in FEV1 and FVC within all but two datasets meant that the FEV1/FVC ratio remained independent of ethnicity. The ‘GLI-Other’ equation fitted data from North India reasonably well while ‘GLI-Black’ equations provided a better approximation for South-Asian data than the ‘GLI-White’ equation. However, marked discrepancies in the mean lung function z-scores between centres especially when examined according to socio-economic conditions precluded derivation of a single South-Asian GLI-adjustment. Conclusion Until improved and more robust prediction equations can be derived, we recommend the use of ‘GLI-Black’ equations for interpreting most South-Asian data, although ‘GLI-Other’ may be more appropriate for North Indian data. Prospective data collection using standardised protocols to explore potential sources of variation due to socio-economic circumstances, secular changes in growth/predictors of lung function and ethnicities within the South-Asian classification are urgently required. PMID:27119342

The TRIDEC Virtual Tsunami Atlas - customized value-added simulation data products for Tsunami Early Warning generated on compute clusters

NASA Astrophysics Data System (ADS)

Löwe, P.; Hammitzsch, M.; Babeyko, A.; Wächter, J.

2012-04-01

The development of new Tsunami Early Warning Systems (TEWS) requires the modelling of spatio-temporal spreading of tsunami waves both recorded from past events and hypothetical future cases. The model results are maintained in digital repositories for use in TEWS command and control units for situation assessment once a real tsunami occurs. Thus the simulation results must be absolutely trustworthy, in a sense that the quality of these datasets is assured. This is a prerequisite as solid decision making during a crisis event and the dissemination of dependable warning messages to communities under risk will be based on them. This requires data format validity, but even more the integrity and information value of the content, being a derived value-added product derived from raw tsunami model output. Quality checking of simulation result products can be done in multiple ways, yet the visual verification of both temporal and spatial spreading characteristics for each simulation remains important. The eye of the human observer still remains an unmatched tool for the detection of irregularities. This requires the availability of convenient, human-accessible mappings of each simulation. The improvement of tsunami models necessitates the changes in many variables, including simulation end-parameters. Whenever new improved iterations of the general models or underlying spatial data are evaluated, hundreds to thousands of tsunami model results must be generated for each model iteration, each one having distinct initial parameter settings. The use of a Compute Cluster Environment (CCE) of sufficient size allows the automated generation of all tsunami-results within model iterations in little time. This is a significant improvement to linear processing on dedicated desktop machines or servers. This allows for accelerated/improved visual quality checking iterations, which in turn can provide a positive feedback into the overall model improvement iteratively. An approach to set-up and utilize the CCE has been implemented by the project Collaborative, Complex, and Critical Decision Processes in Evolving Crises (TRIDEC) funded under the European Union's FP7. TRIDEC focuses on real-time intelligent information management in Earth management. The addressed challenges include the design and implementation of a robust and scalable service infrastructure supporting the integration and utilisation of existing resources with accelerated generation of large volumes of data. These include sensor systems, geo-information repositories, simulations and data fusion tools. Additionally, TRIDEC adopts enhancements of Service Oriented Architecture (SOA) principles in terms of Event Driven Architecture (EDA) design. As a next step the implemented CCE's services to generate derived and customized simulation products are foreseen to be provided via an EDA service for on-demand processing for specific threat-parameters and to accommodate for model improvements.

Current knowledge on agarolytic enzymes and the industrial potential of agar-derived sugars.

PubMed

Yun, Eun Ju; Yu, Sora; Kim, Kyoung Heon

2017-07-01

Agar is a major cell wall carbohydrate of red macroalgae (Rhodophyta). Sugars derived from agar, such as agarooligosaccharides (AOSs), neoagarooligosaccharides (NAOSs), neoagarobiose (NAB), and 3,6-anhydro-L-galactose (L-AHG), possess various physiological activities. These agar-derived sugars can be produced by hydrolysis using chemicals or agarolytic enzymes. Despite the industrial potential of agar-derived sugars, their application has been hampered mainly due to the absence of efficient processes for the liquefaction and saccharification of agar. In this review, we have focused on strategies for producing high value-added sugars from agarose via chemical or enzymatic liquefaction and enzymatic saccharification. The liquefaction of agarose is a key step for preventing gelling and increasing the solubility of agarose in water by prehydrolyzing agarose into AOSs or NAOSs. For the industrial use of agar-derived sugars, AOS, NAOS, NAB, and L-AHG can be used as functional biomaterials owing to their physiological activities such as antiinflammation, skin whitening, and moisturizing. Recently, it was reported that AHG could be considered as a new anticariogenic sugar to replace xylitol. This review provides a comprehensive overview of processes for the hydrolysis of agar or agarose to produce high value-added sugars and the industrial application of these sugars.

Brazilian Pediatric Reference Data for Quantitative Ultrasound of Phalanges According to Gender, Age, Height and Weight

PubMed Central

de Carvalho, Wellington Roberto Gomes; de Moraes, Anderson Marques; Roman, Everton Paulo; Santos, Keila Donassolo; Medaets, Pedro Augusto Rodrigues; Veiga-Junior, Nélio Neves; Coelho, Adrielle Caroline Lace de Moraes; Krahenbühl, Tathyane; Sewaybricker, Leticia Esposito; Barros-Filho, Antonio de Azevedo; Morcillo, Andre Moreno; Guerra-Júnior, Gil

2015-01-01

Aims To establish normative data for phalangeal quantitative ultrasound (QUS) measures in Brazilian students. Methods The sample was composed of 6870 students (3688 females and 3182 males), aged 6 to 17 years. The bone status parameter, Amplitude Dependent Speed of Sound (AD-SoS) was assessed by QUS of the phalanges using DBM Sonic BP (IGEA, Carpi, Italy) equipment. Skin color was obtained by self-evaluation. The LMS method was used to derive smoothed percentiles reference charts for AD-SoS according to sex, age, height and weight and to generate the L, M, and S parameters. Results Girls showed higher AD-SoS values than boys in the age groups 7–16 (p<0.001). There were no differences on AD-SoS Z-scores according to skin color. In both sexes, the obese group showed lower values of AD-SoS Z-scores compared with subjects classified as thin or normal weight. Age (r2 = 0.48) and height (r2 = 0.35) were independent predictors of AD-SoS in females and males, respectively. Conclusion AD-SoS values in Brazilian children and adolescents were influenced by sex, age and weight status, but not by skin color. Our normative data could be used for monitoring AD-SoS in children or adolescents aged 6–17 years. PMID:26043082

Attributes for NHDPlus Catchments (Version 1.1)for the Conterminous United States: Contact Time, 2002

USGS Publications Warehouse

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This data set represents the average contact time, in units of days, compiled for every catchment of NHDPlus for the conterminous United States. Contact time, as described in Wolock and others (1989), is the baseflow residence time in the subsurface. The source data set was the U.S. Geological Survey's (USGS) 1-kilometer grid for the conterminous United States (D.M. Wolock, U.S. Geological Survey, written commun., 2008). The grid was created using a method described by Wolock and others (1997a; see equation 3). In the source data set, the contact time was estimated from 1-kilometer resolution elevation data (Verdin and Greenlee, 1996 ) and STATSGO soil characteristics (Wolock, 1997b). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Normalization of cortical thickness measurements across different T1 magnetic resonance imaging protocols by novel W-Score standardization.

PubMed

Chung, Jinyong; Yoo, Kwangsun; Lee, Peter; Kim, Chan Mi; Roh, Jee Hoon; Park, Ji Eun; Kim, Sang Joon; Seo, Sang Won; Shin, Jeong-Hyeon; Seong, Joon-Kyung; Jeong, Yong

2017-10-01

The use of different 3D T1-weighted magnetic resonance (T1 MR) imaging protocols induces image incompatibility across multicenter studies, negating the many advantages of multicenter studies. A few methods have been developed to address this problem, but significant image incompatibility still remains. Thus, we developed a novel and convenient method to improve image compatibility. W-score standardization creates quality reference values by using a healthy group to obtain normalized disease values. We developed a protocol-specific w-score standardization to control the protocol effect, which is applied to each protocol separately. We used three data sets. In dataset 1, brain T1 MR images of normal controls (NC) and patients with Alzheimer's disease (AD) from two centers, acquired with different T1 MR protocols, were used (Protocol 1 and 2, n = 45/group). In dataset 2, data from six subjects, who underwent MRI with two different protocols (Protocol 1 and 2), were used with different repetition times, echo times, and slice thicknesses. In dataset 3, T1 MR images from a large number of healthy normal controls (Protocol 1: n = 148, Protocol 2: n = 343) were collected for w-score standardization. The protocol effect and disease effect on subjects' cortical thickness were analyzed before and after the application of protocol-specific w-score standardization. As expected, different protocols resulted in differing cortical thickness measurements in both NC and AD subjects. Different measurements were obtained for the same subject when imaged with different protocols. Multivariate pattern difference between measurements was observed between the protocols. Classification accuracy between two protocols was nearly 90%. After applying protocol-specific w-score standardization, the differences between the protocols substantially decreased. Most importantly, protocol-specific w-score standardization reduced both univariate and multivariate differences in the images while maintaining the AD disease effect. Compared to conventional regression methods, our method showed the best performance for in terms of controlling the protocol effect while preserving disease information. Protocol-specific w-score standardization effectively resolved the concerns of conventional regression methods. It showed the best performance for improving the compatibility of a T1 MR post-processed feature, cortical thickness. Copyright © 2017 Elsevier Inc. All rights reserved.

A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.

PubMed

Algamal, Z Y; Lee, M H

2017-01-01

A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.

Knowledge-based decision support for Space Station assembly sequence planning

NASA Astrophysics Data System (ADS)

1991-04-01

A complete Personal Analysis Assistant (PAA) for Space Station Freedom (SSF) assembly sequence planning consists of three software components: the system infrastructure, intra-flight value added, and inter-flight value added. The system infrastructure is the substrate on which software elements providing inter-flight and intra-flight value-added functionality are built. It provides the capability for building representations of assembly sequence plans and specification of constraints and analysis options. Intra-flight value-added provides functionality that will, given the manifest for each flight, define cargo elements, place them in the National Space Transportation System (NSTS) cargo bay, compute performance measure values, and identify violated constraints. Inter-flight value-added provides functionality that will, given major milestone dates and capability requirements, determine the number and dates of required flights and develop a manifest for each flight. The current project is Phase 1 of a projected two phase program and delivers the system infrastructure. Intra- and inter-flight value-added were to be developed in Phase 2, which has not been funded. Based on experience derived from hundreds of projects conducted over the past seven years, ISX developed an Intelligent Systems Engineering (ISE) methodology that combines the methods of systems engineering and knowledge engineering to meet the special systems development requirements posed by intelligent systems, systems that blend artificial intelligence and other advanced technologies with more conventional computing technologies. The ISE methodology defines a phased program process that begins with an application assessment designed to provide a preliminary determination of the relative technical risks and payoffs associated with a potential application, and then moves through requirements analysis, system design, and development.

Knowledge-based decision support for Space Station assembly sequence planning

NASA Technical Reports Server (NTRS)

1991-01-01

A complete Personal Analysis Assistant (PAA) for Space Station Freedom (SSF) assembly sequence planning consists of three software components: the system infrastructure, intra-flight value added, and inter-flight value added. The system infrastructure is the substrate on which software elements providing inter-flight and intra-flight value-added functionality are built. It provides the capability for building representations of assembly sequence plans and specification of constraints and analysis options. Intra-flight value-added provides functionality that will, given the manifest for each flight, define cargo elements, place them in the National Space Transportation System (NSTS) cargo bay, compute performance measure values, and identify violated constraints. Inter-flight value-added provides functionality that will, given major milestone dates and capability requirements, determine the number and dates of required flights and develop a manifest for each flight. The current project is Phase 1 of a projected two phase program and delivers the system infrastructure. Intra- and inter-flight value-added were to be developed in Phase 2, which has not been funded. Based on experience derived from hundreds of projects conducted over the past seven years, ISX developed an Intelligent Systems Engineering (ISE) methodology that combines the methods of systems engineering and knowledge engineering to meet the special systems development requirements posed by intelligent systems, systems that blend artificial intelligence and other advanced technologies with more conventional computing technologies. The ISE methodology defines a phased program process that begins with an application assessment designed to provide a preliminary determination of the relative technical risks and payoffs associated with a potential application, and then moves through requirements analysis, system design, and development.

Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling

USGS Publications Warehouse

Sherba, Jason T.; Sleeter, Benjamin M.; Davis, Adam W.; Parker, Owen P.

2015-01-01

Global land-use/land-cover (LULC) change projections and historical datasets are typically available at coarse grid resolutions and are often incompatible with modeling applications at local to regional scales. The difficulty of downscaling and reapportioning global gridded LULC change projections to regional boundaries is a barrier to the use of these datasets in a state-and-transition simulation model (STSM) framework. Here we compare three downscaling techniques to transform gridded LULC transitions into spatial scales and thematic LULC classes appropriate for use in a regional STSM. For each downscaling approach, Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) LULC projections, at the 0.5 × 0.5 cell resolution, were downscaled to seven Level III ecoregions in the Pacific Northwest, United States. RCP transition values at each cell were downscaled based on the proportional distribution between ecoregions of (1) cell area, (2) land-cover composition derived from remotely-sensed imagery, and (3) historic LULC transition values from a LULC history database. Resulting downscaled LULC transition values were aggregated according to their bounding ecoregion and “cross-walked” to relevant LULC classes. Ecoregion-level LULC transition values were applied in a STSM projecting LULC change between 2005 and 2100. While each downscaling methods had advantages and disadvantages, downscaling using the historical land-use history dataset consistently apportioned RCP LULC transitions in agreement with historical observations. Regardless of the downscaling method, some LULC projections remain improbable and require further investigation.

Evidence of long-term NAO influence on East-Central Europe winter precipitation from a guano-derived δ15N record.

PubMed

Cleary, Daniel M; Wynn, Jonathan G; Ionita, Monica; Forray, Ferenc L; Onac, Bogdan P

2017-10-26

Currently there is a scarcity of paleo-records related to the North Atlantic Oscillation (NAO), particularly in East-Central Europe (ECE). Here we report δ 15 N analysis of guano from a cave in NW Romania with the intent of reconstructing past variation in ECE hydroclimate and examine NAO impacts on winter precipitation. We argue that the δ 15 N values of guano indicate that the nitrogen cycle is hydrologically controlled and the δ 15 N values likely reflect winter precipitation related to nitrogen mineralization prior to the growing season. Drier conditions indicated by δ 15 N values at AD 1848-1852 and AD 1880-1930 correspond to the positive phase of the NAO. The increased frequency of negative phases of the NAO between AD 1940-1975 is contemporaneous with higher δ 15 N values (wetter conditions). A 4‰ decrease in δ 15 N values at the end of the 1970's corresponds to a strong reduction in precipitation associated with a shift from negative to positive phase of the NAO. Using the relationship between NAO index and δ 15 N values in guano for the instrumental period, we reconstructed NAO-like phases back to AD 1650. Our results advocate that δ 15 N values of guano offer a proxy of the NAO conditions in the more distant past, helping assess its predictability.

Value-added potential of expeller-pressed canola oil refining: characterization of sinapic acid derivatives and tocopherols from byproducts.

PubMed

Chen, Yougui; Thiyam-Hollander, Usha; Barthet, Veronique J; Aachary, Ayyappan A

2014-10-08

Valuable phenolic antioxidants are lost during oil refining, but evaluation of their occurrence in refining byproducts is lacking. Rapeseed and canola oil are both rich sources of sinapic acid derivatives and tocopherols. The retention and loss of sinapic acid derivatives and tocopherols in commercially produced expeller-pressed canola oils subjected to various refining steps and the respective byproducts were investigated. Loss of canolol (3) and tocopherols were observed during bleaching (84.9%) and deodorization (37.6%), respectively. Sinapic acid (2) (42.9 μg/g), sinapine (1) (199 μg/g), and canolol (344 μg/g) were found in the refining byproducts, namely, soap stock, spent bleaching clay, and wash water, for the first time. Tocopherols (3.75 mg/g) and other nonidentified phenolic compounds (2.7 mg sinapic acid equivalent/g) were found in deodistillates, a byproduct of deodorization. DPPH radical scavenging confirmed the antioxidant potential of the byproducts. This study confirms the value-added potential of byproducts of refining as sources of endogenous phenolics.

Toward engineering E. coli with an autoregulatory system for lignin valorization

PubMed Central

Wu, Weihua; Liu, Fang; Singh, Seema

2018-01-01

Efficient lignin valorization could add more than 10-fold the value gained from burning it for energy and is critical for economic viability of future biorefineries. However, lignin-derived aromatics from biomass pretreatment are known to be potent fermentation inhibitors in microbial production of fuels and other value-added chemicals. In addition, isopropyl-β-d-1-thiogalactopyranoside and other inducers are routinely added into fermentation broth to induce the expression of pathway enzymes, which further adds to the overall process cost. An autoregulatory system that can diminish the aromatics’ toxicity as well as be substrate-inducible can be the key for successful integration of lignin valorization into future lignocellulosic biorefineries. Toward that goal, in this study an autoregulatory system is demonstrated that alleviates the toxicity issue and eliminates the cost of an external inducer. Specifically, this system is composed of a catechol biosynthesis pathway coexpressed with an active aromatic transporter CouP under induction by a vanillin self-inducible promoter, ADH7, to effectively convert the lignin-derived aromatics into value-added chemicals using Escherichia coli as a host. The constructed autoregulatory system can efficiently transport vanillin across the cell membrane and convert it to catechol. Compared with the system without CouP expression, the expression of catechol biosynthesis pathway with transporter CouP significantly improved the catechol yields about 30% and 40% under promoter pTrc and ADH7, respectively. This study demonstrated an aromatic-induced autoregulatory system that enabled conversion of lignin-derived aromatics into catechol without the addition of any costly, external inducers, providing a promising and economically viable route for lignin valorization. PMID:29500185

The Impact of Alzheimer's Disease on the Chinese Economy.

PubMed

Keogh-Brown, Marcus R; Jensen, Henning Tarp; Arrighi, H Michael; Smith, Richard D

2016-02-01

Recent increases in life expectancy may greatly expand future Alzheimer's Disease (AD) burdens. China's demographic profile, aging workforce and predicted increasing burden of AD-related care make its economy vulnerable to AD impacts. Previous economic estimates of AD predominantly focus on health system burdens and omit wider whole-economy effects, potentially underestimating the full economic benefit of effective treatment. AD-related prevalence, morbidity and mortality for 2011-2050 were simulated and were, together with associated caregiver time and costs, imposed on a dynamic Computable General Equilibrium model of the Chinese economy. Both economic and non-economic outcomes were analyzed. Simulated Chinese AD prevalence quadrupled during 2011-50 from 6-28 million. The cumulative discounted value of eliminating AD equates to China's 2012 GDP (US$8 trillion), and the annual predicted real value approaches US AD cost-of-illness (COI) estimates, exceeding US$1 trillion by 2050 (2011-prices). Lost labor contributes 62% of macroeconomic impacts. Only 10% derives from informal care, challenging previous COI-estimates of 56%. Health and macroeconomic models predict an unfolding 2011-2050 Chinese AD epidemic with serious macroeconomic consequences. Significant investment in research and development (medical and non-medical) is warranted and international researchers and national authorities should therefore target development of effective AD treatment and prevention strategies.

The Impact of Alzheimer's Disease on the Chinese Economy

PubMed Central

Keogh-Brown, Marcus R.; Jensen, Henning Tarp; Arrighi, H. Michael; Smith, Richard D.

2015-01-01

Background Recent increases in life expectancy may greatly expand future Alzheimer's Disease (AD) burdens. China's demographic profile, aging workforce and predicted increasing burden of AD-related care make its economy vulnerable to AD impacts. Previous economic estimates of AD predominantly focus on health system burdens and omit wider whole-economy effects, potentially underestimating the full economic benefit of effective treatment. Methods AD-related prevalence, morbidity and mortality for 2011–2050 were simulated and were, together with associated caregiver time and costs, imposed on a dynamic Computable General Equilibrium model of the Chinese economy. Both economic and non-economic outcomes were analyzed. Findings Simulated Chinese AD prevalence quadrupled during 2011–50 from 6–28 million. The cumulative discounted value of eliminating AD equates to China's 2012 GDP (US$8 trillion), and the annual predicted real value approaches US AD cost-of-illness (COI) estimates, exceeding US$1 trillion by 2050 (2011-prices). Lost labor contributes 62% of macroeconomic impacts. Only 10% derives from informal care, challenging previous COI-estimates of 56%. Interpretation Health and macroeconomic models predict an unfolding 2011–2050 Chinese AD epidemic with serious macroeconomic consequences. Significant investment in research and development (medical and non-medical) is warranted and international researchers and national authorities should therefore target development of effective AD treatment and prevention strategies. PMID:26981556

Simplifying [18F]GE-179 PET: are both arterial blood sampling and 90-min acquisitions essential?

PubMed

McGinnity, Colm J; Riaño Barros, Daniela A; Trigg, William; Brooks, David J; Hinz, Rainer; Duncan, John S; Koepp, Matthias J; Hammers, Alexander

2018-06-11

The NMDA receptor radiotracer [ 18 F]GE-179 has been used with 90-min scans and arterial plasma input functions. We explored whether (1) arterial blood sampling is avoidable and (2) shorter scans are feasible. For 20 existing [ 18 F]GE-179 datasets, we generated (1) standardised uptake values (SUVs) over eight intervals; (2) volume of distribution (V T ) images using population-based input functions (PBIFs), scaled using one parent plasma sample; and (3) V T images using three shortened datasets, using the original parent plasma input functions (ppIFs). Correlations with the original ppIF-derived 90-min V T s increased for later interval SUVs (maximal ρ = 0.78; 80-90 min). They were strong for PBIF-derived V T s (ρ = 0.90), but between-subject coefficient of variation increased. Correlations were very strong for the 60/70/80-min original ppIF-derived V T s (ρ = 0.97-1.00), which suffered regionally variant negative bias. Where arterial blood sampling is available, reduction of scan duration to 60 min is feasible, but with negative bias. The performance of SUVs was more consistent across participants than PBIF-derived V T s.

Earth rotation derived from occultation records

NASA Astrophysics Data System (ADS)

Sôma, Mitsuru; Tanikawa, Kiyotaka

2016-04-01

We determined the values of the Earth's rotation parameter, ΔT = T T - UT, around AD 500 after confirming that the value of the tidal acceleration, dot{n}, of the lunar motion remained unchanged during the period between ancient times and the present. For determining of ΔT, we used contemporaneous occultations of planets by the Moon. In general, occultation records are not useful. However, there are some records that give us a stringent condition for the range of ΔT. Records of the lunar occultations in AD 503 and AD 513 are such examples. In order to assure the usefulness of this occultation data, we used contemporaneous annular and total solar eclipses, which have not been used in the preceding work. This is the first work in which the lunar occultation data have been used as primary data to determine the value of ΔT together with auxiliary contemporaneous annular and total solar eclipses. Our ΔT value is less than a smoothed value (Stephenson 1997) by at least 450 s. The result is consistent with our earlier results obtained from solar eclipses.

The CIRCORT database: Reference ranges and seasonal changes in diurnal salivary cortisol derived from a meta-dataset comprised of 15 field studies.

PubMed

Miller, Robert; Stalder, Tobias; Jarczok, Marc; Almeida, David M; Badrick, Ellena; Bartels, Meike; Boomsma, Dorret I; Coe, Christopher L; Dekker, Marieke C J; Donzella, Bonny; Fischer, Joachim E; Gunnar, Megan R; Kumari, Meena; Lederbogen, Florian; Power, Christine; Ryff, Carol D; Subramanian, S V; Tiemeier, Henning; Watamura, Sarah E; Kirschbaum, Clemens

2016-11-01

Diurnal salivary cortisol profiles are valuable indicators of adrenocortical functioning in epidemiological research and clinical practice. However, normative reference values derived from a large number of participants and across a wide age range are still missing. To fill this gap, data were compiled from 15 independently conducted field studies with a total of 104,623 salivary cortisol samples obtained from 18,698 unselected individuals (mean age: 48.3 years, age range: 0.5-98.5 years, 39% females). Besides providing a descriptive analysis of the complete dataset, we also performed mixed-effects growth curve modeling of diurnal salivary cortisol (i.e., 1-16h after awakening). Cortisol decreased significantly across the day and was influenced by both, age and sex. Intriguingly, we also found a pronounced impact of sampling season with elevated diurnal cortisol in spring and decreased levels in autumn. However, the majority of variance was accounted for by between-participant and between-study variance components. Based on these analyses, reference ranges (LC/MS-MS calibrated) for cortisol concentrations in saliva were derived for different times across the day, with more specific reference ranges generated for males and females in different age categories. This integrative summary provides important reference values on salivary cortisol to aid basic scientists and clinicians in interpreting deviations from the normal diurnal cycle. Copyright © 2016 Elsevier Ltd. All rights reserved.

A multitemporal (1979-2009) land-use/land-cover dataset of the binational Santa Cruz Watershed

USGS Publications Warehouse

2011-01-01

Trends derived from multitemporal land-cover data can be used to make informed land management decisions and to help managers model future change scenarios. We developed a multitemporal land-use/land-cover dataset for the binational Santa Cruz watershed of southern Arizona, United States, and northern Sonora, Mexico by creating a series of land-cover maps at decadal intervals (1979, 1989, 1999, and 2009) using Landsat Multispectral Scanner and Thematic Mapper data and a classification and regression tree classifier. The classification model exploited phenological changes of different land-cover spectral signatures through the use of biseasonal imagery collected during the (dry) early summer and (wet) late summer following rains from the North American monsoon. Landsat images were corrected to remove atmospheric influences, and the data were converted from raw digital numbers to surface reflectance values. The 14-class land-cover classification scheme is based on the 2001 National Land Cover Database with a focus on "Developed" land-use classes and riverine "Forest" and "Wetlands" cover classes required for specific watershed models. The classification procedure included the creation of several image-derived and topographic variables, including digital elevation model derivatives, image variance, and multitemporal Kauth-Thomas transformations. The accuracy of the land-cover maps was assessed using a random-stratified sampling design, reference aerial photography, and digital imagery. This showed high accuracy results, with kappa values (the statistical measure of agreement between map and reference data) ranging from 0.80 to 0.85.

Florbetaben PET in the Early Diagnosis of Alzheimer's Disease: A Discrete Event Simulation to Explore Its Potential Value and Key Data Gaps

PubMed Central

Guo, Shien; Getsios, Denis; Hernandez, Luis; Cho, Kelly; Lawler, Elizabeth; Altincatal, Arman; Lanes, Stephan; Blankenburg, Michael

2012-01-01

The growing understanding of the use of biomarkers in Alzheimer's disease (AD) may enable physicians to make more accurate and timely diagnoses. Florbetaben, a beta-amyloid tracer used with positron emission tomography (PET), is one of these diagnostic biomarkers. This analysis was undertaken to explore the potential value of florbetaben PET in the diagnosis of AD among patients with suspected dementia and to identify key data that are needed to further substantiate its value. A discrete event simulation was developed to conduct exploratory analyses from both US payer and societal perspectives. The model simulates the lifetime course of disease progression for individuals, evaluating the impact of their patient management from initial diagnostic work-up to final diagnosis. Model inputs were obtained from specific analyses of a large longitudinal dataset from the New England Veterans Healthcare System and supplemented with data from public data sources and assumptions. The analyses indicate that florbetaben PET has the potential to improve patient outcomes and reduce costs under certain scenarios. Key data on the use of florbetaben PET, such as its influence on time to confirmation of final diagnosis, treatment uptake, and treatment persistency, are unavailable and would be required to confirm its value. PMID:23326754

Health state utility values of high prevalence mental disorders in Australia: results from the National Survey of Mental Health and Wellbeing.

PubMed

Mihalopoulos, Cathrine; Engel, Lidia; Le, Long Khanh-Dao; Magnus, Anne; Harris, Meredith; Chatterton, Mary Lou

2018-07-01

High prevalence mental disorders including depression, anxiety and substance use disorders are associated with high economic and disease burden. However, there is little information regarding the health state utility values of such disorders according to their clinical severity using comparable instruments across all disorders. This study reports utility values for high prevalence mental disorders using data from the 2007 Australian National Survey of Mental Health and Wellbeing (NSMHWB). Utility values were derived from the AQoL-4D and analysed by disorder classification (affective only (AD), anxiety-related only (ANX), substance use only (SUB) plus four comorbidity groups), severity level (mild, moderate, severe), symptom recency (reported in the past 30 days), and comorbidity (combination of disorders). The adjusted Wald test was applied to detect statistically significant differences of weighted means and the magnitude of difference between groups was presented as a modified Cohen's d. In total, 1526 individuals met criteria for a 12-month mental disorder. The mean utility value was 0.67 (SD = 0.27), with lower utility values associated with higher severity levels and some comorbidities. Utility values for AD, ANX and SUB were 0.64 (SD = 0.25), 0.71 (SD = 0.25) and 0.81 (SD = 0.19), respectively. No differences in utility values were observed between disorders within disorder groups. Utility values were significantly lower among people with recent symptoms (within past 30 days) than those without; when examined by diagnostic group, this pattern held for people with SUB, but not for people with ANX or AD. Health state utility values of people with high prevalence mental disorders differ significantly by severity level, number of mental health comorbidities and the recency of symptoms, which provide new insights on the burden associated with high prevalence mental disorders in Australia. The derived utility values can be used to populate future economic models.

Simplified and age-appropriate recommendations for added sugars in children.

PubMed

Goran, M I; Riemer, S L; Alderete, T L

2018-04-01

Excess sugar intake increases risk for obesity and related comorbidities among children. The World Health Organization (WHO), American Heart Association (AHA) and the 2015 USDA dietary recommendations have proposed guidelines for added sugar intake to reduce risk for disease. WHO and USDA recommendations are presented as a percentage of daily calories from added sugar. This approach is not easily understood or translated to children, where energy needs increase with age. The AHA recommendation is based on a fixed value of 25 g of added sugar for all children 2-19 years of age. This approach does not take into account the different levels of intake across this wide age range. Due to these limitations, we adapted current recommendations for added sugars based on daily energy needs of children 2-19 years. We used those values to derive simple regression equations to predict grams or teaspoons of added sugars per day based on age that would be equivalent to 10% of daily energy needs. This proposed approach aligns with the changing nutritional needs of children and adolescents during growth. © 2017 World Obesity Federation.

Penicillium roqueforti: a multifunctional cell factory of high value-added molecules.

PubMed

Mioso, R; Toledo Marante, F J; Herrera Bravo de Laguna, I

2015-04-01

This is a comprehensive review, with 114 references, of the chemical diversity found in the fungus Penicillium roqueforti. Secondary metabolites of an alkaloidal nature are described, for example, ergot alkaloids such as festuclavine, isofumigaclavines A and B, and diketopiperazine alkaloids such as roquefortines A-D, which are derived from imidazole. Other metabolites are marcfortines A-C, PR-toxin, eremofortines A-E, mycophenolic and penicillic acids, and some γ-lactones. Also, recent developments related to the structural characteristics of botryodiplodin and andrastin are studied-the latter has anticancer properties. Finally, we discuss the enzymes of P. roqueforti, which can participate in the biotechnological production of high value-added molecules, as well as the use of secondary metabolite profiles for taxonomic purposes. © 2014 The Society for Applied Microbiology.

GWIPS-viz: 2018 update

PubMed Central

Michel, Audrey M; Kiniry, Stephen J; O’Connor, Patrick B F; Mullan, James P

2018-01-01

Abstract The GWIPS-viz browser (http://gwips.ucc.ie/) is an on-line genome browser which is tailored for exploring ribosome profiling (Ribo-seq) data. Since its publication in 2014, GWIPS-viz provides Ribo-seq data for an additional 14 genomes bringing the current total to 23. The integration of new Ribo-seq data has been automated thereby increasing the number of available tracks to 1792, a 10-fold increase in the last three years. The increase is particularly substantial for data derived from human sources. Following user requests, we added the functionality to download these tracks in bigWig format. We also incorporated new types of data (e.g. TCP-seq) as well as auxiliary tracks from other sources that help with the interpretation of Ribo-seq data. Improvements in the visualization of the data have been carried out particularly for bacterial genomes where the Ribo-seq data are now shown in a strand specific manner. For higher eukaryotic datasets, we provide characteristics of individual datasets using the RUST program which includes the triplet periodicity, sequencing biases and relative inferred A-site dwell times. This information can be used for assessing the quality of Ribo-seq datasets. To improve the power of the signal, we aggregate Ribo-seq data from several studies into Global aggregate tracks for each genome. PMID:28977460

A publicly available benchmark for biomedical dataset retrieval: the reference standard for the 2016 bioCADDIE dataset retrieval challenge

PubMed Central

Gururaj, Anupama E.; Chen, Xiaoling; Pournejati, Saeid; Alter, George; Hersh, William R.; Demner-Fushman, Dina; Ohno-Machado, Lucila

2017-01-01

Abstract The rapid proliferation of publicly available biomedical datasets has provided abundant resources that are potentially of value as a means to reproduce prior experiments, and to generate and explore novel hypotheses. However, there are a number of barriers to the re-use of such datasets, which are distributed across a broad array of dataset repositories, focusing on different data types and indexed using different terminologies. New methods are needed to enable biomedical researchers to locate datasets of interest within this rapidly expanding information ecosystem, and new resources are needed for the formal evaluation of these methods as they emerge. In this paper, we describe the design and generation of a benchmark for information retrieval of biomedical datasets, which was developed and used for the 2016 bioCADDIE Dataset Retrieval Challenge. In the tradition of the seminal Cranfield experiments, and as exemplified by the Text Retrieval Conference (TREC), this benchmark includes a corpus (biomedical datasets), a set of queries, and relevance judgments relating these queries to elements of the corpus. This paper describes the process through which each of these elements was derived, with a focus on those aspects that distinguish this benchmark from typical information retrieval reference sets. Specifically, we discuss the origin of our queries in the context of a larger collaborative effort, the biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium, and the distinguishing features of biomedical dataset retrieval as a task. The resulting benchmark set has been made publicly available to advance research in the area of biomedical dataset retrieval. Database URL: https://biocaddie.org/benchmark-data PMID:29220453

Abnormal degree centrality in Alzheimer's disease patients with depression: A resting-state functional magnetic resonance imaging study.

PubMed

Guo, Zhongwei; Liu, Xiaozheng; Hou, Hongtao; Wei, Fuquan; Liu, Jian; Chen, Xingli

2016-06-15

Depression is common in Alzheimer's disease (AD) and occurs in AD patients with a prevalence of up to 40%. It reduces cognitive function and increases the burden on caregivers. Currently, there are very few medications that are useful for treating depression in AD patients. Therefore, understanding the brain abnormalities in AD patients with depression (D-AD) is crucial for developing effective interventions. The aim of this study was to investigate the intrinsic dysconnectivity pattern of whole-brain functional networks at the voxel level in D-AD patients based on degree centrality (DC) as measured by resting-state functional magnetic resonance imaging (R-fMRI). Our study included 32 AD patients. All patients were evaluated using the Neuropsychiatric Inventory and Hamilton Depression Rating Scale and further divided into two groups: 15 D-AD patients and 17 non-depressed AD (nD-AD) patients. R-fMRI datasets were acquired from these D-AD and nD-AD patients. First, we performed a DC analysis to identify voxels that showed altered whole brain functional connectivity (FC) with other voxels. We then further investigated FC using the abnormal DC regions to examine in more detail the connectivity patterns of the identified DC changes. D-AD patients had lower DC values in the right middle frontal, precentral, and postcentral gyrus than nD-AD patients. Seed-based analysis revealed decreased connectivity between the precentral and postcentral gyrus to the supplementary motor area and middle cingulum. FC also decreased in the right middle frontal, precentral, and postcentral gyrus. Thus, AD patients with depression fit a 'network dysfunction model' distinct from major depressive disorder and AD. Copyright © 2016. Published by Elsevier Inc.

A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

NASA Astrophysics Data System (ADS)

Porwal, A.; Carranza, J.; Hale, M.

2004-12-01

A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

Modeling the effects of diagenesis on carbonate clumped-isotope values in deep- and shallow-water settings

NASA Astrophysics Data System (ADS)

Stolper, Daniel A.; Eiler, John M.; Higgins, John A.

2018-04-01

The measurement of multiply isotopically substituted ('clumped isotope') carbonate groups provides a way to reconstruct past mineral formation temperatures. However, dissolution-reprecipitation (i.e., recrystallization) reactions, which commonly occur during sedimentary burial, can alter a sample's clumped-isotope composition such that it partially or wholly reflects deeper burial temperatures. Here we derive a quantitative model of diagenesis to explore how diagenesis alters carbonate clumped-isotope values. We apply the model to a new dataset from deep-sea sediments taken from Ocean Drilling Project site 807 in the equatorial Pacific. This dataset is used to ground truth the model. We demonstrate that the use of the model with accompanying carbonate clumped-isotope and carbonate δ18O values provides new constraints on both the diagenetic history of deep-sea settings as well as past equatorial sea-surface temperatures. Specifically, the combination of the diagenetic model and data support previous work that indicates equatorial sea-surface temperatures were warmer in the Paleogene as compared to today. We then explore whether the model is applicable to shallow-water settings commonly preserved in the rock record. Using a previously published dataset from the Bahamas, we demonstrate that the model captures the main trends of the data as a function of burial depth and thus appears applicable to a range of depositional settings.

Short-term experiments in using digestate products as substitutes for mineral (N) fertilizer: Agronomic performance, odours, and ammonia emission impacts.

PubMed

Riva, C; Orzi, V; Carozzi, M; Acutis, M; Boccasile, G; Lonati, S; Tambone, F; D'Imporzano, G; Adani, F

2016-03-15

Anaerobic digestion produces a biologically stable and high-value fertilizer product, the digestate, which can be used as an alternative to mineral fertilizers on crops. However, misuse of digestate can lead to annoyance for the public (odours) and to environmental problems such as nitrate leaching and ammonia emissions into the air. Full field experimental data are needed to support the use of digestate in agriculture, promoting its correct management. In this work, short-term experiments were performed to substitute mineral N fertilizers (urea) with digestate and products derived from it to the crop silage maize. Digestate and the liquid fraction of digestate were applied to soil at pre-sowing and as topdressing fertilizers in comparison with urea, both by surface application and subsurface injection during the cropping seasons 2012 and 2013. After each fertilizer application, both odours and ammonia emissions were measured, giving data about digestate and derived products' impacts. The AD products could substitute for urea without reducing crop yields, apart from the surface application of AD-derived fertilizers. Digestate and derived products, because of high biological stability acquired during the AD, had greatly reduced olfactometry impact, above all when they were injected into soils (82-88% less odours than the untreated biomass, i.e. cattle slurry). Ammonia emission data indicated, as expected, that the correct use of digestate and derived products required their injection into the soil avoiding, ammonia volatilization into the air and preserving fertilizer value. Sub-surface injection allowed ammonia emissions to be reduced by 69% and 77% compared with surface application during the 2012 and 2013 campaigns. Copyright © 2015 Elsevier B.V. All rights reserved.

Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potential

USGS Publications Warehouse

Parks, Sean; Holsinger, Lisa M.; Voss, Morgan; Loehman, Rachel A.; Robinson, Nathaniel P.

2018-01-01

Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre-and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: the delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection and instead use a mean compositing approach in which all valid pixels (e.g. cloud-free) over a pre-specified date range (pre- and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared the standard approach in which one pre-fire and post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western US. These validations are compared to Landsat-based fire severity datasets produced using only one pre- and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets show improved validation statistics compared to parallel versions in which only one pre-fire and post-fire scene are used. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e. fire perimeters) be produced independently and prior to applying our methods, we suggest our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe.

Interpretable functional principal component analysis.

PubMed

Lin, Zhenhua; Wang, Liangliang; Cao, Jiguo

2016-09-01

Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations. However, these intervals are often hard for naïve users to identify, because of the vague definition of "significant values". In this article, we develop a novel penalty-based method to derive FPCs that are only nonzero precisely in the intervals where the values of FPCs are significant, whence the derived FPCs possess better interpretability than the FPCs derived from existing methods. To compute the proposed FPCs, we devise an efficient algorithm based on projection deflation techniques. We show that the proposed interpretable FPCs are strongly consistent and asymptotically normal under mild conditions. Simulation studies confirm that with a competitive performance in explaining variations of sample curves, the proposed FPCs are more interpretable than the traditional counterparts. This advantage is demonstrated by analyzing two real datasets, namely, electroencephalography data and Canadian weather data. © 2015, The International Biometric Society.

Prediction of new brain metastases after radiosurgery: validation and analysis of performance of a multi-institutional nomogram.

PubMed

Ayala-Peacock, Diandra N; Attia, Albert; Braunstein, Steve E; Ahluwalia, Manmeet S; Hepel, Jaroslaw; Chung, Caroline; Contessa, Joseph; McTyre, Emory; Peiffer, Ann M; Lucas, John T; Isom, Scott; Pajewski, Nicholas M; Kotecha, Rupesh; Stavas, Mark J; Page, Brandi R; Kleinberg, Lawrence; Shen, Colette; Taylor, Robert B; Onyeuku, Nasarachi E; Hyde, Andrew T; Gorovets, Daniel; Chao, Samuel T; Corso, Christopher; Ruiz, Jimmy; Watabe, Kounosuke; Tatter, Stephen B; Zadeh, Gelareh; Chiang, Veronica L S; Fiveash, John B; Chan, Michael D

2017-11-01

Stereotactic radiosurgery (SRS) without whole brain radiotherapy (WBRT) for brain metastases can avoid WBRT toxicities, but with risk of subsequent distant brain failure (DBF). Sole use of number of metastases to triage patients may be an unrefined method. Data on 1354 patients treated with SRS monotherapy from 2000 to 2013 for new brain metastases was collected across eight academic centers. The cohort was divided into training and validation datasets and a prognostic model was developed for time to DBF. We then evaluated the discrimination and calibration of the model within the validation dataset, and confirmed its performance with an independent contemporary cohort. Number of metastases (≥8, HR 3.53 p = 0.0001), minimum margin dose (HR 1.07 p = 0.0033), and melanoma histology (HR 1.45, p = 0.0187) were associated with DBF. A prognostic index derived from the training dataset exhibited ability to discriminate patients' DBF risk within the validation dataset (c-index = 0.631) and Heller's explained relative risk (HERR) = 0.173 (SE = 0.048). Absolute number of metastases was evaluated for its ability to predict DBF in the derivation and validation datasets, and was inferior to the nomogram. A nomogram high-risk threshold yielding a 2.1-fold increased need for early WBRT was identified. Nomogram values also correlated to number of brain metastases at time of failure (r = 0.38, p < 0.0001). We present a multi-institutionally validated prognostic model and nomogram to predict risk of DBF and guide risk-stratification of patients who are appropriate candidates for radiosurgery versus upfront WBRT.

Comparison of rotational temperature derived from ground-based OH airglow observations with TIMED/SABER to evaluate the Einstein Coefficients

NASA Astrophysics Data System (ADS)

Liu, W.; Xu, J.; Smith, A. K.; Yuan, W.

2017-12-01

Ground-based observations of the OH(9-4, 8-3, 6-2, 5-1, 3-0) band airglows over Xinglong, China (40°24'N, 117°35'E) from December 2011 to 2014 are used to calculate rotational temperatures. The temperatures are calculated using five commonly used Einstein coefficient datasets. The kinetic temperature from TIMED/SABER is completely independent of the OH rotational temperature. SABER temperatures are weighted vertically by weighting functions calculated for each emitting vibrational state from two SABER OH volume emission rate profiles. By comparing the ground-based OH rotational temperature with SABER's, five Einstein coefficient datasets are evaluated. The results show that temporal variations of the rotational temperatures are well correlated with SABER's; the linear correlation coefficients are higher than 0.72, but the slopes of the fit between the SABER and rotational temperatures are not equal to 1. The rotational temperatures calculated using each set of Einstein coefficients produce a different bias with respect to SABER; these are evaluated over each of vibrational levels to assess the best match. It is concluded that rotational temperatures determined using any of the available Einstein coefficient datasets have systematic errors. However, of the five sets of coefficients, the rotational temperature derived with the Langhoff et al.'s (1986) set is most consistent with SABER. In order to get a set of optimal Einstein coefficients for rotational temperature derivation, we derive the relative values from ground-based OH spectra and SABER temperatures statistically using three year data. The use of a standard set of Einstein coefficients will be beneficial for comparing rotational temperatures observed at different sites.

Residual Shuffling Convolutional Neural Networks for Deep Semantic Image Segmentation Using Multi-Modal Data

NASA Astrophysics Data System (ADS)

Chen, K.; Weinmann, M.; Gao, X.; Yan, M.; Hinz, S.; Jutzi, B.; Weinmann, M.

2018-05-01

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation.

Multi-site assimilation of a terrestrial biosphere model (BETHY) using satellite derived soil moisture data

NASA Astrophysics Data System (ADS)

Wu, Mousong; Sholze, Marko

2017-04-01

We investigated the importance of soil moisture data on assimilation of a terrestrial biosphere model (BETHY) for a long time period from 2010 to 2015. Totally, 101 parameters related to carbon turnover, soil respiration, as well as soil texture were selected for optimization within a carbon cycle data assimilation system (CCDAS). Soil moisture data from Soil Moisture and Ocean Salinity (SMOS) product was derived for 10 sites representing different plant function types (PFTs) as well as different climate zones. Uncertainty of SMOS soil moisture data was also estimated using triple collocation analysis (TCA) method by comparing with ASCAT dataset and BETHY forward simulation results. Assimilation of soil moisture to the system improved soil moisture as well as net primary productivity(NPP) and net ecosystem productivity (NEP) when compared with soil moisture derived from in-situ measurements and fluxnet datasets. Parameter uncertainties were largely reduced relatively to prior values. Using SMOS soil moisture data for assimilation of a terrestrial biosphere model proved to be an efficient approach in reducing uncertainty in ecosystem fluxes simulation. It could be further used in regional an global assimilation work to constrain carbon dioxide concentration simulation by combining with other sources of measurements.

Variability of basin scale water resources indicators derived from global hydrological and land surface models

NASA Astrophysics Data System (ADS)

Werner, Micha; Blyth, Eleanor; Schellekens, Jaap

2016-04-01

Global hydrological and land-surface models are becoming increasingly available, and as the resolution of these improves, as well how hydrological processes are represented, so does their potential. These offer consistent datasets at the global scale, which can be used to establish water balances and derive policy relevant indicators in medium to large basins, including those that are poorly gauged. However, differences in model structure, model parameterisation, and model forcing may result in quite different indicator values being derived, depending on the model used. In this paper we explore indicators developed using four land surface models (LSM) and five global hydrological models (GHM). Results from these models have been made available through the Earth2Observe project, a recent research initiative funded by the European Union 7th Research Framework. All models have a resolution of 0.5 arc degrees, and are forced using the same WATCH-ERA-Interim (WFDEI) meteorological re-analysis data at a daily time step for the 32 year period from 1979 to 2012. We explore three water resources indicators; an aridity index, a simplified water exploitation index; and an indicator that calculates the frequency of occurrence of root zone stress. We compare indicators derived over selected areas/basins in Europe, Colombia, Southern Africa, the Indian Subcontinent and Australia/New Zealand. The hydrological fluxes calculated show quite significant differences between the nine models, despite the common forcing dataset, with these differences reflected in the indicators subsequently derived. The results show that the variability between models is related to the different climates types, with that variability quite logically depending largely on the availability of water. Patterns are also found in the type of models that dominate different parts of the distribution of the indicator values, with LSM models providing lower values, and GHM models providing higher values in some climates, and vice versa in others. How important this variability is in supporting a policy decision, depends largely on how a decision thresholds are set. For example in the case of the aridity index, with areas being denoted as arid with an index of 0.6 or above, we show that the variability is primarily of interest in transitional climates, such as the Mediterranean The analysis shows that while both LSM's and GHM's provide useful data, indices derived to support water resources management planning may differ substantially, depending on the model used. The analysis also identifies in which climates improvements to the models are particularly relevant to support the confidence with which decisions can be taken based on derived indicators.

Co-pyrolyzing plastic mulch waste with animal manures

USDA-ARS?s Scientific Manuscript database

Pyrolyzing various livestock and agricultural wastes produces power and value-added byproducts. It also substantially reduces ultimate waste volume to be disposed of and improves soil fertility and promotes carbon sequestration via soil application of biochar. Researchers found that manure-derived ...

Natural background groundwater composition in the Azores archipelago (Portugal): a hydrogeochemical study and threshold value determination.

PubMed

Cruz, J V; Andrade, C

2015-07-01

Groundwater discharges were sampled in selected springs from São Miguel (Furnas and Fogo trachytic central volcanoes) and Santa Maria islands (Azores, Portugal), in order to characterize natural background levels (NBLs) and proceed to the determination of threshold values (TVs). Besides being a key issue in order to fully assess the anthropogenic pressures, NBLs are also instrumental to derive TVs, therefore complying with requirements from the European Union Groundwater Directive. The composition of groundwater corresponds mainly to low mineralized Na-HCO3 to Na-Cl water types, the latter dominant in Santa Maria island, with a decreasing order of Na>Ca>Mg>K and Cl>HCO3>SO4>NO3 for cations and anion respectively. The majority of the samples are slightly acid to slightly alkaline (pH range of 5.45-7.43), and the electrical conductivity range between 180 and 1458 μS/cm. Groundwater composition is controlled by two major drivers, addition of sea salts and dissolution of silicate minerals. Results shown that TVs established along the present study are in general in the lower rank when compared to the range of values proposed by the several EU member states, with the main exception of NO3, reflecting the impact of agriculture activities over water quality in the Azores, and lower than the national ones. The comparison between the estimated NBL and TV with values derived with another dataset from the Azores, usually higher, depicts the effect of a larger and diverse number of groundwater sources over calculations. On the other hand, all samples which show a contribution from volcanic/hydrothermal systems were excluded from the dataset, which explains why the derived NBLs and TVs are lower comparing to other active volcanic areas, which is also a conservative approach on a subject that has regulatory implications. Copyright © 2015 Elsevier B.V. All rights reserved.

Challenges in Collating Spirometry Reference Data for South-Asian Children: An Observational Study.

PubMed

Lum, Sooky; Bountziouka, Vassiliki; Quanjer, Philip; Sonnappa, Samatha; Wade, Angela; Beardsmore, Caroline; Chhabra, Sunil K; Chudasama, Rajesh K; Cook, Derek G; Harding, Seeromanie; Kuehni, Claudia E; Prasad, K V V; Whincup, Peter H; Lee, Simon; Stocks, Janet

2016-01-01

Spirometry datasets from South-Asian children were collated from four centres in India and five within the UK. Records with transcription errors, missing values for height or spirometry, and implausible values were excluded(n = 110). Following exclusions, cross-sectional data were available from 8,124 children (56.3% male; 5-17 years). When compared with GLI-predicted values from White Europeans, forced expired volume in 1s (FEV1) and forced vital capacity (FVC) in South-Asian children were on average 15% lower, ranging from 4-19% between centres. By contrast, proportional reductions in FEV1 and FVC within all but two datasets meant that the FEV1/FVC ratio remained independent of ethnicity. The 'GLI-Other' equation fitted data from North India reasonably well while 'GLI-Black' equations provided a better approximation for South-Asian data than the 'GLI-White' equation. However, marked discrepancies in the mean lung function z-scores between centres especially when examined according to socio-economic conditions precluded derivation of a single South-Asian GLI-adjustment. Until improved and more robust prediction equations can be derived, we recommend the use of 'GLI-Black' equations for interpreting most South-Asian data, although 'GLI-Other' may be more appropriate for North Indian data. Prospective data collection using standardised protocols to explore potential sources of variation due to socio-economic circumstances, secular changes in growth/predictors of lung function and ethnicities within the South-Asian classification are urgently required.

Five centuries of Czech May-June precipitation and drought variability inferred from instrumental measurements, tree rings and documentary archives

NASA Astrophysics Data System (ADS)

Brázdil, R.; Büntgen, U.; Dobrovolný, P.; Trnka, M.; Kyncl, T.

2010-09-01

Precipitation is one of the most important meteorological elements for different natural processes as well as for human society. Its long term fluctuations in the Czech Lands (recent Czech Republic) can be studied using long instrumental series (Brno since January 1803, Prague-Klementinum since May 1804), a tree-ring chronology from southern Moravian fir Abies alba Mill. developed from living and historical trees (since A.D. 1376), and monthly precipitation indices derived from documentary evidence (from A.D. 1500). The analysis focuses on May-June precipitation and drought patterns represented by the Z-index for the past 500 years showing the highest response of the tree-ring chronology to the mentioned months in the calibration/verification period between 1803 and 1932. Tree-ring and documentary-based May-June Z-index reconstructions explaining ca 30-40% of its variability are compared with existing reconstructions of hydroclimatic patterns of the Central European region. Uncertainties of tree-ring and documentary datasets and corresponding reconstructions are discussed.

Loops in AdS from conformal field theory

DOE PAGES

Aharony, Ofer; Alday, Luis F.; Bissi, Agnese; ...

2017-07-10

We propose and demonstrate a new use for conformal field theory (CFT) crossing equations in the context of AdS/CFT: the computation of loop amplitudes in AdS, dual to non-planar correlators in holographic CFTs. Loops in AdS are largely unexplored, mostly due to technical difficulties in direct calculations. We revisit this problem, and the dual 1=N expansion of CFTs, in two independent ways. The first is to show how to explicitly solve the crossing equations to the first subleading order in 1=N 2, given a leading order solution. This is done as a systematic expansion in inverse powers of the spin, to all orders. These expansions can be resummed, leading to the CFT data for nite values of the spin. Our second approach involves Mellin space. We show how the polar part of the four-point, loop-level Mellin amplitudes can be fully reconstructed from the leading-order data. The anomalous dimensions computed with both methods agree. In the case ofmore » $$\\phi$$ 4 theory in AdS, our crossing solution reproduces a previous computation of the one-loop bubble diagram. We can go further, deriving the four-point scalar triangle diagram in AdS, which had never been computed. In the process, we show how to analytically derive anomalous dimensions from Mellin amplitudes with an in nite series of poles, and discuss applications to more complicated cases such as the N = 4 super-Yang-Mills theory.« less

Loops in AdS from conformal field theory

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

Aharony, Ofer; Alday, Luis F.; Bissi, Agnese

We propose and demonstrate a new use for conformal field theory (CFT) crossing equations in the context of AdS/CFT: the computation of loop amplitudes in AdS, dual to non-planar correlators in holographic CFTs. Loops in AdS are largely unexplored, mostly due to technical difficulties in direct calculations. We revisit this problem, and the dual 1=N expansion of CFTs, in two independent ways. The first is to show how to explicitly solve the crossing equations to the first subleading order in 1=N 2, given a leading order solution. This is done as a systematic expansion in inverse powers of the spin, to all orders. These expansions can be resummed, leading to the CFT data for nite values of the spin. Our second approach involves Mellin space. We show how the polar part of the four-point, loop-level Mellin amplitudes can be fully reconstructed from the leading-order data. The anomalous dimensions computed with both methods agree. In the case ofmore » $$\\phi$$ 4 theory in AdS, our crossing solution reproduces a previous computation of the one-loop bubble diagram. We can go further, deriving the four-point scalar triangle diagram in AdS, which had never been computed. In the process, we show how to analytically derive anomalous dimensions from Mellin amplitudes with an in nite series of poles, and discuss applications to more complicated cases such as the N = 4 super-Yang-Mills theory.« less

Loops in AdS from conformal field theory

NASA Astrophysics Data System (ADS)

Aharony, Ofer; Alday, Luis F.; Bissi, Agnese; Perlmutter, Eric

2017-07-01

We propose and demonstrate a new use for conformal field theory (CFT) crossing equations in the context of AdS/CFT: the computation of loop amplitudes in AdS, dual to non-planar correlators in holographic CFTs. Loops in AdS are largely unexplored, mostly due to technical difficulties in direct calculations. We revisit this problem, and the dual 1 /N expansion of CFTs, in two independent ways. The first is to show how to explicitly solve the crossing equations to the first subleading order in 1 /N 2, given a leading order solution. This is done as a systematic expansion in inverse powers of the spin, to all orders. These expansions can be resummed, leading to the CFT data for finite values of the spin. Our second approach involves Mellin space. We show how the polar part of the four-point, loop-level Mellin amplitudes can be fully reconstructed from the leading-order data. The anomalous dimensions computed with both methods agree. In the case of ϕ 4 theory in AdS, our crossing solution reproduces a previous computation of the one-loop bubble diagram. We can go further, deriving the four-point scalar triangle diagram in AdS, which had never been computed. In the process, we show how to analytically derive anomalous dimensions from Mellin amplitudes with an infinite series of poles, and discuss applications to more complicated cases such as the N = 4 super-Yang-Mills theory.

Design of a High Resolution Open Access Global Snow Cover Web Map Service Using Ground and Satellite Observations

NASA Astrophysics Data System (ADS)

Kadlec, J.; Ames, D. P.

2014-12-01

The aim of the presented work is creating a freely accessible, dynamic and re-usable snow cover map of the world by combining snow extent and snow depth datasets from multiple sources. The examined data sources are: remote sensing datasets (MODIS, CryoLand), weather forecasting model outputs (OpenWeatherMap, forecast.io), ground observation networks (CUAHSI HIS, GSOD, GHCN, and selected national networks), and user-contributed snow reports on social networks (cross-country and backcountry skiing trip reports). For adding each type of dataset, an interface and an adapter is created. Each adapter supports queries by area, time range, or combination of area and time range. The combined dataset is published as an online snow cover mapping service. This web service lowers the learning curve that is required to view, access, and analyze snow depth maps and snow time-series. All data published by this service are licensed as open data; encouraging the re-use of the data in customized applications in climatology, hydrology, sports and other disciplines. The initial version of the interactive snow map is on the website snow.hydrodata.org. This website supports the view by time and view by site. In view by time, the spatial distribution of snow for a selected area and time period is shown. In view by site, the time-series charts of snow depth at a selected location is displayed. All snow extent and snow depth map layers and time series are accessible and discoverable through internationally approved protocols including WMS, WFS, WCS, WaterOneFlow and WaterML. Therefore they can also be easily added to GIS software or 3rd-party web map applications. The central hypothesis driving this research is that the integration of user contributed data and/or social-network derived snow data together with other open access data sources will result in more accurate and higher resolution - and hence more useful snow cover maps than satellite data or government agency produced data by itself.

Dual functional cholinesterase and MAO inhibitors for the treatment of Alzheimer's disease: synthesis, pharmacological analysis and molecular modeling of homoisoflavonoid derivatives.

PubMed

Wang, Yali; Sun, Yang; Guo, Yueyan; Wang, Zechen; Huang, Ling; Li, Xingshu

2016-01-01

Because of the complexity of Alzheimer's disease (AD), the multi-target-directed ligand (MTDL) strategy is expected to provide superior effects for the treatment of AD, instead of the classic one-drug-one-target strategy. In this context, we focused on the design, synthesis and evaluation of homoisoflavonoid derivatives as dual acetyl cholinesterase (AChE) and monoamine oxidase (MAO-B) inhibitors. Among all the synthesized compounds, compound 10 provided a desired balance of AChE and hMAO-B inhibition activities, with IC50 value of 3.94 and 3.44 μM, respectively. Further studies revealed that compound 10 was a mixed-type inhibitor of AChE and an irreversible inhibitor of hMAO-B, which was also confirmed by molecular modeling studies. Taken together, the data indicated that 10 was a promising dual functional agent for the treatment of AD.

On the added value of WUDAPT for Urban Climate Modelling

NASA Astrophysics Data System (ADS)

Brousse, Oscar; Martilli, Alberto; Mills, Gerald; Bechtel, Benjamin; Hammerberg, Kris; Demuzere, Matthias; Wouters, Hendrik; Van Lipzig, Nicole; Ren, Chao; Feddema, Johannes J.; Masson, Valéry; Ching, Jason

2017-04-01

Over half of the planet's population now live in cities and is expected to grow up to 65% by 2050 (United Nations, 2014), most of whom will actually occupy new emerging cities of the global South. Cities' impact on climate is known to be a key driver of environmental change (IPCC, 2014) and has been studied for decades now (Howard, 1875). Still very little is known about our cities' structure around the world, preventing urban climate simulations to be done and hence guidance to be provided for mitigation. Assessing the need to bridge the urban knowledge gap for urban climate modelling perspectives, the World Urban Database and Access Portal Tool - WUDAPT - project (Ching et al., 2015; Mills et al., 2015) developed an innovative technique to map cities globally rapidly and freely. The framework established by Bechtel and Daneke (2012) derives Local Climate Zones (Stewart and Oke, 2012) city maps out of LANDSAT 8 OLI-TIRS imagery (Bechtel et al., 2015) through a supervised classification by a Random Forest Classification algorithm (Breiman, 2001). The first attempt to implement Local Climate Zones (LCZ) out of the WUDAPT product within a major climate model was carried out by Brousse et al. (2016) over Madrid, Spain. This study proved the applicability of LCZs as an enhanced urban parameterization within the WRF model (Chen et al. 2011) employing the urban canopy model BEP-BEM (Martilli, 2002; Salamanca et al., 2010), using the averaged values of the morphological and physical parameters' ranges proposed by Stewart and Oke (2012). Other studies have now used the Local Climate Zones for urban climate modelling purposes (Alexander et al., 2016; Wouters et al. 2016; Hammerberg et al., 2017; Brousse et al., 2017) and demonstrated the added value of the WUDAPT dataset. As urban data accessibility is one of the major challenge for simulations in emerging countries, this presentation will show results of simulations using LCZs and the capacity of the WUDAPT framework to be of high relevancy in multiple regions of the world, such as Africa or Asia.

Metabolic parameters linked by phenotype microarray to acid resistance profiles of poultry-associated Salmonella enterica.

PubMed

Guard, Jean; Rothrock, Michael J; Shah, Devendra H; Jones, Deana R; Gast, Richard K; Sanchez-Ingunza, Roxana; Madsen, Melissa; El-Attrache, John; Lungu, Bwalya

Phenotype microarrays were analyzed for 51 datasets derived from Salmonella enterica. The top 4 serotypes associated with poultry products and one associated with turkey, respectively Typhimurium, Enteritidis, Heidelberg, Infantis and Senftenberg, were represented. Datasets were partitioned initially into two clusters based on ranking by values at pH 4.5 (PM10 A03). Negative control wells were used to establish 90 respiratory units as the point differentiating acid resistance from sensitive strains. Thus, 24 isolates that appeared most acid-resistant were compared initially to 27 that appeared most acid-sensitive (24 × 27 format). Paired cluster analysis was also done and it included the 7 most acid-resistant and -sensitive datasets (7 × 7 format). Statistical analyses of ranked data were then calculated in order of standard deviation, probability value by the Student's t-test and a measure of the magnitude of difference called effect size. Data were reported as significant if, by order of filtering, the following parameters were calculated: i) a standard deviation of 24 respiratory units or greater from all datasets for each chemical, ii) a probability value of less than or equal to 0.03 between clusters and iii) an effect size of at least 0.50 or greater between clusters. Results suggest that between 7.89% and 23.16% of 950 chemicals differentiated acid-resistant isolates from sensitive ones, depending on the format applied. Differences were more evident at the extremes of phenotype using the subset of data in the paired 7 × 7 format. Results thus provide a strategy for selecting compounds for additional research, which may impede the emergence of acid-resistant Salmonella enterica in food. Published by Elsevier Masson SAS.

A zeta potential value determines the aggregate's size of penta-substituted [60]fullerene derivatives in aqueous suspension whereas positive charge is required for toxicity against bacterial cells.

PubMed

Deryabin, Dmitry G; Efremova, Ludmila V; Vasilchenko, Alexey S; Saidakova, Evgeniya V; Sizova, Elena A; Troshin, Pavel A; Zhilenkov, Alexander V; Khakina, Ekaterina A; Khakina, Ekaterina E

2015-08-08

The cause-effect relationships between physicochemical properties of amphiphilic [60]fullerene derivatives and their toxicity against bacterial cells have not yet been clarified. In this study, we report how the differences in the chemical structure of organic addends in 10 originally synthesized penta-substituted [60]fullerene derivatives modulate their zeta potential and aggregate's size in salt-free and salt-added aqueous suspensions as well as how these physicochemical characteristics affect the bioenergetics of freshwater Escherichia coli and marine Photobacterium phosphoreum bacteria. Dynamic light scattering, laser Doppler micro-electrophoresis, agarose gel electrophoresis, atomic force microscopy, and bioluminescence inhibition assay were used to characterize the fullerene aggregation behavior in aqueous solution and their interaction with the bacterial cell surface, following zeta potential changes and toxic effects. Dynamic light scattering results indicated the formation of self-assembled [60]fullerene aggregates in aqueous suspensions. The measurement of the zeta potential of the particles revealed that they have different surface charges. The relationship between these physicochemical characteristics was presented as an exponential regression that correctly described the dependence of the aggregate's size of penta-substituted [60]fullerene derivatives in salt-free aqueous suspension from zeta potential value. The prevalence of DLVO-related effects was shown in salt-added aqueous suspension that decreased zeta potential values and affected the aggregation of [60]fullerene derivatives expressed differently for individual compounds. A bioluminescence inhibition assay demonstrated that the toxic effect of [60]fullerene derivatives against E. coli cells was strictly determined by their positive zeta potential charge value being weakened against P. phosphoreum cells in an aquatic system of high salinity. Atomic force microscopy data suggested that the activity of positively charged [60]fullerene derivatives against bacterial cells required their direct interaction. The following zeta potential inversion on the bacterial cells surface was observed as an early stage of toxicity mechanism that violates the membrane-associated energetic functions. The novel data about interrelations between physicochemical parameters and toxic properties of amphiphilic [60]fullerene derivatives make possible predicting their behavior in aquatic environment and their activity against bacterial cells.

Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm.

PubMed

Beheshti, Iman; Demirel, Hasan; Matsuda, Hiroshi

2017-04-01

We developed a novel computer-aided diagnosis (CAD) system that uses feature-ranking and a genetic algorithm to analyze structural magnetic resonance imaging data; using this system, we can predict conversion of mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) at between one and three years before clinical diagnosis. The CAD system was developed in four stages. First, we used a voxel-based morphometry technique to investigate global and local gray matter (GM) atrophy in an AD group compared with healthy controls (HCs). Regions with significant GM volume reduction were segmented as volumes of interest (VOIs). Second, these VOIs were used to extract voxel values from the respective atrophy regions in AD, HC, stable MCI (sMCI) and progressive MCI (pMCI) patient groups. The voxel values were then extracted into a feature vector. Third, at the feature-selection stage, all features were ranked according to their respective t-test scores and a genetic algorithm designed to find the optimal feature subset. The Fisher criterion was used as part of the objective function in the genetic algorithm. Finally, the classification was carried out using a support vector machine (SVM) with 10-fold cross validation. We evaluated the proposed automatic CAD system by applying it to baseline values from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (160 AD, 162 HC, 65 sMCI and 71 pMCI subjects). The experimental results indicated that the proposed system is capable of distinguishing between sMCI and pMCI patients, and would be appropriate for practical use in a clinical setting. Copyright © 2017 Elsevier Ltd. All rights reserved.

Green Processing Technologies for Improving Germinated Brown Rice Milk Beverages

USDA-ARS?s Scientific Manuscript database

Rice feeds approximately half the world’s population. Rice-derived beverages offer non-soy, lactose-free, hypoallergenic, cholesterol and gluten free value-added food sources. Rice milk beverages offer exceptional options for those with lactose intolerance, gluten sensitivities, obesity, heart dis...

Toward engineering E. coli with an autoregulatory system for lignin valorization.

PubMed

Wu, Weihua; Liu, Fang; Singh, Seema

2018-03-20

Efficient lignin valorization could add more than 10-fold the value gained from burning it for energy and is critical for economic viability of future biorefineries. However, lignin-derived aromatics from biomass pretreatment are known to be potent fermentation inhibitors in microbial production of fuels and other value-added chemicals. In addition, isopropyl-β-d-1-thiogalactopyranoside and other inducers are routinely added into fermentation broth to induce the expression of pathway enzymes, which further adds to the overall process cost. An autoregulatory system that can diminish the aromatics' toxicity as well as be substrate-inducible can be the key for successful integration of lignin valorization into future lignocellulosic biorefineries. Toward that goal, in this study an autoregulatory system is demonstrated that alleviates the toxicity issue and eliminates the cost of an external inducer. Specifically, this system is composed of a catechol biosynthesis pathway coexpressed with an active aromatic transporter CouP under induction by a vanillin self-inducible promoter, ADH7, to effectively convert the lignin-derived aromatics into value-added chemicals using Escherichia coli as a host. The constructed autoregulatory system can efficiently transport vanillin across the cell membrane and convert it to catechol. Compared with the system without CouP expression, the expression of catechol biosynthesis pathway with transporter CouP significantly improved the catechol yields about 30% and 40% under promoter pTrc and ADH7, respectively. This study demonstrated an aromatic-induced autoregulatory system that enabled conversion of lignin-derived aromatics into catechol without the addition of any costly, external inducers, providing a promising and economically viable route for lignin valorization. Copyright © 2018 the Author(s). Published by PNAS.

Principal component analysis of the cytokine and chemokine response to human traumatic brain injury.

PubMed

Helmy, Adel; Antoniades, Chrystalina A; Guilfoyle, Mathew R; Carpenter, Keri L H; Hutchinson, Peter J

2012-01-01

There is a growing realisation that neuro-inflammation plays a fundamental role in the pathology of Traumatic Brain Injury (TBI). This has led to the search for biomarkers that reflect these underlying inflammatory processes using techniques such as cerebral microdialysis. The interpretation of such biomarker data has been limited by the statistical methods used. When analysing data of this sort the multiple putative interactions between mediators need to be considered as well as the timing of production and high degree of statistical co-variance in levels of these mediators. Here we present a cytokine and chemokine dataset from human brain following human traumatic brain injury and use principal component analysis and partial least squares discriminant analysis to demonstrate the pattern of production following TBI, distinct phases of the humoral inflammatory response and the differing patterns of response in brain and in peripheral blood. This technique has the added advantage of making no assumptions about the Relative Recovery (RR) of microdialysis derived parameters. Taken together these techniques can be used in complex microdialysis datasets to summarise the data succinctly and generate hypotheses for future study.

Transethnic genome-wide scan identifies novel Alzheimer's disease loci.

PubMed

Jun, Gyungah R; Chung, Jaeyoon; Mez, Jesse; Barber, Robert; Beecham, Gary W; Bennett, David A; Buxbaum, Joseph D; Byrd, Goldie S; Carrasquillo, Minerva M; Crane, Paul K; Cruchaga, Carlos; De Jager, Philip; Ertekin-Taner, Nilufer; Evans, Denis; Fallin, M Danielle; Foroud, Tatiana M; Friedland, Robert P; Goate, Alison M; Graff-Radford, Neill R; Hendrie, Hugh; Hall, Kathleen S; Hamilton-Nelson, Kara L; Inzelberg, Rivka; Kamboh, M Ilyas; Kauwe, John S K; Kukull, Walter A; Kunkle, Brian W; Kuwano, Ryozo; Larson, Eric B; Logue, Mark W; Manly, Jennifer J; Martin, Eden R; Montine, Thomas J; Mukherjee, Shubhabrata; Naj, Adam; Reiman, Eric M; Reitz, Christiane; Sherva, Richard; St George-Hyslop, Peter H; Thornton, Timothy; Younkin, Steven G; Vardarajan, Badri N; Wang, Li-San; Wendlund, Jens R; Winslow, Ashley R; Haines, Jonathan; Mayeux, Richard; Pericak-Vance, Margaret A; Schellenberg, Gerard; Lunetta, Kathryn L; Farrer, Lindsay A

2017-07-01

Genetic loci for Alzheimer's disease (AD) have been identified in whites of European ancestry, but the genetic architecture of AD among other populations is less understood. We conducted a transethnic genome-wide association study (GWAS) for late-onset AD in Stage 1 sample including whites of European Ancestry, African-Americans, Japanese, and Israeli-Arabs assembled by the Alzheimer's Disease Genetics Consortium. Suggestive results from Stage 1 from novel loci were followed up using summarized results in the International Genomics Alzheimer's Project GWAS dataset. Genome-wide significant (GWS) associations in single-nucleotide polymorphism (SNP)-based tests (P < 5 × 10 -8 ) were identified for SNPs in PFDN1/HBEGF, USP6NL/ECHDC3, and BZRAP1-AS1 and for the interaction of the (apolipoprotein E) APOE ε4 allele with NFIC SNP. We also obtained GWS evidence (P < 2.7 × 10 -6 ) for gene-based association in the total sample with a novel locus, TPBG (P = 1.8 × 10 -6 ). Our findings highlight the value of transethnic studies for identifying novel AD susceptibility loci. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Differentiation of wood-derived vanillin from synthetic vanillin in distillates using gas chromatography/combustion/isotope ratio mass spectrometry for δ13 C analysis.

PubMed

van Leeuwen, Katryna A; Prenzler, Paul D; Ryan, Danielle; Paolini, Mauro; Camin, Federica

2018-02-28

Typical storage in oak barrels releases in distillates different degradation products such as vanillin, which play an important role in flavour and aroma. The addition of vanillin, as well as other aroma compounds, of different origin is prohibited by European laws. As vanillin samples from different sources have different δ 13 C values, the δ 13 C value could be used to determine whether the vanillin is authentic (lignin-derived), or if it has been added from another source (e.g. synthetic). The δ 13 C values for vanillin derived from different sources, including natural, synthetic and tannins, were measured by gas chromatography/combustion/isotope ratio mass spectrometry (GC/C/IRMS), after diethyl ether addition and/or ethanol dilution. A method for analysing vanillin in distillates after dichloromethane extraction was developed. Tests were undertaken to prove the reliability, reproducibility and accuracy of the method with standards and samples. Distillate samples were run to measure the δ 13 C values of vanillin and to compare them with values for other sources of vanillin. δ 13 C values were determined for: natural vanillin extracts (-21.0 to -19.3‰, 16 samples); vanillin ex-lignin (-28.2‰, 1 sample); and synthetic vanillin (-32.6 to -29.3‰, 7 samples). Seventeen tannin samples were found to have δ 13 C values of -29.5 to -26.7‰, which were significantly different (p < 0.05) from those of the natural and synthetic vanillins. The vanillin δ 13 C values measured in distillates (-28.9 to -25.7‰) were mainly in the tannin range, although one spirit (-32.5‰) was found to contain synthetic vanillin. The results show that synthetic vanillin added to a distillate could be differentiated from vanillin derived from oak barrels by their respective δ 13 C values. The GC/C/IRMS method could be a useful tool in the determination of adulteration of distillates. Copyright © 2017 John Wiley & Sons, Ltd.

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.

Compatibility of household budget and individual nutrition surveys: results of the preliminary analysis.

PubMed

Naska, A; Trichopoulou, A

2001-08-01

The EU-supported project entitled: "Compatibility of household budget and individual nutrition surveys and disparities in food habits" aimed at comparing individualised household budget survey (HBS) data with food consumption values derived from individual nutrition surveys (INS). The present paper provides a brief description of the methodology applied for rendering the datasets at a comparable level. Results of the preliminary evaluation of their compatibility are also presented. A non parametric modelling approach was used for the individualisation (age and gender-specific) of the food data collected at household level, in the context of the national HBSs and the bootstrap technique was used for the derivation of 95% confidence intervals. For each food group, INS and HBS-derived mean values were calculated for twenty-four research units, jointly defined by country (four countries involved), gender (male, female) and age (younger, middle-aged and older). Pearson correlation coefficients were calculated. The results of this preliminary analysis show that there is considerable scope in the nutritional information derived from HBSs. Additional and more sophisticated work is however required, putting particular emphasis on addressing limitations present in both surveys and on deriving reliable individual consumption point and interval estimates, on the basis of HBS data.

Image change detection using paradoxical theory for patient follow-up quantitation and therapy assessment.

PubMed

David, Simon; Visvikis, Dimitris; Quellec, Gwénolé; Le Rest, Catherine Cheze; Fernandez, Philippe; Allard, Michèle; Roux, Christian; Hatt, Mathieu

2012-09-01

In clinical oncology, positron emission tomography (PET) imaging can be used to assess therapeutic response by quantifying the evolution of semi-quantitative values such as standardized uptake value, early during treatment or after treatment. Current guidelines do not include metabolically active tumor volume (MATV) measurements and derived parameters such as total lesion glycolysis (TLG) to characterize the response to the treatment. To achieve automatic MATV variation estimation during treatment, we propose an approach based on the change detection principle using the recent paradoxical theory, which models imprecision, uncertainty, and conflict between sources. It was applied here simultaneously to pre- and post-treatment PET scans. The proposed method was applied to both simulated and clinical datasets, and its performance was compared to adaptive thresholding applied separately on pre- and post-treatment PET scans. On simulated datasets, the adaptive threshold was associated with significantly higher classification errors than the developed approach. On clinical datasets, the proposed method led to results more consistent with the known partial responder status of these patients. The method requires accurate rigid registration of both scans which can be obtained only in specific body regions and does not explicitly model uptake heterogeneity. In further investigations, the change detection of intra-MATV tracer uptake heterogeneity will be developed by incorporating textural features into the proposed approach.

Reaction pathways of biomass-derived oxygenates on noble metal surfaces

NASA Astrophysics Data System (ADS)

McManus, Jesse R.

As the global demand for energy continues to rise, the environmental concerns associated with increased fossil fuel consumption have motivated the use of biomass as an alternative, carbon-renewable energy feedstock. Controlling reactive chemistry of the sugars that comprise biomass through the use of catalysis becomes essential in effectively producing green fuels and value-added chemicals. Recent work on biomass conversion catalysts have demonstrated the efficacy of noble metal catalyst systems for the reforming of biomass to hydrogen fuel, and the hydrodeoxygenation of biomass-derived compounds to value-added chemicals. In particular, Pt and Pd surfaces have shown considerable promise as reforming catalysts in preliminary aqueous phase reforming studies. It becomes important to understand the mechanisms by which these molecules react on the catalyst surfaces in order to determine structure-activity relationships and bond scission energetics as to provide a framework for engineering more active and selective catalysts. Fundamental surface science techniques provide the tools to do this; however, work in this field has been so far limited to simple model molecules like ethanol and ethylene glycol. Herein, temperature programmed desorption and high resolution electron energy loss spectroscopy are utilized in an ultra-high vacuum surface science study of the biomass-derived sugar glucose on Pt and Pd single crystal catalysts. Overall, it was determined that the aldehyde function of a ring-open glucose molecule plays an integral part in the initial bonding and reforming reaction pathway, pointing to the use of aldoses glycolaldehyde and glyceraldehyde as the most appropriate model compounds for future studies. Furthermore, the addition of adatom Zn to a Pt(111) surface was found to significantly decrease the C-H and C-C bond scission activity in aldehyde containing compounds, resulting in a preferred deoxygenation pathway in opposition to the decarbonylation pathway common on clean Pt(111). This has implications in the hydrodeoxygenation of biomass-derived compounds for the production of value-added chemicals like 2-methylfuran from furfural, or the catalytic upgrading of sugars. Ultimately, identification of the reactive mechanisms of biomass-derived molecules on different unique surfaces has lead to a greater understanding for what makes a more selective catalyst for specific chemical pathways.

Estimating the brain pathological age of Alzheimer’s disease patients from MR image data based on the separability distance criterion

NASA Astrophysics Data System (ADS)

Li, Yongming; Li, Fan; Wang, Pin; Zhu, Xueru; Liu, Shujun; Qiu, Mingguo; Zhang, Jingna; Zeng, Xiaoping

2016-10-01

Traditional age estimation methods are based on the same idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to accelerated brain aging. This paper considers this deviation and searches for it by maximizing the separability distance value rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to prior knowledge. Secondly, use the support vector regression (SVR) as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the separability distance criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. The experimental results showed that the separability was apparently improved. For normal control-Alzheimer’s disease (NC-AD), normal control-mild cognition impairment (NC-MCI), and MCI-AD, the average improvements were 0.178 (35.11%), 0.033 (14.47%), and 0.017 (39.53%), respectively. For NC-MCI-AD, the average improvement was 0.2287 (64.22%). The estimated brain pathological age could be not only more helpful to the classification of AD but also more precisely reflect accelerated brain aging. In conclusion, this paper offers a new method for brain age estimation that can distinguish different states of AD and can better reflect the extent of accelerated aging.

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

NASA Astrophysics Data System (ADS)

Selkowitz, David J.; Forster, Richard R.

2016-07-01

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s-1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010-2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987-1988 and 2008-2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25-50 years.

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard R.

2016-01-01

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s–1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010–2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987–1988 and 2008–2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25–50 years.

INFOMAR, Ireland's National Seabed Mapping Programme; Sharing Valuable Insights.

NASA Astrophysics Data System (ADS)

Judge, M. T.; McGrath, F.; Cullen, S.; Verbruggen, K.

2017-12-01

Following the successful high-resolution deep-sea mapping carried out as part of the Irish National Seabed Survey (INSS), a strategic, long term programme was established: INtegrated mapping FOr the sustainable development of Ireland MArine Resources (INFOMAR). Funded by Ireland's Department of Communication, Climate Action and Environment, INFOMAR comprises a multi-platform approach to completing Ireland's marine mapping, and is a key action in the integrated marine plan, Harnessing Our Ocean Wealth. Co-managed by Geological Survey Ireland and the Marine Institute, the programme has three work strands: Data Acquisition; Data Exchange and Integration; Value Added Exploitation.The Data Acquisition strand includes collection of geological, hydrographic, oceanographic, habitat and heritage datasets that underpin sustainable development and management of Ireland's marine resources. INFOMAR operates a free data policy; data and outputs are delivered online through the Data Exchange and Integration strand. Uses of data and outputs are wide-ranging and multipurpose. In order to address the evolution and diversification of user requirements, further data product development is facilitated through the Value Added Exploitation strand.Ninety percent of Ireland's territory lies offshore. Therefore, strategic national seabed mapping continues to provide critical, high-resolution baseline datasets for numerous economic sectors and societal needs. From these we can glean important geodynamic knowledge of Ireland's vast maritime territory. INFOMAR remains aligned with national and European policies and directives. Exemplified by our commitment to EMODnet, a European Commission funded project that supports the collection, standardisation and sharing of available marine information, data and data products across all European Seas. As EMODnet Geology Minerals leaders we have developed a framework for mapping marine minerals. Furthermore, collaboration with the international research project NAGTEC has unlocked the value of Irish marine data as an important jigsaw piece in the new atlas detailing the tectonostratigraphic evolution of the North-East Atlantic, with emphasis on conjugate margin comparisons.

A multi-centennial time series of well-constrained ΔR values for the Irish Sea derived using absolutely-dated shell samples from the mollusc Arctica islandica

NASA Astrophysics Data System (ADS)

Butler, P. G.; Scourse, J. D.; Richardson, C. A.; Wanamaker, A. D., Jr.

2009-04-01

Determinations of the local correction (ΔR) to the globally averaged marine radiocarbon reservoir age are often isolated in space and time, derived from heterogeneous sources and constrained by significant uncertainties. Although time series of ΔR at single sites can be obtained from sediment cores, these are subject to multiple uncertainties related to sedimentation rates, bioturbation and interspecific variations in the source of radiocarbon in the analysed samples. Coral records provide better resolution, but these are available only for tropical locations. It is shown here that it is possible to use the shell of the long-lived bivalve mollusc Arctica islandica as a source of high resolution time series of absolutely-dated marine radiocarbon determinations for the shelf seas surrounding the North Atlantic ocean. Annual growth increments in the shell can be crossdated and chronologies can be constructed in a precise analogue with the use of tree-rings. Because the calendar dates of the samples are known, ΔR can be determined with high precision and accuracy and because all the samples are from the same species, the time series of ΔR values possesses a high degree of internal consistency. Presented here is a multi-centennial (AD 1593 - AD 1933) time series of 31 ΔR values for a site in the Irish Sea close to the Isle of Man. The mean value of ΔR (-62 14C yrs) does not change significantly during this period but increased variability is apparent before AD 1750.

INFOMAR - Ireland's National Seabed Mapping Programme: A Tool For Marine Spatial Planning

NASA Astrophysics Data System (ADS)

Furey, T. M.

2016-02-01

INFOMAR is Ireland's national seabed mapping programme and is a key action in the national integrated marine plan, Harnessing Our Ocean Wealth. It comprises a multi-platform approach to delivering marine integrated mapping in 2 phases, over a projected 20 year timeline (2006-2026). The programme has three work strands; Data Acquisition, Data Exchange and Integration, and Value Added Exploitation. The Data Acquisition strand includes collection of hydrographic, oceanographic, geological, habitat and heritage datasets that will underpin future sustainable development and management of Ireland's marine resource. INFOMAR outputs are delivered through the Data Exchange and Integration strand. Uses of these outputs are wide ranging and multipurpose, from management plans for fisheries, aquaculture and coastal protection works, to environmental impact assessments, ocean renewable development and integrated coastal zone management. In order to address the evolution and diversification of maritime user requirements, the programme has realigned and developed outputs and new products, in part, through an innovative research funding initiative. Development is also fostered through the Value Added Exploitation strand. INFOMAR outputs and products serve to underpin delivery of Ireland's statutory obligations and enhance compliance with EU and national legislation. This is achieved through co-operation with the agencies responsible for supporting Ireland's international obligations and for the implementation of marine spatial planning. A strategic national seabed mapping programme such as INFOMAR, provides a critical baseline dataset which underpins development of the marine economy, and improves our understanding of the response of marine systems to pressures, and the effect of cumulative impacts. This paper will focus on the evolution and scope of INFOMAR, and look at examples of outputs being harnessed to serve approaches to the management of activities having an impact on the marine environment.

Geomagnetic paleointensity between 1300 and 1750 A.D. derived from a bread oven floor sequence in Lübeck, Germany

NASA Astrophysics Data System (ADS)

Schnepp, Elisabeth; Lanos, Philippe; Chauvin, Annick

2009-08-01

Geomagnetic paleointensities have been determined from a single archaeological site in Lübeck, Germany, where a sequence of 25 bread oven floors has been preserved in a bakery from medieval times until today. Age dating confines the time interval from about 1300 A.D. to about 1750 A.D. Paleomagnetic directions have been published from each oven floor and are updated here. The specimens have very stable directions and no or only weak secondary components. The oven floor material was characterized rock magnetically using Thellier viscosity indices, median destructive field values, Curie point determinations, and hysteresis measurements. Magnetic carriers are mixtures of SD, PSD, and minor MD magnetite and/or maghemite together with small amounts of hematite. Paleointensity was measured from selected specimens with the double-heating Thellier method including pTRM checks and determination of TRM anisotropy tensors. Corrections for anisotropy as well as for cooling rate turned out to be unnecessary. Ninety-two percent of the Thellier experiments passed the assigned acceptance criteria and provided four to six reliable paleointensity estimates per oven floor. Mean paleointensity values derived from 22 oven floors show maxima in the 15th and early 17th centuries A.D., followed by a decrease of paleointensity of about 20% until 1750 A.D. Together with the directions the record represents about 450 years of full vector secular variation. The results compare well with historical models of the Earth's magnetic field as well as with a selected high-quality paleointensity data set for western and central Europe.

Intrinsic functional component analysis via sparse representation on Alzheimer's disease neuroimaging initiative database.

PubMed

Jiang, Xi; Zhang, Xin; Zhu, Dajiang

2014-10-01

Alzheimer's disease (AD) is the most common type of dementia (accounting for 60% to 80%) and is the fifth leading cause of death for those people who are 65 or older. By 2050, one new case of AD in United States is expected to develop every 33 sec. Unfortunately, there is no available effective treatment that can stop or slow the death of neurons that causes AD symptoms. On the other hand, it is widely believed that AD starts before development of the associated symptoms, so its prestages, including mild cognitive impairment (MCI) or even significant memory concern (SMC), have received increasing attention, not only because of their potential as a precursor of AD, but also as a possible predictor of conversion to other neurodegenerative diseases. Although these prestages have been defined clinically, accurate/efficient diagnosis is still challenging. Moreover, brain functional abnormalities behind those alterations and conversions are still unclear. In this article, by developing novel sparse representations of whole-brain resting-state functional magnetic resonance imaging signals and by using the most updated Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we successfully identified multiple functional components simultaneously, and which potentially represent those intrinsic functional networks involved in the resting-state activities. Interestingly, these identified functional components contain all the resting-state networks obtained from traditional independent-component analysis. Moreover, by using the features derived from those functional components, it yields high classification accuracy for both AD (94%) and MCI (92%) versus normal controls. Even for SMC we can still have 92% accuracy.

Terrestrial Ecosystems - Topographic Moisture Potential of the Conterminous United States

USGS Publications Warehouse

Cress, Jill J.; Sayre, Roger G.; Comer, Patrick; Warner, Harumi

2009-01-01

As part of an effort to map terrestrial ecosystems, the U.S. Geological Survey has generated topographic moisture potential classes to be used in creating maps depicting standardized, terrestrial ecosystem models for the conterminous United States, using an ecosystems classification developed by NatureServe. A biophysical stratification approach, developed for South America and now being implemented globally, was used to model the ecosystem distributions. Substrate moisture regimes strongly influence the differentiation and distribution of terrestrial ecosystems, and therefore topographic moisture potential is one of the key input layers in this biophysical stratification. The method used to produce these topographic moisture potential classes was based on the derivation of ground moisture potential using a combination of computed topographic characteristics (CTI, slope, and aspect) and mapped National Wetland Inventory (NWI) boundaries. This method does not use climate or soil attributes to calculate relative topographic moisture potential since these characteristics are incorporated into the ecosystem model though other input layers. All of the topographic data used for this assessment were derived from the USGS 30-meter National Elevation Dataset (NED ) including the National Compound Topographic Index (CTI). The CTI index is a topographically derived measure of slope for a raster cell and the contributing area from upstream raster cells, and thus expresses potential for water flow to a point. In other words CTI data are 'a quantification of the position of a site in the local landscape', where the lowest values indicate ridges and the highest values indicate stream channels, lakes and ponds. These CTI values were compared to independent estimates of water accumulation by obtaining geospatial data from a number of sample locations representing two types of NWI boundaries: freshwater emergent wetlands and freshwater forested/shrub wetlands. Where these shorelines (the interface between the NWI wetlands and adjacent land) occurred, the CTI values were extracted and a histogram of their statistical distributions was calculated. Based on an evaluation of these histograms, CTI thresholds were developed to separate periodically saturated or flooded land, mesic uplands (moderately moist), and uplands. After the range of CTI values for these three different substrate moisture regimes was determined, the CTI values were grouped into three initial topographic moisture potential classes. As a final step in the generation of this national data layer, the uplands classification was subdivided into either very dry uplands or dry uplands. Very dry uplands were defined as uplands with relatively steep, south-facing slopes, and identification of this class was based on the slope and aspect datasets derived from the NED. The remaining uplands that did not meet these additional criteria were simply re-classified as dry uplands. The final National Topographic Moisture Potential dataset for the conterminous United States contains four classes: periodically saturated or flooded land (CTI = 18.5), mesic uplands (12 = 24 degrees and 91 degrees =< Aspect =< 314 degrees). This map shows a smoothed and generalized image of the four topographic moisture potential classes. Additional information about this map and any of the data developed for the ecosystems modeling of the conterminous United States is available online at http://rmgsc.cr.usgs.gov/ecosystems/.

Multi-temporal AirSWOT elevations on the Willamette river: error characterization and algorithm testing

NASA Astrophysics Data System (ADS)

Tuozzolo, S.; Frasson, R. P. M.; Durand, M. T.

2017-12-01

We analyze a multi-temporal dataset of in-situ and airborne water surface measurements from the March 2015 AirSWOT field campaign on the Willamette River in Western Oregon, which included six days of AirSWOT flights over a 75km stretch of the river. We examine systematic errors associated with dark water and layover effects in the AirSWOT dataset, and test the efficacies of different filtering and spatial averaging techniques at reconstructing the water surface profile. Finally, we generate a spatially-averaged time-series of water surface elevation and water surface slope. These AirSWOT-derived reach-averaged values are ingested in a prospective SWOT discharge algorithm to assess its performance on SWOT-like data collected from a borderline SWOT-measurable river (mean width = 90m).

VizieR Online Data Catalog: 231 transiting planets eccentricity and mass (Bonomo+, 2017)

NASA Astrophysics Data System (ADS)

Bonomo, A. S.; Desidera, S.; Benatti, S.; Borsa, F.; Crespi, S.; Damasso, M.; Lanza, A. F.; Sozzetti, A.; Lodato, G.; Marzari, F.; Boccato, C.; Claudi, R. U.; Cosentino, R.; Covino, E.; Gratton, R.; Maggio, A.; Micela, G.; Molinari, E.; Pagano, I.; Piotto, G.; Poretti, E.; Smareglia, R.; Affer, L.; Biazzo, K.; Bignamini, A.; Esposito, M.; Giacobbe, P.; Hebrard, G.; Malavolta, L.; Maldonado, J.; Mancini, L.; Martinez Fiorenzano, A.; Masiero, S.; Nascimbeni, V.; Pedani, M.; Rainer, M.; Scandariato, G.

2017-04-01

We carried out a homogeneous determination of the orbital parameters of 231 TGPs by analysing with our Bayesian DEMCMC tool both the literature RVs and the new high-accuracy and high-precision HARPS-N data we acquired for 45 TGPs orbiting relatively bright stars over ~3 years. We thus produced the largest uniform catalogue of giant planet orbital and physical parameters. For several systems we combined for the first time RV datasets collected with different spectrographs by different groups thus improving the orbital solution. In general, we fitted a separate jitter term for each dataset by allowing for different values of extra noise caused by instrumental effects and/or changing levels of stellar activity in different observing seasons. This way, we uniformly derived the orbital eccentricities of (8 data files).

Breast Shape Analysis With Curvature Estimates and Principal Component Analysis for Cosmetic and Reconstructive Breast Surgery.

PubMed

Catanuto, Giuseppe; Taher, Wafa; Rocco, Nicola; Catalano, Francesca; Allegra, Dario; Milotta, Filippo Luigi Maria; Stanco, Filippo; Gallo, Giovanni; Nava, Maurizio Bruno

2018-03-20

Breast shape is defined utilizing mainly qualitative assessment (full, flat, ptotic) or estimates, such as volume or distances between reference points, that cannot describe it reliably. We will quantitatively describe breast shape with two parameters derived from a statistical methodology denominated principal component analysis (PCA). We created a heterogeneous dataset of breast shapes acquired with a commercial infrared 3-dimensional scanner on which PCA was performed. We plotted on a Cartesian plane the two highest values of PCA for each breast (principal components 1 and 2). Testing of the methodology on a preoperative and postoperative surgical case and test-retest was performed by two operators. The first two principal components derived from PCA are able to characterize the shape of the breast included in the dataset. The test-retest demonstrated that different operators are able to obtain very similar values of PCA. The system is also able to identify major changes in the preoperative and postoperative stages of a two-stage reconstruction. Even minor changes were correctly detected by the system. This methodology can reliably describe the shape of a breast. An expert operator and a newly trained operator can reach similar results in a test/re-testing validation. Once developed and after further validation, this methodology could be employed as a good tool for outcome evaluation, auditing, and benchmarking.

Validation of satellite retrievals of cloud microsphysics and liquid water path using observations from FIRE

NASA Technical Reports Server (NTRS)

Rossow, W.; White, A.; Han, Q.; Welch, R.; Chou, J.

1995-01-01

Cloud effective radii (r(sub e)) and cloud liquid water path (LWP) are derived from ISCCP spatially sampled satellite data and validated with ground-based pyranometer and microwave radiometer measurements taken on San Nicolas Island during the 1987 FIRE IFO. Values of r(sub e) derived from the ISCCP data are also compared to values retrieved by a hybrid method that uses the combination of LWP derived from microwave measurement and optical thickness derived from GOES data. The results show that there is significant variability in cloud properties over a 100 km x 80 km area and that the values at San Nicolas Island are not necessarily representative of the surrounding cloud field. On the other hand, even though there were large spatial variations in optical depth, the r(sub e) values remained relatively constant (with sigma less than or equal to 2-3 microns in most cases) in the marine stratocumulus. Furthermore, values of r(sub e) derived from the upper portion of the cloud generally are representative of the entire stratiform cloud. When LWP values are less than 100 g m(exp -2), then LWP values derived from ISCCP data agree well with those values estimated from ground-based microwave measurements. In most cases LWP differences were less than 20 g m(exp -2). However, when LWP values become large (e.g., greater than or equal to 200 g m(exp -2)), then relative differences may be as large as 50%- 100%. There are two reasons for this discrepancy in the large LWP clouds: (1) larger vertical inhomogeneities in precipitating clouds and (2) sampling errors on days of high spatial variability of cloud optical thicknesses. Variations of r(sub e) in stratiform clouds may indicate drizzle: clouds with droplet sizes larger than 15 microns appear to be associated with drizzling, while those less than 10 microns are indicative of nonprecipitating clouds. Differences in r(sub e) values between the GOES and ISCCP datasets are found to be 0.16 +/- 0.98 micron.

FEV1/FVC and FEV1 for the assessment of chronic airflow obstruction in prevalence studies: do prediction equations need revision?

PubMed

Roche, Nicolas; Dalmay, François; Perez, Thierry; Kuntz, Claude; Vergnenègre, Alain; Neukirch, Françoise; Giordanella, Jean-Pierre; Huchon, Gérard

2008-11-01

Little is known on the long-term validity of reference equations used in the calculation of FEV(1) and FEV(1)/FVC predicted values. This survey assessed the prevalence of chronic airflow obstruction in a population-based sample and how it is influenced by: (i) the definition of airflow obstruction; and (ii) equations used to calculate predicted values. Subjects aged 45 or more were recruited in health prevention centers, performed spirometry and fulfilled a standardized ECRHS-derived questionnaire. Previously diagnosed cases and risk factors were identified. Prevalence of airflow obstruction was calculated using: (i) ATS-GOLD definition (FEV(1)/FVC<0.70); and (ii) ERS definition (FEV(1)/FVC

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets.

PubMed

Abu-Jamous, Basel; Fa, Rui; Roberts, David J; Nandi, Asoke K

2015-06-04

Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

Northern Hemisphere winter storm track trends since 1959 derived from multiple reanalysis datasets

NASA Astrophysics Data System (ADS)

Chang, Edmund K. M.; Yau, Albert M. W.

2016-09-01

In this study, a comprehensive comparison of Northern Hemisphere winter storm track trend since 1959 derived from multiple reanalysis datasets and rawinsonde observations has been conducted. In addition, trends in terms of variance and cyclone track statistics have been compared. Previous studies, based largely on the National Center for Environmental Prediction-National Center for Atmospheric Research Reanalysis (NNR), have suggested that both the Pacific and Atlantic storm tracks have significantly intensified between the 1950s and 1990s. Comparison with trends derived from rawinsonde observations suggest that the trends derived from NNR are significantly biased high, while those from the European Center for Medium Range Weather Forecasts 40-year Reanalysis and the Japanese 55-year Reanalysis are much less biased but still too high. Those from the two twentieth century reanalysis datasets are most consistent with observations but may exhibit slight biases of opposite signs. Between 1959 and 2010, Pacific storm track activity has likely increased by 10 % or more, while Atlantic storm track activity has likely increased by <10 %. Our analysis suggests that trends in Pacific and Atlantic basin wide storm track activity prior to the 1950s derived from the two twentieth century reanalysis datasets are unlikely to be reliable due to changes in density of surface observations. Nevertheless, these datasets may provide useful information on interannual variability, especially over the Atlantic.

Identification of Differentially Expressed Genes through Integrated Study of Alzheimer's Disease Affected Brain Regions.

PubMed

Puthiyedth, Nisha; Riveros, Carlos; Berretta, Regina; Moscato, Pablo

2016-01-01

Alzheimer's disease (AD) is the most common form of dementia in older adults that damages the brain and results in impaired memory, thinking and behaviour. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. In the past decade, several studies have reported many genes that are associated with AD. This wealth of information has become difficult to follow and interpret as most of the results are conflicting. In that case, it is worth doing an integrated study of multiple datasets that helps to increase the total number of samples and the statistical power in detecting biomarkers. In this study, we present an integrated analysis of five different brain region datasets and introduce new genes that warrant further investigation. The aim of our study is to apply a novel combinatorial optimisation based meta-analysis approach to identify differentially expressed genes that are associated to AD across brain regions. In this study, microarray gene expression data from 161 samples (74 non-demented controls, 87 AD) from the Entorhinal Cortex (EC), Hippocampus (HIP), Middle temporal gyrus (MTG), Posterior cingulate cortex (PC), Superior frontal gyrus (SFG) and visual cortex (VCX) brain regions were integrated and analysed using our method. The results are then compared to two popular meta-analysis methods, RankProd and GeneMeta, and to what can be obtained by analysing the individual datasets. We find genes related with AD that are consistent with existing studies, and new candidate genes not previously related with AD. Our study confirms the up-regualtion of INFAR2 and PTMA along with the down regulation of GPHN, RAB2A, PSMD14 and FGF. Novel genes PSMB2, WNK1, RPL15, SEMA4C, RWDD2A and LARGE are found to be differentially expressed across all brain regions. Further investigation on these genes may provide new insights into the development of AD. In addition, we identified the presence of 23 non-coding features, including four miRNA precursors (miR-7, miR570, miR-1229 and miR-6821), dysregulated across the brain regions. Furthermore, we compared our results with two popular meta-analysis methods RankProd and GeneMeta to validate our findings and performed a sensitivity analysis by removing one dataset at a time to assess the robustness of our results. These new findings may provide new insights into the disease mechanisms and thus make a significant contribution in the near future towards understanding, prevention and cure of AD.

A Deep Learning Approach to Neuroanatomical Characterisation of Alzheimer's Disease.

PubMed

Ambastha, Abhinit Kumar; Leong, Tze-Yun

2017-01-01

Alzheimer's disease (AD) is a neurological degenerative disorder that leads to progressive mental deterioration. This work introduces a computational approach to improve our understanding of the progression of AD. We use ensemble learning methods and deep neural networks to identify salient structural correlations among brain regions that degenerate together in AD; this provides an understanding of how AD progresses in the brain. The proposed technique has a classification accuracy of 81.79% for AD against healthy subjects using a single modality imaging dataset.

Crisp clustering of airborne geophysical data from the Alto Ligonha pegmatite field, northeastern Mozambique, to predict zones of increased rare earth element potential

NASA Astrophysics Data System (ADS)

Eberle, Detlef G.; Daudi, Elias X. F.; Muiuane, Elônio A.; Nyabeze, Peter; Pontavida, Alfredo M.

2012-01-01

The National Geology Directorate of Mozambique (DNG) and Maputo-based Eduardo-Mondlane University (UEM) entered a joint venture with the South African Council for Geoscience (CGS) to conduct a case study over the meso-Proterozoic Alto Ligonha pegmatite field in the Zambézia Province of northeastern Mozambique to support the local exploration and mining sectors. Rare-metal minerals, i.e. tantalum and niobium, as well as rare-earth minerals have been mined in the Alto Ligonha pegmatite field since decades, but due to the civil war (1977-1992) production nearly ceased. The Government now strives to promote mining in the region as contribution to poverty alleviation. This study was undertaken to facilitate the extraction of geological information from the high resolution airborne magnetic and radiometric data sets recently acquired through a World Bank funded survey and mapping project. The aim was to generate a value-added map from the airborne geophysical data that is easier to read and use by the exploration and mining industries than mere airborne geophysical grid data or maps. As a first step towards clustering, thorium (Th) and potassium (K) concentrations were determined from the airborne geophysical data as well as apparent magnetic susceptibility and first vertical magnetic gradient data. These four datasets were projected onto a 100 m spaced regular grid to assemble 850,000 four-element (multivariate) sample vectors over the study area. Classification of the sample vectors using crisp clustering based upon the Euclidian distance between sample and class centre provided a (pseudo-) geology map or value-added map, respectively, displaying the spatial distribution of six different classes in the study area. To learn the quality of sample allocation, the degree of membership of each sample vector was determined using a-posterior discriminant analysis. Geophysical ground truth control was essential to allocate geology/geophysical attributes to the six classes. The highest probability to meet pegmatite bodies is in close vicinity to (magnetic) amphibole schist occurring in areas where depletion of potassium as indication of metasomatic processes is evident from the airborne radiometric data. Clustering has proven to be a fast and effective method to compile value-added maps from multivariate geophysical datasets. Experience made in the Alto Ligonha pegmatite field encourages adopting this new methodology for mapping other parts of the Mozambique Fold Belt.

Analytical Problems and Suggestions in the Analysis of Behavioral Economic Demand Curves.

PubMed

Yu, Jihnhee; Liu, Liu; Collins, R Lorraine; Vincent, Paula C; Epstein, Leonard H

2014-01-01

Behavioral economic demand curves (Hursh, Raslear, Shurtleff, Bauman, & Simmons, 1988) are innovative approaches to characterize the relationships between consumption of a substance and its price. In this article, we investigate common analytical issues in the use of behavioral economic demand curves, which can cause inconsistent interpretations of demand curves, and then we provide methodological suggestions to address those analytical issues. We first demonstrate that log transformation with different added values for handling zeros changes model parameter estimates dramatically. Second, demand curves are often analyzed using an overparameterized model that results in an inefficient use of the available data and a lack of assessment of the variability among individuals. To address these issues, we apply a nonlinear mixed effects model based on multivariate error structures that has not been used previously to analyze behavioral economic demand curves in the literature. We also propose analytical formulas for the relevant standard errors of derived values such as P max, O max, and elasticity. The proposed model stabilizes the derived values regardless of using different added increments and provides substantially smaller standard errors. We illustrate the data analysis procedure using data from a relative reinforcement efficacy study of simulated marijuana purchasing.

Observational uncertainty and regional climate model evaluation: A pan-European perspective

NASA Astrophysics Data System (ADS)

Kotlarski, Sven; Szabó, Péter; Herrera, Sixto; Räty, Olle; Keuler, Klaus; Soares, Pedro M.; Cardoso, Rita M.; Bosshard, Thomas; Pagé, Christian; Boberg, Fredrik; Gutiérrez, José M.; Jaczewski, Adam; Kreienkamp, Frank; Liniger, Mark. A.; Lussana, Cristian; Szepszo, Gabriella

2017-04-01

Local and regional climate change assessments based on downscaling methods crucially depend on the existence of accurate and reliable observational reference data. In dynamical downscaling via regional climate models (RCMs) observational data can influence model development itself and, later on, model evaluation, parameter calibration and added value assessment. In empirical-statistical downscaling, observations serve as predictand data and directly influence model calibration with corresponding effects on downscaled climate change projections. Focusing on the evaluation of RCMs, we here analyze the influence of uncertainties in observational reference data on evaluation results in a well-defined performance assessment framework and on a European scale. For this purpose we employ three different gridded observational reference grids, namely (1) the well-established EOBS dataset (2) the recently developed EURO4M-MESAN regional re-analysis, and (3) several national high-resolution and quality-controlled gridded datasets that recently became available. In terms of climate models five reanalysis-driven experiments carried out by five different RCMs within the EURO-CORDEX framework are used. Two variables (temperature and precipitation) and a range of evaluation metrics that reflect different aspects of RCM performance are considered. We furthermore include an illustrative model ranking exercise and relate observational spread to RCM spread. The results obtained indicate a varying influence of observational uncertainty on model evaluation depending on the variable, the season, the region and the specific performance metric considered. Over most parts of the continent, the influence of the choice of the reference dataset for temperature is rather small for seasonal mean values and inter-annual variability. Here, model uncertainty (as measured by the spread between the five RCM simulations considered) is typically much larger than reference data uncertainty. For parameters of the daily temperature distribution and for the spatial pattern correlation, however, important dependencies on the reference dataset can arise. The related evaluation uncertainties can be as large or even larger than model uncertainty. For precipitation the influence of observational uncertainty is, in general, larger than for temperature. It often dominates model uncertainty especially for the evaluation of the wet day frequency, the spatial correlation and the shape and location of the distribution of daily values. But even the evaluation of large-scale seasonal mean values can be considerably affected by the choice of the reference. When employing a simple and illustrative model ranking scheme on these results it is found that RCM ranking in many cases depends on the reference dataset employed.

Derivation of ecological criteria for copper in land-applied biosolids and biosolid-amended agricultural soils.

PubMed

Lu, Tao; Li, Jumei; Wang, Xiaoqing; Ma, Yibing; Smolders, Erik; Zhu, Nanwen

2016-12-01

The difference in availability between soil metals added via biosolids and soluble salts was not taken into account in deriving the current land-applied biosolids standards. In the present study, a biosolids availability factor (BAF) approach was adopted to investigate the ecological thresholds for copper (Cu) in land-applied biosolids and biosolid-amended agricultural soils. First, the soil property-specific values of HC5 add (the added hazardous concentration for 5% of species) for Cu 2+ salt amended were collected with due attention to data for organisms and soils relevant to China. Second, a BAF representing the difference in availability between soil Cu added via biosolids and soluble salts was estimated based on long-term biosolid-amended soils, including soils from China. Third, biosolids Cu HC5 input values (the input hazardous concentration for 5% of species of Cu from biosolids to soil) as a function of soil properties were derived using the BAF approach. The average potential availability of Cu in agricultural soils amended with biosolids accounted for 53% of that for the same soils spiked with same amount of soluble Cu salts and with a similar aging time. The cation exchange capacity was the main factor affecting the biosolids Cu HC5 input values, while soil pH and organic carbon only explained 24.2 and 1.5% of the variation, respectively. The biosolids Cu HC5 input values can be accurately predicted by regression models developed based on 2-3 soil properties with coefficients of determination (R 2 ) of 0.889 and 0.945. Compared with model predicted biosolids Cu HC5 input values, current standards (GB4284-84) are most likely to be less protective in acidic and neutral soil, but conservative in alkaline non-calcareous soil. Recommendations on ecological criteria for Cu in land-applied biosolids and biosolid-amended agriculture soils may be helpful to fill the gaps existing between science and regulations, and can be useful for Cu risk assessments in soils amended with biosolids. Copyright © 2016 Elsevier Ltd. All rights reserved.

Examining the validity and utility of two secondary sources of food environment data against street audits in England.

PubMed

Wilkins, Emma L; Radley, Duncan; Morris, Michelle A; Griffiths, Claire

2017-12-20

Secondary data containing the locations of food outlets is increasingly used in nutrition and obesity research and policy. However, evidence evaluating these data is limited. This study validates two sources of secondary food environment data: Ordnance Survey Points of Interest data (POI) and food hygiene data from the Food Standards Agency (FSA), against street audits in England and appraises the utility of these data. Audits were conducted across 52 Lower Super Output Areas in England. All streets within each Lower Super Output Area were covered to identify the name and street address of all food outlets therein. Audit-identified outlets were matched to outlets in the POI and FSA data to identify true positives (TP: outlets in both the audits and the POI/FSA data), false positives (FP: outlets in the POI/FSA data only) and false negatives (FN: outlets in the audits only). Agreement was assessed using positive predictive values (PPV: TP/(TP + FP)) and sensitivities (TP/(TP + FN)). Variations in sensitivities and PPVs across environment and outlet types were assessed using multi-level logistic regression. Proprietary classifications within the POI data were additionally used to classify outlets, and agreement between audit-derived and POI-derived classifications was assessed. Street audits identified 1172 outlets, compared to 1100 and 1082 for POI and FSA respectively. PPVs were statistically significantly higher for FSA (0.91, CI: 0.89-0.93) than for POI (0.86, CI: 0.84-0.88). However, sensitivity values were not different between the two datasets. Sensitivity and PPVs varied across outlet types for both datasets. Without accounting for this, POI had statistically significantly better PPVs in rural and affluent areas. After accounting for variability across outlet types, FSA had statistically significantly better sensitivity in rural areas and worse sensitivity in rural middle affluence areas (relative to deprived). Audit-derived and POI-derived classifications exhibited substantial agreement (p < 0.001; Kappa = 0.66, CI: 0.63-0.70). POI and FSA data have good agreement with street audits; although both datasets had geographic biases which may need to be accounted for in analyses. Use of POI proprietary classifications is an accurate method for classifying outlets, providing time savings compared to manual classification of outlets.

Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

PubMed

Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

2018-05-01

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Development and optimization of a wildfire plume rise model based on remote sensing data inputs - Part 2

NASA Astrophysics Data System (ADS)

Paugam, R.; Wooster, M.; Atherton, J.; Freitas, S. R.; Schultz, M. G.; Kaiser, J. W.

2015-03-01

Biomass burning is one of a relatively few natural processes that can inject globally significant quantities of gases and aerosols into the atmosphere at altitudes well above the planetary boundary layer, in some cases at heights in excess of 10 km. The "injection height" of biomass burning emissions is therefore an important parameter to understand when considering the characteristics of the smoke plumes emanating from landscape scale fires, and in particular when attempting to model their atmospheric transport. Here we further extend the formulations used within a popular 1D plume rise model, widely used for the estimation of landscape scale fire smoke plume injection height, and develop and optimise the model both so that it can run with an increased set of remotely sensed observations. The model is well suited for application in atmospheric Chemistry Transport Models (CTMs) aimed at understanding smoke plume downstream impacts, and whilst a number of wildfire emission inventories are available for use in such CTMs, few include information on plume injection height. Since CTM resolutions are typically too spatially coarse to capture the vertical transport induced by the heat released from landscape scale fires, approaches to estimate the emissions injection height are typically based on parametrizations. Our extensions of the existing 1D plume rise model takes into account the impact of atmospheric stability and latent heat on the plume up-draft, driving it with new information on active fire area and fire radiative power (FRP) retrieved from MODIS satellite Earth Observation (EO) data, alongside ECMWF atmospheric profile information. We extend the model by adding an equation for mass conservation and a new entrainment scheme, and optimise the values of the newly added parameters based on comparison to injection heights derived from smoke plume height retrievals made using the MISR EO sensor. Our parameter optimisation procedure is based on a twofold approach using sequentially a Simulating Annealing algorithm and a Markov chain Monte Carlo uncertainty test, and to try to ensure the appropriate convergence on suitable parameter values we use a training dataset consisting of only fires where a number of specific quality criteria are met, including local ambient wind shear limits derived from the ECMWF and MISR data, and "steady state" plumes and fires showing only relatively small changes between consecutive MODIS observations. Using our optimised plume rise model (PRMv2) with information from all MODIS-detected active fires detected in 2003 over North America, with outputs gridded to a 0.1° horizontal and 500 m vertical resolution mesh, we are able to derive wildfire injection height distributions whose maxima extend to the type of higher altitudes seen in actual observation-based wildfire plume datasets than are those derived either via the original plume model or any other parametrization tested herein. We also find our model to be the only one tested that more correctly simulates the very high plume (6 to 8 km a.s.l.), created by a large fire in Alberta (Canada) on the 17 August 2003, though even our approach does not reach the stratosphere as the real plume is expected to have done. Our results lead us to believe that our PRMv2 approach to modelling the injection height of wildfire plumes is a strong candidate for inclusion into CTMs aiming to represent this process, but we note that significant advances in the spatio-temporal resolutions of the data required to feed the model will also very likely bring key improvements in our ability to more accurately represent such phenomena, and that there remain challenges to the detailed validation of such simulations due to the relative sparseness of plume height observations and their currently rather limited temporal coverage which are not necessarily well matched to when fires are most active (MISR being confined to morning observations for example).

Development of the phosphorus and nitrogen containing flame retardant for value added cotton product

USDA-ARS?s Scientific Manuscript database

It is our desire to develop new crosslinking agents for cotton textiles that afford useful flame protection regardless of fabric construction. Herein we present the synthesis and the application of the triazine and piperazine derivatives as flame retardant on cotton. Novel phosphorus-nitrogen contai...

Mind the Gap: Accountability, Observation and Special Education

ERIC Educational Resources Information Center

Crowe, Christina C.; Rivers, Susan E.; Bertoli, Michelle C.

2017-01-01

There is an absence of observation-based tools designed to evaluate teaching in special education classrooms. Evaluations derived from classroom observations are integral to the accountability process, adding value to understanding teaching and learning by providing a lens into the classroom that test scores cannot capture. The present paper…

Catalytic modification of fats and oils to value-added biobased products

USDA-ARS?s Scientific Manuscript database

Biobased materials derived from fats and oils can be relatively benign to the environment because they tend to have good biodegradability. Oils are used in a myriad of applications, including foods, cosmetics, paints, biodegradable lubricants and polymers, biodiesel, and more. For many of these ap...

A Novel Anti-classification Approach for Knowledge Protection.

PubMed

Lin, Chen-Yi; Chen, Tung-Shou; Tsai, Hui-Fang; Lee, Wei-Bin; Hsu, Tien-Yu; Kao, Yuan-Hung

2015-10-01

Classification is the problem of identifying a set of categories where new data belong, on the basis of a set of training data whose category membership is known. Its application is wide-spread, such as the medical science domain. The issue of the classification knowledge protection has been paid attention increasingly in recent years because of the popularity of cloud environments. In the paper, we propose a Shaking Sorted-Sampling (triple-S) algorithm for protecting the classification knowledge of a dataset. The triple-S algorithm sorts the data of an original dataset according to the projection results of the principal components analysis so that the features of the adjacent data are similar. Then, we generate noise data with incorrect classes and add those data to the original dataset. In addition, we develop an effective positioning strategy, determining the added positions of noise data in the original dataset, to ensure the restoration of the original dataset after removing those noise data. The experimental results show that the disturbance effect of the triple-S algorithm on the CLC, MySVM, and LibSVM classifiers increases when the noise data ratio increases. In addition, compared with existing methods, the disturbance effect of the triple-S algorithm is more significant on MySVM and LibSVM when a certain amount of the noise data added to the original dataset is reached.

In silico models for predicting ready biodegradability under REACH: a comparative study.

PubMed

Pizzo, Fabiola; Lombardo, Anna; Manganaro, Alberto; Benfenati, Emilio

2013-10-01

REACH (Registration Evaluation Authorization and restriction of Chemicals) legislation is a new European law which aims to raise the human protection level and environmental health. Under REACH all chemicals manufactured or imported for more than one ton per year must be evaluated for their ready biodegradability. Ready biodegradability is also used as a screening test for persistent, bioaccumulative and toxic (PBT) substances. REACH encourages the use of non-testing methods such as QSAR (quantitative structure-activity relationship) models in order to save money and time and to reduce the number of animals used for scientific purposes. Some QSAR models are available for predicting ready biodegradability. We used a dataset of 722 compounds to test four models: VEGA, TOPKAT, BIOWIN 5 and 6 and START and compared their performance on the basis of the following parameters: accuracy, sensitivity, specificity and Matthew's correlation coefficient (MCC). Performance was analyzed from different points of view. The first calculation was done on the whole dataset and VEGA and TOPKAT gave the best accuracy (88% and 87% respectively). Then we considered the compounds inside and outside the training set: BIOWIN 6 and 5 gave the best results for accuracy (81%) outside training set. Another analysis examined the applicability domain (AD). VEGA had the highest value for compounds inside the AD for all the parameters taken into account. Finally, compounds outside the training set and in the AD of the models were considered to assess predictive ability. VEGA gave the best accuracy results (99%) for this group of chemicals. Generally, START model gave poor results. Since BIOWIN, TOPKAT and VEGA models performed well, they may be used to predict ready biodegradability. Copyright © 2013 Elsevier B.V. All rights reserved.

Anatomy of small-scale mixing along a Northeast Atlantic transect

NASA Astrophysics Data System (ADS)

Jurado, Elena; Dijkstra, Henk A.; van der Woerd, Hans; Brussaard, Corina

2010-05-01

The study of turbulence occurring at the smallest scales, in the energy dissipation range, is required when evaluating interrelations between turbulent mixing and phytoplankton distribution. To derive microturbulent parameters, microstructure profiler surveys, consisting in high resolution temperature, salinity or velocity vertical profiles have been performed in localized regions of the open ocean. However, they are very localized and based on few datasets, difficult to extrapolate to other regions due to the dependence on the local background conditions. During the STRATIPHYT-I cruise (July-August 2009) from Las Palmas (Gran Canaria) to Reykjavik (Iceland), high resolution measurements of both turbulent mixing (with a Self Contained Autonomous Micro Profiler, SCAMP) and phytoplankton have been carried out in the top 100 m of the ocean. With these data, the gradient from a more stratified, warmer surface water tropical environment to a less stratified subpolar ocean environment is covered. Adding up a total of 15 stations and 148 profiles, it constitutes the most extensive dataset of directly derived vertical mixing coefficients in a latitudinal transect of the Northeast Atlantic. In the presentation, the focus is on the explanation of the changes in turbulent mixing along the cruise section, recalling in its latitudinal gradient and presenting parameters that can further help to evaluate effects in the phytoplankton distribution. Side issues such as the encountered disagreement between heat and density eddy diffusivities and an analysis of the main source of instabilities through GOTM model and an internal wave analysis, are also treated in detail.

A Dataset from TIMSS to Examine the Relationship between Computer Use and Mathematics Achievement

ERIC Educational Resources Information Center

Kadijevich, Djordje M.

2015-01-01

Because the relationship between computer use and achievement is still puzzling, there is a need to prepare and analyze good quality datasets on computer use and achievement. Such a dataset can be derived from TIMSS data. This paper describes how this dataset can be prepared. It also gives an example of how the dataset may be analyzed. The…

HYDRA Hyperspectral Data Research Application Tom Rink and Tom Whittaker

NASA Astrophysics Data System (ADS)

Rink, T.; Whittaker, T.

2005-12-01

HYDRA is a freely available, easy to install tool for visualization and analysis of large local or remote hyper/multi-spectral datasets. HYDRA is implemented on top of the open source VisAD Java library via Jython - the Java implementation of the user friendly Python programming language. VisAD provides data integration, through its generalized data model, user-display interaction and display rendering. Jython has an easy to read, concise, scripting-like, syntax which eases software development. HYDRA allows data sharing of large datasets through its support of the OpenDAP and OpenADDE server-client protocols. The users can explore and interrogate data, and subset in physical and/or spectral space to isolate key areas of interest for further analysis without having to download an entire dataset. It also has an extensible data input architecture to recognize new instruments and understand different local file formats, currently NetCDF and HDF4 are supported.

A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States

DOE PAGES

Robinson, Nathaniel; Allred, Brady; Jones, Matthew; ...

2017-08-21

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodologicalmore » challenges. Here, we address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.« less

A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States

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

Robinson, Nathaniel; Allred, Brady; Jones, Matthew

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodologicalmore » challenges. Here, we address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.« less

Evaluation of different methods for immobilization of Candida antarctica lipase B (CalB lipase) in polyurethane foam and its application in the production of geranyl propionate.

PubMed

Nicoletti, Gabrieli; Cipolatti, Eliane P; Valério, Alexsandra; Carbonera, NatáliaThaisa Gamba; Soares, Nicole Spillere; Theilacker, Eron; Ninow, Jorge L; de Oliveira, Débora

2015-09-01

With the aim of studying the best method for the interaction of polyurethane (PU) foam and Candida antarctica lipase B, different methods of CalB immobilization were studied: adsorption (PU-ADS), bond (using polyethyleneimine) (PU-PEI), ionic adsorption by PEI with cross-linking with glutaraldehyde (PU-PEI-GA) and entrapment (PU). The characterization of immobilized enzyme derivatives was performed by apparent density and Fourier transform infrared spectroscopy. The free enzyme and enzyme preparations were evaluated at different pH values and temperatures. The highest enzyme activity was obtained using the PU method (5.52 U/g). The methods that stood out to compare the stabilities and kinetic parameters were the PU and PU-ADS. Conversions of 83.5 and 95.9 % for PU and PU-ADS derivatives were obtained, in 24 h reaction, using citronella oil and propionic acid as substrates.

Application of a Global-to-Beam Irradiance Model to the NASA GEWEX SRB Dataset: An Extension of the NASA Surface Meteorology and Solar Energy Datasets

NASA Technical Reports Server (NTRS)

Zhang, Taiping; Stackhouse, Paul W., Jr.; Chandler, William S.; Westberg, David J.

2014-01-01

The DIRINDEX model was designed to estimate hourly solar beam irradiances from hourly global horizontal irradiances. This model was applied to the NASA GEWEX SRB(Rel. 3.0) 3-hourly global horizontal irradiance data to derive3-hourly global maps of beam, or direct normal, irradiance for the period from January 2000 to December 2005 at the 1 deg. x 1 deg. resolution. The DIRINDEX model is a combination of the DIRINT model, a quasi-physical global-to-beam irradiance model based on regression of hourly observed data, and a broadband simplified version of the SOLIS clear-sky beam irradiance model. In this study, the input variables of the DIRINDEX model are 3-hourly global horizontal irradiance, solar zenith angle, dew-point temperature, surface elevation, surface pressure, sea-level pressure, aerosol optical depth at 700 nm, and column water vapor. The resulting values of the 3-hourly direct normal irradiance are then used to compute daily and monthly means. The results are validated against the ground-based BSRN data. The monthly means show better agreement with the BSRN data than the results from an earlier endeavor which empirically derived the monthly mean direct normal irradiance from the GEWEX SRB monthly mean global horizontal irradiance. To assimilate the observed information into the final results, the direct normal fluxes from the DIRINDEX model are adjusted according to the comparison statistics in the latitude-longitude-cosine of solar zenith angle phase space, in which the inverse-distance interpolation is used for the adjustment. Since the NASA Surface meteorology and Solar Energy derives its data from the GEWEX SRB datasets, the results discussed herein will serve to extend the former.

Predicting missing values in a home care database using an adaptive uncertainty rule method.

PubMed

Konias, S; Gogou, G; Bamidis, P D; Vlahavas, I; Maglaveras, N

2005-01-01

Contemporary literature illustrates an abundance of adaptive algorithms for mining association rules. However, most literature is unable to deal with the peculiarities, such as missing values and dynamic data creation, that are frequently encountered in fields like medicine. This paper proposes an uncertainty rule method that uses an adaptive threshold for filling missing values in newly added records. A new approach for mining uncertainty rules and filling missing values is proposed, which is in turn particularly suitable for dynamic databases, like the ones used in home care systems. In this study, a new data mining method named FiMV (Filling Missing Values) is illustrated based on the mined uncertainty rules. Uncertainty rules have quite a similar structure to association rules and are extracted by an algorithm proposed in previous work, namely AURG (Adaptive Uncertainty Rule Generation). The main target was to implement an appropriate method for recovering missing values in a dynamic database, where new records are continuously added, without needing to specify any kind of thresholds beforehand. The method was applied to a home care monitoring system database. Randomly, multiple missing values for each record's attributes (rate 5-20% by 5% increments) were introduced in the initial dataset. FiMV demonstrated 100% completion rates with over 90% success in each case, while usual approaches, where all records with missing values are ignored or thresholds are required, experienced significantly reduced completion and success rates. It is concluded that the proposed method is appropriate for the data-cleaning step of the Knowledge Discovery process in databases. The latter, containing much significance for the output efficiency of any data mining technique, can improve the quality of the mined information.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Deriving sulfamethoxazole dissipation endpoints in pasture soils using first order and biphasic kinetic models.

PubMed

Srinivasan, Prakash; Sarmah, Ajit K; Rohan, Maheswaran

2014-08-01

Single first-order (SFO) kinetic model is often used to derive the dissipation endpoints of an organic chemical in soil. This model is used due to its simplicity and requirement by regulatory agencies. However, using the SFO model for all types of decay pattern could lead to under- or overestimation of dissipation endpoints when the deviation from first-order is significant. In this study the performance of three biphasic kinetic models - bi-exponential decay (BEXP), first-order double exponential decay (FODED), and first-order two-compartment (FOTC) models was evaluated using dissipation datasets of sulfamethoxazole (SMO) antibiotic in three different soils under varying concentration, depth, temperature, and sterile conditions. Corresponding 50% (DT50) and 90% (DT90) dissipation times for the antibiotics were numerically obtained and compared against those obtained using the SFO model. The fit of each model to the measured values was evaluated based on an array of statistical measures such as coefficient of determination (R(2)adj), root mean square error (RMSE), chi-square (χ(2)) test at 1% significance, Bayesian Information Criteria (BIC) and % model error. Box-whisker residual plots were also used to compare the performance of each model to the measured datasets. The antibiotic dissipation was successfully predicted by all four models. However, the nonlinear biphasic models improved the goodness-of-fit parameters for all datasets. Deviations from datasets were also often less evident with the biphasic models. The fits of FOTC and FODED models for SMO dissipation datasets were identical in most cases, and were found to be superior to the BEXP model. Among the biphasic models, the FOTC model was found to be the most suitable for obtaining the endpoints and could provide a mechanistic explanation for SMO dissipation in the soils. Copyright © 2014 Elsevier B.V. All rights reserved.

Penicilones A-D, Anti-MRSA Azaphilones from the Marine-Derived Fungus Penicillium janthinellum HK1-6.

PubMed

Chen, Min; Shen, Nan-Xing; Chen, Zhi-Qi; Zhang, Feng-Min; Chen, Yang

2017-04-28

Four new azaphilones, penicilones A-D (1-4), were isolated from the mangrove rhizosphere soil-derived fungus Penicillium janthinellum HK1-6. Their planar structures and absolute configurations were determined by extensive analysis of NMR spectroscopic data, ECD spectra, the modified Mosher's method, and chemical conversions. Interestingly, 1 and 2 had the opposite configuration at C-7 compared to the closely related chloro analogues 3 and 4. Ester hydrolysis of 2 and 4 afforded their parental azaphilones, named penicilones E (5) and F (6). Compounds 1-6 were evaluated for their antibacterial activities in vitro. Penicilones B-D (2-4) showed potent anti-MRSA (Staphylococcus aureus ATCC 43300, ATCC 33591) activities with MIC values ranging from 3.13 to 6.25 μg/mL.

Metabolite formation kinetics and intrinsic clearance of phenacetin, tolbutamide, alprazolam, and midazolam in adenoviral cytochrome P450-transfected HepG2 cells and comparison with hepatocytes and in vivo.

PubMed

Donato, M Teresa; Hallifax, David; Picazo, Laura; Castell, José V; Houston, J Brian; Gomez-Lechón, M José; Lahoz, Agustin

2010-09-01

Cryopreserved human hepatocytes and other in vitro systems often underpredict in vivo intrinsic clearance (CL(int)). The aim of this study was to explore the potential utility of HepG2 cells transduced with adenovirus vectors expressing a single cytochrome P450 enzyme (Ad-CYP1A2, Ad-CYP2C9, or Ad-CYP3A4) for metabolic clearance predictions. The kinetics of metabolite formation from phenacetin, tolbutamide, and alprazolam and midazolam, selected as substrates probes for CYP1A2, CYP2C9, and CYP3A4, respectively, were characterized in this in vitro system. The magnitude of the K(m) or S(50) values observed in Ad-P450 cells was similar to those found in the literature for other human liver-derived systems. For each substrate, CL(int) (or CL(max)), values from Ad-P450 systems were scaled to human hepatocytes in primary culture using the relative activity factor (RAF) approach. Scaled Ad-P450 CL(int) values were approximately 3- to 6-fold higher (for phenacetin O-deethylation, tolbutamide 4-hydroxylation, and alprazolam 4-hydroxyaltion) or lower (midazolam 1'-hydroxylation) than those reported for human cryopreserved hepatocytes in suspension. Comparison with the in vivo data reveals that Ad-P450 cells provide a favorable prediction of CL(int) for the substrates studied (in a range of 20-200% in vivo observed CL(int)). This is an improvement compared with the consistent underpredictions (<10-50% in in vivo observed CL(int)) found in cryopreserved hepatocyte studies with the same substrates. These results suggest that the Ad-P450 cell is a promising in vitro system for clearance predictions of P450-metabolized drugs.

Microbial chemical factories: recent advances in pathway engineering for synthesis of value added chemicals.

PubMed

Dhamankar, Himanshu; Prather, Kristala L J

2011-08-01

The dwindling nature of petroleum and other fossil reserves has provided impetus towards microbial synthesis of fuels and value added chemicals from biomass-derived sugars as a renewable resource. Microbes have naturally evolved enzymes and pathways that can convert biomass into hundreds of unique chemical structures, a property that can be effectively exploited for their engineering into Microbial Chemical Factories (MCFs). De novo pathway engineering facilitates expansion of the repertoire of microbially synthesized compounds beyond natural products. In this review, we visit some recent successes in such novel pathway engineering and optimization, with particular emphasis on the selection and engineering of pathway enzymes and balancing of their accessory cofactors. Copyright © 2011 Elsevier Ltd. All rights reserved.

Reliability of the totality of the eclipse in AD 628 in Nihongi

NASA Astrophysics Data System (ADS)

Tanikawa, Kiyotaka; Soma, Mitsuru

It is generally accepted that the solar eclipse on April 10, 628 (the second day, the third month, the thirty-sixth year of Empress Suiko) recorded in Nihongi is not total but partial though it is written as a total eclipse. We argue for the record appealing to the contemporary total or near total eclipses in Chinese history books and Japanese occultation observation. If the value of the tidal term in the lunar longitude (the coefficient of T2 term) is different from the present value by about -2"/cy-2, then there disappears an apparent contradiction of ΔT around AD 600 derived from lunar and solar eclipses. Grazing occultation data are found to be useful.

Starch--value addition by modification.

PubMed

Tharanathan, Rudrapatnam N

2005-01-01

Starch is one of the most important but flexible food ingredients possessing value added attributes for innumerable industrial applications. Its various chemically modified derivatives offer a great scope of high technological value in both food and non-food industries. Modified starches are designed to overcome one or more of the shortcomings, such as loss of viscosity and thickening power upon cooking and storage, particularly at low pH, retrogradation characteristics, syneresis, etc., of native starches. Oxidation, esterification, hydroxyalkylation, dextrinization, and cross-linking are some of the modifications commonly employed to prepare starch derivatives. In a way, starch modification provides desirable functional attributes as well as offering economic alternative to other hydrocolloid ingredients, such as gums and mucilages, which are unreliable in quality and availability. Resistant starch, a highly retrograded starch fractionformed upon food processing, is another useful starch derivative. It exhibits the beneficial physiological effects of therapeutic and nutritional values akin to dietary fiber. There awaits considerable opportunity for future developments, especially for tailor-made starch derivatives with multiple modifications and with the desired functional and nutritional properties, although the problem of obtaining legislative approval for the use of novel starch derivatives in processed food formulations is still under debate. Nevertheless, it can be predicted that new ventures in starch modifications and their diverse applications will continue to be of great interest in applied research.

Cross-validation of a Shortened Battery for the Assessment of Dysexecutive Disorders in Alzheimer Disease.

PubMed

Godefroy, Olivier; Martinaud, Olivier; Verny, Marc; Mosca, Chrystèle; Lenoir, Hermine; Bretault, Eric; Devendeville, Agnès; Diouf, Momar; Pere, Jean-Jacques; Bakchine, Serge; Delabrousse-Mayoux, Jean-Philippe; Roussel, Martine

2016-01-01

The frequency of executive disorders in mild-to-moderate Alzheimer disease (AD) has been demonstrated by the application of a comprehensive battery. The present study analyzed data from 2 recent multicenter studies based on the same executive battery. The objective was to derive a shortened battery by using the GREFEX population as a training dataset and by cross-validating the results in the REFLEX population. A total of 102 AD patients of the GREFEX study (MMSE=23.2±2.9) and 72 patients of the REFLEX study (MMSE=20.8±3.5) were included. Tests were selected and receiver operating characteristic curves were generated relative to the performance of 780 controls from the GREFEX study. Stepwise logistic regression identified 3 cognitive tests (Six Elements Task, categorical fluency and Trail Making Test B error) and behavioral disorders globally referred as global hypoactivity (P=0.0001, all). This shortened battery was as accurate as the entire GREFEX battery in diagnosing dysexecutive disorders in both training group and the validation group. Bootstrap procedure confirmed the stability of AUC. A shortened battery based on 3 cognitive tests and 3 behavioral domains provides a high diagnosis accuracy of executive disorders in mild-to-moderate AD.

Hearing Nano-Structures: A Case Study in Timbral Sonification

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

Schedel, M.; Yager, K.

2012-06-18

We explore the sonification of x-ray scattering data, which are two-dimensional arrays of intensity whose meaning is obscure and non-intuitive. Direct mapping of the experimental data into sound is found to produce timbral sonifications that, while sacrificing conventional aesthetic appeal, provide a rich auditory landscape for exploration. We discuss the optimization of sonification variables, and speculate on potential real-world applications. We have presented a case study of sonifying x-ray scattering data. Direct mapping of the two-dimensional intensity values of a scattering dataset into the two-dimensional matrix of a sonogram is a natural and information-preserving operation that creates rich sounds. Ourmore » work supports the notion that many problems in understanding rather abstract scientific datasets can be ameliorated by adding the auditory modality of sonification. We further emphasize that sonification need not be limited to time-series data: any data matrix is amenable. Timbral sonification is less obviously aesthetic, than tonal sonification, which generate melody, harmony, or rhythm. However these musical sonifications necessarily sacrifice information content for beauty. Timbral sonification is useful because the entire dataset is represented. Non-musicians can understand the data through the overall color of the sound; audio experts can extract more detailed insight by studying all the features of the sound.« less

Development and Evaluation of High-Resolution Climate Simulations Over the Mountainous Northeastern United States

NASA Technical Reports Server (NTRS)

Winter, Jonathan M.; Beckage, Brian; Bucini, Gabriela; Horton, Radley M.; Clemins, Patrick J.

2016-01-01

The mountain regions of the northeastern United States are a critical socioeconomic resource for Vermont, New York State, New Hampshire, Maine, and southern Quebec. While global climate models (GCMs) are important tools for climate change risk assessment at regional scales, even the increased spatial resolution of statistically downscaled GCMs (commonly approximately 1/ 8 deg) is not sufficient for hydrologic, ecologic, and land-use modeling of small watersheds within the mountainous Northeast. To address this limitation, an ensemble of topographically downscaled, high-resolution (30"), daily 2-m maximum air temperature; 2-m minimum air temperature; and precipitation simulations are developed for the mountainous Northeast by applying an additional level of downscaling to intermediately downscaled (1/ 8 deg) data using high-resolution topography and station observations. First, observed relationships between 2-m air temperature and elevation and between precipitation and elevation are derived. Then, these relationships are combined with spatial interpolation to enhance the resolution of intermediately downscaled GCM simulations. The resulting topographically downscaled dataset is analyzed for its ability to reproduce station observations. Topographic downscaling adds value to intermediately downscaled maximum and minimum 2-m air temperature at high-elevation stations, as well as moderately improves domain-averaged maximum and minimum 2-m air temperature. Topographic downscaling also improves mean precipitation but not daily probability distributions of precipitation. Overall, the utility of topographic downscaling is dependent on the initial bias of the intermediately downscaled product and the magnitude of the elevation adjustment. As the initial bias or elevation adjustment increases, more value is added to the topographically downscaled product.

The radiocarbon reservoir age of the Chukchi Sea, Arctic Ocean

NASA Astrophysics Data System (ADS)

Pearce, C.; Gyllencreutz, R.; West, G.; O'Regan, M.; Jakobsson, M.

2017-12-01

Radiocarbon (14C) dating is the standard method for obtaining the age of marine sediments of Holocene and late Pleistocene age. For accurate calibrations, however, this tool relies on precise knowledge of the local radiocarbon reservoir age of the surface ocean, i.e. the regional difference (ΔR) from the average global marine calibration dataset. This parameter has become impossible to measure from modern mollusk samples because of 14C contamination from extensive testing of thermo-nuclear bombs in the second half of the twentieth century. The local reservoir age can thus only be calculated from the radiocarbon age of samples collected before AD 1950 or from sediment records containing absolute age markers, derived from e.g. tephrochronology or paleomagnetism. Knowledge of the marine reservoir age in the Arctic Ocean is extremely sparse, and relies on work by only a few studies. No information exists for the entire East Siberian Sea, and the Chukchi Sea is represented solely by sites along the Alaskan coast. Here we present new radiocarbon measurements on historical mollusk collections from the East Siberian and Chukchi margins. Our results show a clear and consistent signal of "old" Pacific Water in the Chukchi Sea with ΔR values around 450 years. Towards the East Siberian Sea the values drop as Pacific Water has decreased influence further away from the Bering Strait. Complementing the modern data, we also provide constraints on the reservoir age during the late Holocene. These are based on tephrochronology and high resolution analyses of paleomagnetic secular variation from a sediment archive from Herald Canyon, Chukchi Sea.

Education-Adjusted Normality Thresholds for FDG-PET in the Diagnosis of Alzheimer Disease.

PubMed

Mainta, Ismini C; Trombella, Sara; Morbelli, Silvia; Frisoni, Giovanni B; Garibotto, Valentina

2018-06-05

A corollary of the reserve hypothesis is that what is regarded as pathological cortical metabolism in patients might vary according to education. The aim of this study is to assess the incremental diagnostic value of education-adjusted over unadjusted thresholds on the diagnostic accuracy of FDG-PET as a biomarker for Alzheimer disease (AD). We compared cortical metabolism in 90 healthy controls and 181 AD patients from the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The AUC of the ROC curve did not differ significantly between the whole group and the higher-education patients or the lower-education subjects. The threshold of wMetaROI values providing 80% sensitivity was lower in higher-education patients and higher in the lower-education patients, compared to the standard threshold derived over the whole AD collective, without, however, significant changes in sensitivity and specificity. These data show that education, as a proxy of reserve, is not a major confounder in the diagnostic accuracy of FDG-PET in AD and the adoption of education-adjusted thresholds is not required in daily practice. © 2018 S. Karger AG, Basel.

Derivation of a Provisional, Age-dependent, AIS2+ Thoracic Risk Curve for the THOR50 Test Dummy via Integration of NASS Cases, PMHS Tests, and Simulation Data.

PubMed

Laituri, Tony R; Henry, Scott; El-Jawahri, Raed; Muralidharan, Nirmal; Li, Guosong; Nutt, Marvin

2015-11-01

A provisional, age-dependent thoracic risk equation (or, "risk curve") was derived to estimate moderate-to-fatal injury potential (AIS2+), pertaining to men with responses gaged by the advanced mid-sized male test dummy (THOR50). The derivation involved two distinct data sources: cases from real-world crashes (e.g., the National Automotive Sampling System, NASS) and cases involving post-mortem human subjects (PMHS). The derivation was therefore more comprehensive, as NASS datasets generally skew towards younger occupants, and PMHS datasets generally skew towards older occupants. However, known deficiencies had to be addressed (e.g., the NASS cases had unknown stimuli, and the PMHS tests required transformation of known stimuli into THOR50 stimuli). For the NASS portion of the analysis, chest-injury outcomes for adult male drivers about the size of the THOR50 were collected from real-world, 11-1 o'clock, full-engagement frontal crashes (NASS, 1995-2012 calendar years, 1985-2012 model-year light passenger vehicles). The screening for THOR50-sized men involved application of a set of newly-derived "correction" equations for self-reported height and weight data in NASS. Finally, THOR50 stimuli were estimated via field simulations involving attendant representative restraint systems, and those stimuli were then assigned to corresponding NASS cases (n=508). For the PMHS portion of the analysis, simulation-based closure equations were developed to convert PMHS stimuli into THOR50 stimuli. Specifically, closure equations were derived for the four measurement locations on the THOR50 chest by cross-correlating the results of matched-loading simulations between the test dummy and the age-dependent, Ford Human Body Model. The resulting closure equations demonstrated acceptable fidelity (n=75 matched simulations, R2≥0.99). These equations were applied to the THOR50-sized men in the PMHS dataset (n=20). The NASS and PMHS datasets were combined and subjected to survival analysis with event-frequency weighting and arbitrary censoring. The resulting risk curve--a function of peak THOR50 chest compression and age--demonstrated acceptable fidelity for recovering the AIS2+ chest injury rate of the combined dataset (i.e., IR_dataset=1.97% vs. curve-based IR_dataset=1.98%). Additional sensitivity analyses showed that (a) binary logistic regression yielded a risk curve with nearly-identical fidelity, (b) there was only a slight advantage of combining the small-sample PMHS dataset with the large-sample NASS dataset, (c) use of the PMHS-based risk curve for risk estimation of the combined dataset yielded relatively poor performance (194% difference), and (d) when controlling for the type of contact (lab-consistent or not), the resulting risk curves were similar.

Perspective on Biotransformation and De Novo Biosynthesis of Licorice Constituents.

PubMed

Zhao, Yujia; Lv, Bo; Feng, Xudong; Li, Chun

2017-12-27

Licorice, an important herbal medicine, is derived from the dried roots and rhizomes of Glycyrrhiza genus plants. It has been widely used in food, pharmaceutical, tobacco, and cosmetics industries with high economic value. However, overexploitation of licorice resources has severely destroyed the local ecology. Therefore, producing bioactive compounds of licorice through the biotransformation and bioengineering methods is a hot spot in recent years. In this perspective, we comprehensively summarize the biotransformation of licorice constituents into high-value-added derivatives by biocatalysts. Furthermore, successful cases and the strategies for de novo biosynthesizing compounds of licorice in microbes have been summarized. This paper will provide new insights for the further research of licorice.

Understanding controls of hydrologic processes across two headwater monolithological catchments using model-data synthesis

NASA Astrophysics Data System (ADS)

Xiao, D.; Shi, Y.; Hoagland, B.; Del Vecchio, J.; Russo, T. A.; DiBiase, R. A.; Li, L.

2017-12-01

How do watershed hydrologic processes differ in catchments derived from different lithology? This study compares two first order, deciduous forest watersheds in Pennsylvania, a sandstone watershed, Garner Run (GR, 1.34 km2), and a shale-derived watershed, Shale Hills (SH, 0.08 km2). Both watersheds are simulated using a combination of national datasets and field measurements, and a physics-based land surface hydrologic model, Flux-PIHM. We aim to evaluate the effects of lithology on watershed hydrology and assess if we can simulate a new watershed without intensive measurements, i.e., directly use calibration information from one watershed (SH) to reproduce hydrologic dynamics of another watershed (GR). Without any calibration, the model at GR based on national datasets and calibration inforamtion from SH cannot capture some discharge peaks or the baseflow during dry periods. The model prediction agrees well with the GR field discharge and soil moisture after calibrating the soil hydraulic parameters using the uncertainty based Hornberger-Spear-Young algorithm and the Latin Hypercube Sampling method. Agreeing with the field observation and national datasets, the difference in parameter values shows that the sandstone watershed has a larger averaged soil pore diameter, greater water storage created by porosity, lower water retention ability, and greater preferential flow. The water budget calculation shows that the riparian zone and the colluvial valley serves as buffer zones that stores water at GR. Using the same procedure, we compared Flux-PIHM simulations with and without a field measured surface boulder map at GR. When the boulder map is used, the prediction of areal averaged soil moisture is improved, without performing extra calibration. When calibrated separately, the cases with or without boulder map yield different calibration values, but their hydrologic predictions are similar, showing equifinality. The calibrated soil hydraulic parameter values in the with boulder map case is more physically plausible than the without boulder map case. We switched the topography and soil properties between GR and SH, and results indicate that the hydrologic processes are more sensitive to changes in domain topography than to changes in the soil properties.

The 3D elevation program - Precision agriculture and other farm practices

USGS Publications Warehouse

Sugarbaker, Larry J.; Carswell, Jr., William J.

2016-12-27

A founding motto of the Natural Resources Conservation Service (NRCS), originally the Soil Conservation Service (SCS), explains that “If we take care of the land, it will take care of us.” Digital elevation models (DEMs; see fig. 1) are derived from light detection and ranging (lidar) data and can be processed to derive values such as slope angle, aspect, and topographic curvature. These three measurements are the principal parameters of the NRCS LidarEnhanced Soil Survey (LESS) model, which improves the precision of soil surveys, by more accurately displaying the slopes and soils patterns, while increasing the objectivity and science in line placement. As combined resources, DEMs, LESS model outputs, and similar derived datasets are essential for conserving soil, wetlands, and other natural resources managed and overseen by the NRCS and other Federal and State agencies.

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets.

PubMed

Huang, Min-Wei; Lin, Wei-Chao; Tsai, Chih-Fong

2018-01-01

Many real-world medical datasets contain some proportion of missing (attribute) values. In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based on the (complete) observed data. However, if the observed data contain some noisy information or outliers, the estimations of the missing values may not be reliable or may even be quite different from the real values. The aim of this paper is to examine whether a combination of instance selection from the observed data and missing value imputation offers better performance than performing missing value imputation alone. In particular, three instance selection algorithms, DROP3, GA, and IB3, and three imputation algorithms, KNNI, MLP, and SVM, are used in order to find out the best combination. The experimental results show that that performing instance selection can have a positive impact on missing value imputation over the numerical data type of medical datasets, and specific combinations of instance selection and imputation methods can improve the imputation results over the mixed data type of medical datasets. However, instance selection does not have a definitely positive impact on the imputation result for categorical medical datasets.

NHEXAS PHASE I MARYLAND STUDY--STANDARD OPERATING PROCEDURE FOR LAB RESULTS DATA ENTRY AND PREPARATION (D03)

EPA Science Inventory

The purpose of this SOP is to describe how lab results are organized and processed into the official database known as the Complete Dataset (CDS); to describe the structure and creation of the Analysis-ready Dataset (ADS); and to describe the structure and process of creating the...

U.S. Taxation of Business: Relevance of the European Experience. German Studies Notes.

ERIC Educational Resources Information Center

McLure, Charles E., Jr.

American and European business taxation policies are compared in this booklet. Topics discussed in the paper include effects of the corporation income tax, integration of income taxation, and the value added tax. Two major differences between the American and European systems are noted. First, European countries derive substantial portions of…

Use of biodiesel-derived crude glycerol for producing eicosapentaenoic acid (EPA) by the fungus pythium irregulare

USDA-ARS?s Scientific Manuscript database

Crude glycerol is a major byproduct for the biodiesel industry. Producing value-added products through microbial fermentation on crude glycerol provides opportunities to utilize a large quantity of this byproduct. The objective of this study is to explore the potential of using crude glycerol for ...

The CERAD Neuropsychologic Battery Total Score and the progression of Alzheimer disease.

PubMed

Rossetti, Heidi C; Munro Cullum, C; Hynan, Linda S; Lacritz, Laura H

2010-01-01

To establish the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychologic battery as a valid measure of cognitive progression in Alzheimer disease (AD) by deriving annualized CERAD Total Change Scores and corresponding confidence intervals in AD and controls from which to define clinically meaningful change. Subjects included 383 normal control (NC) and 655 AD subjects with serial data from the CERAD registry database. Annualized CERAD Total Change Scores were derived and Reliable Change Indexes (RCIs) calculated to establish statistically reliable change values. CERAD Change Scores were compared with annualized change scores from the Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale (CDR) Sum of Boxes, and Blessed Dementia Rating Scale (BDRS). For the CERAD Total Score, the AD sample showed significantly greater decline than the NC sample over the 4-year interval, with AD subjects declining an average of 22.2 points compared with the NCs' improving an average 2.8 points from baseline to last visit [Group x Time interaction [F(4,1031)=246.08, P<0.001)]. By Visit 3, the majority of AD subjects (65.2%) showed a degree of cognitive decline that fell outside the RCI. CERAD Change Scores significantly correlated (P<0.001) with MMSE (r=-0.66), CDR (r=-0.42), and BDRS (r=-0.38) change scores. Results support the utility of the CERAD Total Score as a measure of AD progression and provide comparative data for annualized change in CERAD Total Score and other summary measures.

Detailed Modeling and Analysis of the CPFM Dataset

NASA Technical Reports Server (NTRS)

Swartz, William H.; Lloyd, Steven A.; DeMajistre, Robert

2004-01-01

A quantitative understanding of photolysis rate coefficients (or "j-values") is essential to determining the photochemical reaction rates that define ozone loss and other crucial processes in the atmosphere. j-Values can be calculated with radiative transfer models, derived from actinic flux observations, or inferred from trace gas measurements. The principal objective of this study is to cross-validate j-values from the Composition and Photodissociative Flux Measurement (CPFM) instrument during the Photochemistry of Ozone Loss in the Arctic Region In Summer (POLARIS) and SAGE I11 Ozone Loss and Validation Experiment (SOLVE) field campaigns with model calculations and other measurements and to use this detailed analysis to improve our ability to determine j-values. Another objective is to analyze the spectral flux from the CPFM (not just the j-values) and, using a multi-wavelength/multi-species spectral fitting technique, determine atmospheric composition.

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

PubMed Central

Inouye, David I.; Ravikumar, Pradeep; Dhillon, Inderjit S.

2016-01-01

We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models (Yang et al., 2015) did not allow positive dependencies for the exponential and Poisson generalizations. However, in many real-world datasets, variables clearly have positive dependencies. For example, the airport delay time in New York—modeled as an exponential distribution—is positively related to the delay time in Boston. With this motivation, we give an example of our model class derived from the univariate exponential distribution that allows for almost arbitrary positive and negative dependencies with only a mild condition on the parameter matrix—a condition akin to the positive definiteness of the Gaussian covariance matrix. Our Poisson generalization allows for both positive and negative dependencies without any constraints on the parameter values. We also develop parameter estimation methods using node-wise regressions with ℓ1 regularization and likelihood approximation methods using sampling. Finally, we demonstrate our exponential generalization on a synthetic dataset and a real-world dataset of airport delay times. PMID:27563373

Standards-based curation of a decade-old digital repository dataset of molecular information.

PubMed

Harvey, Matthew J; Mason, Nicholas J; McLean, Andrew; Murray-Rust, Peter; Rzepa, Henry S; Stewart, James J P

2015-01-01

The desirable curation of 158,122 molecular geometries derived from the NCI set of reference molecules together with associated properties computed using the MOPAC semi-empirical quantum mechanical method and originally deposited in 2005 into the Cambridge DSpace repository as a data collection is reported. The procedures involved in the curation included annotation of the original data using new MOPAC methods, updating the syntax of the CML documents used to express the data to ensure schema conformance and adding new metadata describing the entries together with a XML schema transformation to map the metadata schema to that used by the DataCite organisation. We have adopted a granularity model in which a DataCite persistent identifier (DOI) is created for each individual molecule to enable data discovery and data metrics at this level using DataCite tools. We recommend that the future research data management (RDM) of the scientific and chemical data components associated with journal articles (the "supporting information") should be conducted in a manner that facilitates automatic periodic curation. Graphical abstractStandards and metadata-based curation of a decade-old digital repository dataset of molecular information.

Time-Series Analysis: A Cautionary Tale

NASA Technical Reports Server (NTRS)

Damadeo, Robert

2015-01-01

Time-series analysis has often been a useful tool in atmospheric science for deriving long-term trends in various atmospherically important parameters (e.g., temperature or the concentration of trace gas species). In particular, time-series analysis has been repeatedly applied to satellite datasets in order to derive the long-term trends in stratospheric ozone, which is a critical atmospheric constituent. However, many of the potential pitfalls relating to the non-uniform sampling of the datasets were often ignored and the results presented by the scientific community have been unknowingly biased. A newly developed and more robust application of this technique is applied to the Stratospheric Aerosol and Gas Experiment (SAGE) II version 7.0 ozone dataset and the previous biases and newly derived trends are presented.

A 1000-year record of dry conditions in the eastern Canadian prairies reconstructed from oxygen and carbon isotope measurements on Lake Winnipeg sediment organics

USGS Publications Warehouse

Buhay, W.M.; Simpson, S.; Thorleifson, H.; Lewis, M.; King, J.; Telka, A.; Wilkinson, Philip M.; Babb, J.; Timsic, S.; Bailey, D.

2009-01-01

A short sediment core (162 cm), covering the period AD 920-1999, was sampled from the south basin of Lake Winnipeg for a suite of multi-proxy analyses leading towards a detailed characterisation of the recent millennial lake environment and hydroclimate of southern Manitoba, Canada. Information on the frequency and duration of major dry periods in southern Manitoba, in light of the changes that are likely to occur as a result of an increasingly warming atmosphere, is of specific interest in this study. Intervals of relatively enriched lake sediment cellulose oxygen isotope values (??18Ocellulose) were found to occur from AD 1180 to 1230 (error range: AD 1104-1231 to 1160-1280), 1610-1640 (error range: AD 1571-1634 to 1603-1662), 1670-1720 (error range: AD 1643-1697 to 1692-1738) and 1750-1780 (error range: AD 1724-1766 to 1756-1794). Regional water balance, inferred from calculated Lake Winnipeg water oxygen isotope values (??18Oinf-lw), suggest that the ratio of lake evaporation to catchment input may have been 25-40% higher during these isotopically distinct periods. Associated with the enriched d??18Ocellulose intervals are some depleted carbon isotope values associated with more abundantly preserved sediment organic matter (d??13COM). These suggest reduced microbial oxidation of terrestrially derived organic matter and/or subdued lake productivity during periods of minimised input of nutrients from the catchment area. With reference to other corroborating evidence, it is suggested that the AD 1180-1230, 1610-1640, 1670-1720 and 1750-1780 intervals represent four distinctly drier periods (droughts) in southern Manitoba, Canada. Additionally, lower-magnitude and duration dry periods may have also occurred from 1320 to 1340 (error range: AD 1257-1363), 1530-1540 (error range: AD 1490-1565 to 1498-1572) and 1570-1580 (error range: AD 1531-1599 to 1539-1606). ?? 2009 John Wiley & Sons, Ltd.

Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data.

PubMed

Drakesmith, M; Caeyenberghs, K; Dutt, A; Lewis, G; David, A S; Jones, D K

2015-09-01

Graph theory (GT) is a powerful framework for quantifying topological features of neuroimaging-derived functional and structural networks. However, false positive (FP) connections arise frequently and influence the inferred topology of networks. Thresholding is often used to overcome this problem, but an appropriate threshold often relies on a priori assumptions, which will alter inferred network topologies. Four common network metrics (global efficiency, mean clustering coefficient, mean betweenness and smallworldness) were tested using a model tractography dataset. It was found that all four network metrics were significantly affected even by just one FP. Results also show that thresholding effectively dampens the impact of FPs, but at the expense of adding significant bias to network metrics. In a larger number (n=248) of tractography datasets, statistics were computed across random group permutations for a range of thresholds, revealing that statistics for network metrics varied significantly more than for non-network metrics (i.e., number of streamlines and number of edges). Varying degrees of network atrophy were introduced artificially to half the datasets, to test sensitivity to genuine group differences. For some network metrics, this atrophy was detected as significant (p<0.05, determined using permutation testing) only across a limited range of thresholds. We propose a multi-threshold permutation correction (MTPC) method, based on the cluster-enhanced permutation correction approach, to identify sustained significant effects across clusters of thresholds. This approach minimises requirements to determine a single threshold a priori. We demonstrate improved sensitivity of MTPC-corrected metrics to genuine group effects compared to an existing approach and demonstrate the use of MTPC on a previously published network analysis of tractography data derived from a clinical population. In conclusion, we show that there are large biases and instability induced by thresholding, making statistical comparisons of network metrics difficult. However, by testing for effects across multiple thresholds using MTPC, true group differences can be robustly identified. Copyright © 2015. Published by Elsevier Inc.

Enabling Open Research Data Discovery through a Recommender System

NASA Astrophysics Data System (ADS)

Devaraju, Anusuriya; Jayasinghe, Gaya; Klump, Jens; Hogan, Dominic

2017-04-01

Government agencies, universities, research and nonprofit organizations are increasingly publishing their datasets to promote transparency, induce new research and generate economic value through the development of new products or services. The datasets may be downloaded from various data portals (data repositories) which are general or domain-specific. The Registry of Research Data Repository (re3data.org) lists more than 2500 such data repositories from around the globe. Data portals allow keyword search and faceted navigation to facilitate discovery of research datasets. However, the volume and variety of datasets have made finding relevant datasets more difficult. Common dataset search mechanisms may be time consuming, may produce irrelevant results and are primarily suitable for users who are familiar with the general structure and contents of the respective database. Therefore, we need new approaches to support research data discovery. Recommender systems offer new possibilities for users to find datasets that are relevant to their research interests. This study presents a recommender system developed for the CSIRO Data Access Portal (DAP, http://data.csiro.au). The datasets hosted on the portal are diverse, published by researchers from 13 business units in the organisation. The goal of the study is not to replace the current search mechanisms on the data portal, but rather to extend the data discovery through an exploratory search, in this case by building a recommender system. We adopted a hybrid recommendation approach, comprising content-based filtering and item-item collaborative filtering. The content-based filtering computes similarities between datasets based on metadata such as title, keywords, descriptions, fields of research, location, contributors, etc. The collaborative filtering utilizes user search behaviour and download patterns derived from the server logs to determine similar datasets. Similarities above are then combined with different degrees of importance (weights) to determine the overall data similarity. We determined the similarity weights based on a survey involving 150 users of the portal. The recommender results for a given dataset are accessible programmatically via a RESTful web service. An offline evaluation involving data users demonstrates the ability of the recommender system to discover relevant and 'novel' datasets.

Tools for proactive collection and use of quality metadata in GEOSS

NASA Astrophysics Data System (ADS)

Bastin, L.; Thum, S.; Maso, J.; Yang, K. X.; Nüst, D.; Van den Broek, M.; Lush, V.; Papeschi, F.; Riverola, A.

2012-12-01

The GEOSS Common Infrastructure allows interactive evaluation and selection of Earth Observation datasets by the scientific community and decision makers, but the data quality information needed to assess fitness for use is often patchy and hard to visualise when comparing candidate datasets. In a number of studies over the past decade, users repeatedly identified the same types of gaps in quality metadata, specifying the need for enhancements such as peer and expert review, better traceability and provenance information, information on citations and usage of a dataset, warning about problems identified with a dataset and potential workarounds, and 'soft knowledge' from data producers (e.g. recommendations for use which are not easily encoded using the existing standards). Despite clear identification of these issues in a number of recommendations, the gaps persist in practice and are highlighted once more in our own, more recent, surveys. This continuing deficit may well be the result of a historic paucity of tools to support the easy documentation and continual review of dataset quality. However, more recent developments in tools and standards, as well as more general technological advances, present the opportunity for a community of scientific users to adopt a more proactive attitude by commenting on their uses of data, and for that feedback to be federated with more traditional and static forms of metadata, allowing a user to more accurately assess the suitability of a dataset for their own specific context and reliability thresholds. The EU FP7 GeoViQua project aims to develop this opportunity by adding data quality representations to the existing search and visualisation functionalities of the Geo Portal. Subsequently we will help to close the gap by providing tools to easily create quality information, and to permit user-friendly exploration of that information as the ultimate incentive for improved data quality documentation. Quality information is derived from producer metadata, from the data themselves, from validation of in-situ sensor data, from provenance information and from user feedback, and will be aggregated to produce clear and useful summaries of quality, including a GEO Label. GeoViQua's conceptual quality information models for users and producers are specifically described and illustrated in this presentation. These models (which have been encoded as XML schemas and can be accessed at http://schemas.geoviqua.org/) are designed to satisfy the identified user needs while remaining consistent with current standards such as ISO 19115 and advanced drafts such as ISO 19157. The resulting components being developed for the GEO Portal are designed to lower the entry barrier to users who wish to help to generate and explore rich and useful metadata. This metadata will include reviews, comments and ratings, reports of usage in specific domains and specification of datasets used for benchmarking, as well as rich quantitative information encoded in more traditional data quality elements such as thematic correctness and positional accuracy. The value of the enriched metadata will also be enhanced by graphical tools for visualizing spatially distributed uncertainties. We demonstrate practical example applications in selected environmental application domains.

Global relationships in river hydromorphology

NASA Astrophysics Data System (ADS)

Pavelsky, T.; Lion, C.; Allen, G. H.; Durand, M. T.; Schumann, G.; Beighley, E.; Yang, X.

2017-12-01

Since the widespread adoption of digital elevation models (DEMs) in the 1980s, most global and continental-scale analysis of river flow characteristics has been focused on measurements derived from DEMs such as drainage area, elevation, and slope. These variables (especially drainage area) have been related to other quantities of interest such as river width, depth, and velocity via empirical relationships that often take the form of power laws. More recently, a number of groups have developed more direct measurements of river location and some aspects of planform geometry from optical satellite imagery on regional, continental, and global scales. However, these satellite-derived datasets often lack many of the qualities that make DEM=derived datasets attractive, including robust network topology. Here, we present analysis of a dataset that combines the Global River Widths from Landsat (GRWL) database of river location, width, and braiding index with a river database extracted from the Shuttle Radar Topography Mission DEM and the HydroSHEDS dataset. Using these combined tools, we present a dataset that includes measurements of river width, slope, braiding index, upstream drainage area, and other variables. The dataset is available everywhere that both datasets are available, which includes all continental areas south of 60N with rivers sufficiently large to be observed with Landsat imagery. We use the dataset to examine patterns and frequencies of river form across continental and global scales as well as global relationships among variables including width, slope, and drainage area. The results demonstrate the complex relationships among different dimensions of river hydromorphology at the global scale.

A database on the distribution of butterflies (Lepidoptera) in northern Belgium (Flanders and the Brussels Capital Region)

PubMed Central

Maes, Dirk; Vanreusel, Wouter; Herremans, Marc; Vantieghem, Pieter; Brosens, Dimitri; Gielen, Karin; Beck, Olivier; Van Dyck, Hans; Desmet, Peter; Natuurpunt, Vlinderwerkgroep

2016-01-01

Abstract In this data paper, we describe two datasets derived from two sources, which collectively represent the most complete overview of butterflies in Flanders and the Brussels Capital Region (northern Belgium). The first dataset (further referred to as the INBO dataset – http://doi.org/10.15468/njgbmh) contains 761,660 records of 70 species and is compiled by the Research Institute for Nature and Forest (INBO) in cooperation with the Butterfly working group of Natuurpunt (Vlinderwerkgroep). It is derived from the database Vlinderdatabank at the INBO, which consists of (historical) collection and literature data (1830-2001), for which all butterfly specimens in institutional and available personal collections were digitized and all entomological and other relevant publications were checked for butterfly distribution data. It also contains observations and monitoring data for the period 1991-2014. The latter type were collected by a (small) butterfly monitoring network where butterflies were recorded using a standardized protocol. The second dataset (further referred to as the Natuurpunt dataset – http://doi.org/10.15468/ezfbee) contains 612,934 records of 63 species and is derived from the database http://waarnemingen.be, hosted at the nature conservation NGO Natuurpunt in collaboration with Stichting Natuurinformatie. This dataset contains butterfly observations by volunteers (citizen scientists), mainly since 2008. Together, these datasets currently contain a total of 1,374,594 records, which are georeferenced using the centroid of their respective 5 × 5 km² Universal Transverse Mercator (UTM) grid cell. Both datasets are published as open data and are available through the Global Biodiversity Information Facility (GBIF). PMID:27199606

A database on the distribution of butterflies (Lepidoptera) in northern Belgium (Flanders and the Brussels Capital Region).

PubMed

Maes, Dirk; Vanreusel, Wouter; Herremans, Marc; Vantieghem, Pieter; Brosens, Dimitri; Gielen, Karin; Beck, Olivier; Van Dyck, Hans; Desmet, Peter; Natuurpunt, Vlinderwerkgroep

2016-01-01

In this data paper, we describe two datasets derived from two sources, which collectively represent the most complete overview of butterflies in Flanders and the Brussels Capital Region (northern Belgium). The first dataset (further referred to as the INBO dataset - http://doi.org/10.15468/njgbmh) contains 761,660 records of 70 species and is compiled by the Research Institute for Nature and Forest (INBO) in cooperation with the Butterfly working group of Natuurpunt (Vlinderwerkgroep). It is derived from the database Vlinderdatabank at the INBO, which consists of (historical) collection and literature data (1830-2001), for which all butterfly specimens in institutional and available personal collections were digitized and all entomological and other relevant publications were checked for butterfly distribution data. It also contains observations and monitoring data for the period 1991-2014. The latter type were collected by a (small) butterfly monitoring network where butterflies were recorded using a standardized protocol. The second dataset (further referred to as the Natuurpunt dataset - http://doi.org/10.15468/ezfbee) contains 612,934 records of 63 species and is derived from the database http://waarnemingen.be, hosted at the nature conservation NGO Natuurpunt in collaboration with Stichting Natuurinformatie. This dataset contains butterfly observations by volunteers (citizen scientists), mainly since 2008. Together, these datasets currently contain a total of 1,374,594 records, which are georeferenced using the centroid of their respective 5 × 5 km² Universal Transverse Mercator (UTM) grid cell. Both datasets are published as open data and are available through the Global Biodiversity Information Facility (GBIF).

Identification of Differentially Expressed Genes through Integrated Study of Alzheimer’s Disease Affected Brain Regions

PubMed Central

Berretta, Regina; Moscato, Pablo

2016-01-01

Background Alzheimer’s disease (AD) is the most common form of dementia in older adults that damages the brain and results in impaired memory, thinking and behaviour. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. In the past decade, several studies have reported many genes that are associated with AD. This wealth of information has become difficult to follow and interpret as most of the results are conflicting. In that case, it is worth doing an integrated study of multiple datasets that helps to increase the total number of samples and the statistical power in detecting biomarkers. In this study, we present an integrated analysis of five different brain region datasets and introduce new genes that warrant further investigation. Methods The aim of our study is to apply a novel combinatorial optimisation based meta-analysis approach to identify differentially expressed genes that are associated to AD across brain regions. In this study, microarray gene expression data from 161 samples (74 non-demented controls, 87 AD) from the Entorhinal Cortex (EC), Hippocampus (HIP), Middle temporal gyrus (MTG), Posterior cingulate cortex (PC), Superior frontal gyrus (SFG) and visual cortex (VCX) brain regions were integrated and analysed using our method. The results are then compared to two popular meta-analysis methods, RankProd and GeneMeta, and to what can be obtained by analysing the individual datasets. Results We find genes related with AD that are consistent with existing studies, and new candidate genes not previously related with AD. Our study confirms the up-regualtion of INFAR2 and PTMA along with the down regulation of GPHN, RAB2A, PSMD14 and FGF. Novel genes PSMB2, WNK1, RPL15, SEMA4C, RWDD2A and LARGE are found to be differentially expressed across all brain regions. Further investigation on these genes may provide new insights into the development of AD. In addition, we identified the presence of 23 non-coding features, including four miRNA precursors (miR-7, miR570, miR-1229 and miR-6821), dysregulated across the brain regions. Furthermore, we compared our results with two popular meta-analysis methods RankProd and GeneMeta to validate our findings and performed a sensitivity analysis by removing one dataset at a time to assess the robustness of our results. These new findings may provide new insights into the disease mechanisms and thus make a significant contribution in the near future towards understanding, prevention and cure of AD. PMID:27050411

Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset

NASA Astrophysics Data System (ADS)

Hancock, Matthew C.; Magnan, Jerry F.

2017-03-01

To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capabilities of statistical learning methods for classifying nodule malignancy, utilizing the Lung Image Database Consortium (LIDC) dataset, and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that is achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (+/-1.14)% which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (+/-0.012), which increases to 0.949 (+/-0.007) when diameter and volume features are included, along with the accuracy to 88.08 (+/-1.11)%. Our results are comparable to those in the literature that use algorithmically-derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features, and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

Osteosarcoma enters a post genomic era with in silico opportunities: Generation of the High Dimensional Database for facilitating sarcoma biology research: A report from the Children's Oncology Group and the QuadW Foundation.

PubMed

Glover, Jason; Man, Tsz-Kwong; Barkauskas, Donald A; Hall, David; Tello, Tanya; Sullivan, Mary Beth; Gorlick, Richard; Janeway, Katherine; Grier, Holcombe; Lau, Ching; Toretsky, Jeffrey A; Borinstein, Scott C; Khanna, Chand; Fan, Timothy M

2017-01-01

The prospective banking of osteosarcoma tissue samples to promote research endeavors has been realized through the establishment of a nationally centralized biospecimen repository, the Children's Oncology Group (COG) biospecimen bank located at the Biopathology Center (BPC)/Nationwide Children's Hospital in Columbus, Ohio. Although the physical inventory of osteosarcoma biospecimens is substantive (>15,000 sample specimens), the nature of these resources remains exhaustible. Despite judicious allocation of these high-value biospecimens for conducting sarcoma-related research, a deeper understanding of osteosarcoma biology, in particular metastases, remains unrealized. In addition the identification and development of novel diagnostics and effective therapeutics remain elusive. The QuadW-COG Childhood Sarcoma Biostatistics and Annotation Office (CSBAO) has developed the High Dimensional Data (HDD) platform to complement the existing physical inventory and to promote in silico hypothesis testing in sarcoma biology. The HDD is a relational biologic database derived from matched osteosarcoma biospecimens in which diverse experimental readouts have been generated and digitally deposited. As proof-of-concept, we demonstrate that the HDD platform can be utilized to address previously unrealized biologic questions though the systematic juxtaposition of diverse datasets derived from shared biospecimens. The continued population of the HDD platform with high-value, high-throughput and mineable datasets allows a shared and reusable resource for researchers, both experimentalists and bioinformatics investigators, to propose and answer questions in silico that advance our understanding of osteosarcoma biology.

Interoperability Between Coastal Web Atlases Using Semantic Mediation: A Case Study of the International Coastal Atlas Network (ICAN)

NASA Astrophysics Data System (ADS)

Wright, D. J.; Lassoued, Y.; Dwyer, N.; Haddad, T.; Bermudez, L. E.; Dunne, D.

2009-12-01

Coastal mapping plays an important role in informing marine spatial planning, resource management, maritime safety, hazard assessment and even national sovereignty. As such, there is now a plethora of data/metadata catalogs, pre-made maps, tabular and text information on resource availability and exploitation, and decision-making tools. A recent trend has been to encapsulate these in a special class of web-enabled geographic information systems called a coastal web atlas (CWA). While multiple benefits are derived from tailor-made atlases, there is great value added from the integration of disparate CWAs. CWAs linked to one another can query more successfully to optimize planning and decision-making. If a dataset is missing in one atlas, it may be immediately located in another. Similar datasets in two atlases may be combined to enhance study in either region. *But how best to achieve semantic interoperability to mitigate vague data queries, concepts or natural language semantics when retrieving and integrating data and information?* We report on the development of a new prototype seeking to interoperate between two initial CWAs: the Marine Irish Digital Atlas (MIDA) and the Oregon Coastal Atlas (OCA). These two mature atlases are used as a testbed for more regional connections, with the intent for the OCA to use lessons learned to develop a regional network of CWAs along the west coast, and for MIDA to do the same in building and strengthening atlas networks with the UK, Belgium, and other parts of Europe. Our prototype uses semantic interoperability via services harmonization and ontology mediation, allowing local atlases to use their own data structures, and vocabularies (ontologies). We use standard technologies such as OGC Web Map Services (WMS) for delivering maps, and OGC Catalogue Service for the Web (CSW) for delivering and querying ISO-19139 metadata. The metadata records of a given CWA use a given ontology of terms called local ontology. Human or machine users formulate their requests using a common ontology of metadata terms, called global ontology. A CSW mediator rewrites the user’s request into CSW requests over local CSWs using their own (local) ontologies, collects the results and sends them back to the user. To extend the system, we have recently added global maritime boundaries and are also considering nearshore ocean observing system data. Ongoing work includes adding WFS, error management, and exception handling, enabling Smart Searches, and writing full documentation. This prototype is a central research project of the new International Coastal Atlas Network (ICAN), a group of 30+ organizations from 14 nations (and growing) dedicated to seeking interoperability approaches to CWAs in support of coastal zone management and the translation of coastal science to coastal decision-making.

Photolysis Rate Coefficient Calculations in Support of SOLVE Campaign

NASA Technical Reports Server (NTRS)

Lloyd, Steven A.; Swartz, William H.

2001-01-01

The objectives for this SOLVE project were 3-fold. First, we sought to calculate a complete set of photolysis rate coefficients (j-values) for the campaign along the ER-2 and DC-8 flight tracks. En route to this goal, it would be necessary to develop a comprehensive set of input geophysical conditions (e.g., ozone profiles), derived from various climatological, aircraft, and remotely sensed datasets, in order to model the radiative transfer of the atmosphere accurately. These j-values would then need validation by comparison with flux-derived j-value measurements. The second objective was to analyze chemistry along back trajectories using the NASA/Goddard chemistry trajectory model initialized with measurements of trace atmospheric constituents. This modeling effort would provide insight into the completeness of current measurements and the chemistry of Arctic wintertime ozone loss. Finally, we sought to coordinate stellar occultation measurements of ozone (and thus ozone loss) during SOLVE using the MSX/UVISI satellite instrument. Such measurements would determine ozone loss during the Arctic polar night and represent the first significant science application of space-based stellar occultation in the Earth's atmosphere.

Association of δ13C in Fingerstick Blood with Added Sugars and Sugar-sweetened Beverage Intake

PubMed Central

Davy, Brenda M.; Jahren, A. Hope; Hedrick, Valisa E.; Comber, Dana L.

2011-01-01

A reliance on self-reported dietary intake measures is a common research limitation, thus the need for dietary biomarkers. Added sugar intake may play a role in the development and progression of obesity and related co-morbidities; common sweeteners include corn and sugar cane derivatives. These plants contain a high amount of 13C, a naturally-occurring stable carbon isotope. Consumption of these sweeteners, of which sugar-sweetened beverages (SSB) are the primary dietary source, may be reflected in the δ13C value of blood. Fingerstick blood represents an ideal substrate for bioassay due to its ease of acquisition. The objective of this investigation was to determine if the δ13C value of fingerstick blood is a potential biomarker of added sugar and SSB intake. Individuals aged ≥21 years (n=60) were recruited to attend three laboratory visits; assessments completed at each visit depended upon a randomly assigned sequence (sequence one or two). The initial visit included assessment of height, weight, and dietary intake (sequence one: beverage intake questionnaire [BEVQ], sequence two: four-day food intake record [FIR]). Sequence one participants completed an FIR at visit two, and non-fasting blood samples were obtained via routine finger sticks at visits one and three. Sequence two participants completed a BEVQ at visit two, and provided fingerstick blood samples at visits two and three. Samples were analyzed for δ13C value using natural abundance stable isotope mass spectrometry. δ13C value was compared to dietary outcomes in all participants, as well as among those in the highest and lowest tertile of added sugar intake. Reported mean added sugar consumption was 66±5g/day, and SSB consumption was 330±53g/day and 134±25 kcal/day. Mean fingerstick δ13C value was −19.94±0.10‰, which differed by BMI status. δ13C value was associated (all p<0.05) with intake of total added sugars (g, r=0.37; kcal, r=0.37), soft drinks (g, r=0.26; kcal, r=0.27), and total SSB (g, r=0.28; kcal, r=0.35). The δ13C value in the lowest and the highest added sugar intake tertiles were significantly different (mean difference = −0.48‰, p=0.028). Even though there are several potential dietary sources for blood carbon, the δ13C value of fingerstick blood shows promise as a non-invasive biomarker of added sugar and SSB intake based on these findings. PMID:21616200

The DataBridge: A System For Optimizing The Use Of Dark Data From The Long Tail Of Science

NASA Astrophysics Data System (ADS)

Lander, H.; Rajasekar, A.

2015-12-01

The DataBridge is a National Science Foundation funded collaborative project (OCI-1247652, OCI-1247602, OCI-1247663) designed to assist in the discovery of dark data sets from the long tail of science. The DataBridge aims to to build queryable communities of datasets using sociometric network analysis. This approach is being tested to evaluate the ability to leverage various forms of metadata to facilitate discovery of new knowledge. Each dataset in the Databridge has an associated name space used as a first level partitioning. In addition to testing known algorithms for SNA community building, the DataBridge project has built a message-based platform that allows users to provide their own algorithms for each of the stages in the community building process. The stages are: Signature Generation (SG): An SG algorithm creates a metadata signature for a dataset. Signature algorithms might use text metadata provided by the dataset creator or derive metadata. Relevance Algorithm (RA): An RA compares a pair of datasets and produces a similarity value between 0 and 1 for the two datasets. Sociometric Network Analysis (SNA): The SNA will operate on a similarity matrix produced by an RA to partition all of the datasets in the name space into a set of clusters. These clusters represent communities of closely related datasets. The DataBridge also includes a web application that produces a visual representation of the clustering. Future work includes a more complete application that will allow different types of searching of the network of datasets. The DataBridge approach is relevant to geoscience research and informatics. In this presentation we will outline the project, illustrate the deployment of the approach, and discuss other potential applications and next steps for the research such as applying this approach to models. In addition we will explore the relevance of DataBridge to other geoscience projects such as various EarthCube Building Blocks and DIBBS projects.

Comparison of Different Machine Learning Algorithms for Lithological Mapping Using Remote Sensing Data and Morphological Features: A Case Study in Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan; Gloaguen, Richard

2015-04-01

Topographic effects and complex vegetation cover hinder lithology classification in mountain regions based not only in field, but also in reflectance remote sensing data. The area of interest "Bardi-Zard" is located in the NE of Iraq. It is part of the Zagros orogenic belt, where seven lithological units outcrop and is known for its chromite deposit. The aim of this study is to compare three machine learning algorithms (MLAs): Maximum Likelihood (ML), Support Vector Machines (SVM), and Random Forest (RF) in the context of a supervised lithology classification task using Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite, its derived, spatial information (spatial coordinates) and geomorphic data. We emphasize the enhancement in remote sensing lithological mapping accuracy that arises from the integration of geomorphic features and spatial information (spatial coordinates) in classifications. This study identifies that RF is better than ML and SVM algorithms in almost the sixteen combination datasets, which were tested. The overall accuracy of the best dataset combination with the RF map for the all seven classes reach ~80% and the producer and user's accuracies are ~73.91% and 76.09% respectively while the kappa coefficient is ~0.76. TPI is more effective with SVM algorithm than an RF algorithm. This paper demonstrates that adding geomorphic indices such as TPI and spatial information in the dataset increases the lithological classification accuracy.

A synthesis of light absorption properties of the Pan-Arctic Ocean: application to semi-analytical estimates of dissolved organic carbon concentrations from space

NASA Astrophysics Data System (ADS)

Matsuoka, A.; Babin, M.; Doxaran, D.; Hooker, S. B.; Mitchell, B. G.; Bélanger, S.; Bricaud, A.

2013-11-01

The light absorption coefficients of particulate and dissolved materials are the main factors determining the light propagation of the visible part of the spectrum and are, thus, important for developing ocean color algorithms. While these absorption properties have recently been documented by a few studies for the Arctic Ocean (e.g., Matsuoka et al., 2007, 2011; Ben Mustapha et al., 2012), the datasets used in the literature were sparse and individually insufficient to draw a general view of the basin-wide spatial and temporal variations in absorption. To achieve such a task, we built a large absorption database at the pan-Arctic scale by pooling the majority of published datasets and merging new datasets. Our results showed that the total non-water absorption coefficients measured in the Eastern Arctic Ocean (EAO; Siberian side) are significantly higher than in the Western Arctic Ocean (WAO; North American side). This higher absorption is explained by higher concentration of colored dissolved organic matter (CDOM) in watersheds on the Siberian side, which contains a large amount of dissolved organic carbon (DOC) compared to waters off North America. In contrast, the relationship between the phytoplankton absorption (aφ(λ)) and chlorophyll a (chl a) concentration in the EAO was not significantly different from that in the WAO. Because our semi-analytical CDOM absorption algorithm is based on chl a-specific aφ(λ) values (Matsuoka et al., 2013), this result indirectly suggests that CDOM absorption can be appropriately derived not only for the WAO but also for the EAO using ocean color data. Derived CDOM absorption values were reasonable compared to in situ measurements. By combining this algorithm with empirical DOC vs. CDOM relationships, a semi-analytical algorithm for estimating DOC concentrations for coastal waters at the Pan-Arctic scale is presented and applied to satellite ocean color data.

Spatiotemporal image correlation-derived volumetric Doppler impedance indices from spherical samples of the placenta: intraobserver reliability and correlation with conventional umbilical artery Doppler indices.

PubMed

Welsh, A W; Hou, M; Meriki, N; Martins, W P

2012-10-01

Volumetric impedance indices derived from spatiotemporal image correlation (STIC) power Doppler ultrasound (PDU) might overcome the influence of machine settings and attenuation. We examined the feasibility of obtaining these indices from spherical samples of anterior placentas in healthy pregnancies, and assessed intraobserver reliability and correlation with conventional umbilical artery (UA) impedance indices. Uncomplicated singleton pregnancies with anterior placenta were included in the study. A single observer evaluated UA pulsatility index (PI), resistance index (RI) and systolic/diastolic ratio (S/D) and acquired three STIC-PDU datasets from the placenta just above the placental cord insertion. Another observer analyzed the STIC-PDU datasets using Virtual Organ Computer-aided AnaLysis (VOCAL) spherical samples from every frame to determine the vascularization index (VI) and vascularization flow index (VFI); maximum, minimum and average values were used to determine the three volumetric impedance indices (vPI, vRI, vS/D). Intraobserver reliability was examined by intraclass correlation coefficients (ICC) and association between volumetric indices from placenta, and UA Doppler indices were assessed by Pearson's correlation coefficient. A total of 25 pregnant women were evaluated but five were excluded because of artifacts observed during analysis. The reliability of measurement of volumetric indices of both VI and VFI from three STIC-PDU datasets was similar, with all ICCs ≥ 0.78. Pearson's r values showed a weak and non-significant correlation between UA pulsed-wave Doppler indices and their respective volumetric indices from spherical samples of placenta (all r ≥ 0.23). VOCAL indices from specific phases of the cardiac cycle showed good repeatability (ICC ≥ 0.92). Volumetric impedance indices determined from spherical samples of placenta are sufficiently reliable but do not correlate with UA Doppler indices in healthy pregnancies. Copyright © 2012 ISUOG. Published by John Wiley & Sons, Ltd.

The Neotoma Paleoecology Database: An International Community-Curated Resource for Paleoecological and Paleoenvironmental Data

NASA Astrophysics Data System (ADS)

Williams, J. W.; Grimm, E. C.; Ashworth, A. C.; Blois, J.; Charles, D. F.; Crawford, S.; Davis, E.; Goring, S. J.; Graham, R. W.; Miller, D. A.; Smith, A. J.; Stryker, M.; Uhen, M. D.

2017-12-01

The Neotoma Paleoecology Database supports global change research at the intersection of geology and ecology by providing a high-quality, community-curated data repository for paleoecological data. These data are widely used to study biological responses and feedbacks to past environmental change at local to global scales. The Neotoma data model is flexible and can store multiple kinds of fossil, biogeochemical, or physical variables measured from sedimentary archives. Data additions to Neotoma are growing and include >3.5 million observations, >16,000 datasets, and >8,500 sites. Dataset types include fossil pollen, vertebrates, diatoms, ostracodes, macroinvertebrates, plant macrofossils, insects, testate amoebae, geochronological data, and the recently added organic biomarkers, stable isotopes, and specimen-level data. Neotoma data can be found and retrieved in multiple ways, including the Explorer map-based interface, a RESTful Application Programming Interface, the neotoma R package, and digital object identifiers. Neotoma has partnered with the Paleobiology Database to produce a common data portal for paleobiological data, called the Earth Life Consortium. A new embargo management is designed to allow investigators to put their data into Neotoma and then make use of Neotoma's value-added services. Neotoma's distributed scientific governance model is flexible and scalable, with many open pathways for welcoming new members, data contributors, stewards, and research communities. As the volume and variety of scientific data grow, community-curated data resources such as Neotoma have become foundational infrastructure for big data science.

Evaluation of a Method for Estimating Retinal Ganglion Cell Counts Using Visual Fields and Optical Coherence Tomography

PubMed Central

Raza, Ali S.; Hood, Donald C.

2015-01-01

Purpose. To evaluate the accuracy and generalizability of a published model that derives estimates of retinal ganglion cell (RGC) counts and relates structural and functional changes due to glaucoma. Methods. Both the Harwerth et al. nonlinear model (H-NLM) and the Hood and Kardon linear model (HK-LM) were applied to an independent dataset of frequency-domain optical coherence tomography and visual fields, consisting of 48 eyes of 48 healthy controls, 100 eyes of 77 glaucoma patients and suspects, and 18 eyes of 14 nonarteritic anterior ischemic optic neuropathy (ION) patients with severe vision loss. Using the coefficient of determination R2, the models were compared while keeping constant the topographic maps, specifically a map by Garway-Heath et al. and a separate map by Harwerth et al., which relate sensitivity test stimulus locations to corresponding regions around the optic disc. Additionally, simulations were used to evaluate the assumptions of the H-NLM. Results. Although the predictions of the HK-LM with the anatomically-derived Garway-Heath et al. map were reasonably good (R2 = 0.31–0.64), the predictions of the H-NLM were poor (R2 < 0) regardless of the map used. Furthermore, simulations of the H-NLM yielded results that differed substantially from RGC estimates based on histology from human subjects. Finally, the value-added of factors increasing the relative complexity of the H-NLM, such as assumptions regarding age- and stage-dependent corrections to structural measures, was unclear. Conclusions. Several of the assumptions underlying the H-NLM should be revisited. Studies and models relying on the RGC estimates of the H-NLM should be interpreted with caution. PMID:25604684

DISSOLUTION OF LANTHANUM FLUORIDE PRECIPITATES

DOEpatents

Fries, B.A.

1959-11-10

A plutonium separatory ore concentration procedure involving the use of a fluoride type of carrier is presented. An improvement is given in the derivation step in the process for plutonium recovery by carrier precipitation of plutonium values from solution with a lanthanum fluoride carrier precipitate and subsequent derivation from the resulting plutonium bearing carrier precipitate of an aqueous acidic plutonium-containing solution. The carrier precipitate is contacted with a concentrated aqueous solution of potassium carbonate to effect dissolution therein of at least a part of the precipitate, including the plutonium values. Any remaining precipitate is separated from the resulting solution and dissolves in an aqueous solution containing at least 20% by weight of potassium carbonate. The reacting solutions are combined, and an alkali metal hydroxide added to a concentration of at least 2N to precipitate lanthanum hydroxide concomitantly carrying plutonium values.

In Vitro Screening for Anti-Cholinesterase and Antioxidant Activity of Methanolic Extracts of Ayurvedic Medicinal Plants Used for Cognitive Disorders

PubMed Central

Mathew, Maya; Subramanian, Sarada

2014-01-01

Inhibition of Acetylcholinesterase (AChE) is still considered as the main therapeutic strategy against Alzheimer’s disease (AD). Many plant derived phytochemicals have shown AChE inhibitory activity in addition to the currently approved drugs for AD. In the present study, methanolic extracts of 20 plants used in Indian Ayurvedic system of medicine for improving cognitive function were screened for acetylcholinesterase inhibitory activity by Ellman’s microplate colorimetric method. Out of 20 extracts, Emblica officinalis, Nardostachys jatamansi, Nelumbo nucifera, Punica granatum and Raulfia Serpentina showed IC50 values <100 µg/ml for acetylcholinesterase inhibitory activity. Antioxidant activities of these plants were assessed by DPPH scavenging assay. Among the extracts used, antioxidant activity was highest for Terminalia chebula and Emblica officinalis with IC50 values <10 µg/ml. Considering the complex multifactorial etiology of AD, these plant extracts will be safer and better candidates for the future disease modifying therapies against this devastating disease. PMID:24466247

In vitro screening for anti-cholinesterase and antioxidant activity of methanolic extracts of ayurvedic medicinal plants used for cognitive disorders.

PubMed

Mathew, Maya; Subramanian, Sarada

2014-01-01

Inhibition of Acetylcholinesterase (AChE) is still considered as the main therapeutic strategy against Alzheimer's disease (AD). Many plant derived phytochemicals have shown AChE inhibitory activity in addition to the currently approved drugs for AD. In the present study, methanolic extracts of 20 plants used in Indian Ayurvedic system of medicine for improving cognitive function were screened for acetylcholinesterase inhibitory activity by Ellman's microplate colorimetric method. Out of 20 extracts, Emblica officinalis, Nardostachys jatamansi, Nelumbo nucifera, Punica granatum and Raulfia Serpentina showed IC50 values <100 µg/ml for acetylcholinesterase inhibitory activity. Antioxidant activities of these plants were assessed by DPPH scavenging assay. Among the extracts used, antioxidant activity was highest for Terminalia chebula and Emblica officinalis with IC50 values <10 µg/ml. Considering the complex multifactorial etiology of AD, these plant extracts will be safer and better candidates for the future disease modifying therapies against this devastating disease.

Meson effective mass in the isospin medium in hard-wall AdS/QCD model

NASA Astrophysics Data System (ADS)

Mamedov, Shahin

2016-02-01

We study a mass splitting of the light vector, axial-vector, and pseudoscalar mesons in the isospin medium in the framework of the hard-wall model. We write an effective mass definition for the interacting gauge fields and scalar field introduced in gauge field theory in the bulk of AdS space-time. Relying on holographic duality we obtain a formula for the effective mass of a boundary meson in terms of derivative operator over the extra bulk coordinate. The effective mass found in this way coincides with the one obtained from finding of poles of the two-point correlation function. In order to avoid introducing distinguished infrared boundaries in the quantization formula for the different mesons from the same isotriplet we introduce extra action terms at this boundary, which reduces distinguished values of this boundary to the same value. Profile function solutions and effective mass expressions were found for the in-medium ρ , a_1, and π mesons.

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies

PubMed Central

Jia, Erik; Chen, Tianlu

2018-01-01

Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: https://github.com/WandeRum/GSimp. PMID:29385130

Boosting Classification Accuracy of Diffusion MRI Derived Brain Networks for the Subtypes of Mild Cognitive Impairment Using Higher Order Singular Value Decomposition

PubMed Central

Zhan, L.; Liu, Y.; Zhou, J.; Ye, J.; Thompson, P.M.

2015-01-01

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's disease (AD), and around 10-15% of people with MCI develop AD each year. More recently, MCI has been further subdivided into early and late stages, and there is interest in identifying sensitive brain imaging biomarkers that help to differentiate stages of MCI. Here, we focused on anatomical brain networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying early versus late MCI. PMID:26413202

Overview of groundwater quality in the Piceance Basin, western Colorado, 1946--2009

USGS Publications Warehouse

Thomas, J.C.; McMahon, P.B.

2013-01-01

Groundwater-quality data from public and private sources for the period 1946 to 2009 were compiled and put into a common data repository for the Piceance Basin. The data repository is available on the web at http://rmgsc.cr.usgs.gov/cwqdr/Piceance/index.shtml. A subset of groundwater-quality data from the repository was compiled, reviewed, and checked for quality assurance for this report. The resulting dataset consists of the most recently collected sample from 1,545 wells, 1,007 (65 percent) of which were domestic wells. From those samples, the following constituents were selected for presentation in this report: dissolved oxygen, dissolved solids, pH, major ions (chloride, sulfate, fluoride), trace elements (arsenic, barium, iron, manganese, selenium), nitrate, benzene, toluene, ethylbenzene, xylene, methane, and the stable isotopic compositions of water and methane. Some portion of recharge to most of the wells for which data were available was derived from precipitation (most likely snowmelt), as indicated by δ2H [H2O] and δ18O[H2O] values that plot along the Global Meteoric Water Line and near the values for snow samples collected in the study area. Ninety-three percent of the samples were oxic, on the basis of concentrations of dissolved oxygen that were greater than or equal to 0.5 milligrams per liter. Concentration data were compared with primary and secondary drinking-water standards established by the U.S. Environmental Protection Agency. Constituents that exceeded the primary standards were arsenic (13 percent), selenium (9.2 percent), fluoride (8.4 percent), barium (4.1 percent), nitrate (1.6 percent), and benzene (0.6 percent). Concentrations of toluene, xylenes, and ethylbenzene did not exceed standards in any samples. Constituents that exceeded the secondary standard were dissolved solids (72 percent), sulfate (37 percent), manganese (21 percent), iron (16 percent), and chloride (10 percent). Drinking-water standards have not been established for methane, which was detected in 24 percent of samples. Methane concentrations were greater than or equal to 1 milligram per liter in 8.5 percent of samples. Methane isotopic data for samples collected primarily from domestic wells in Garfield County indicate that methane in samples with relative high methane concentrations were derived from both biogenic and thermogenic sources. Many of the constituents that exceeded standards, such as arsenic, fluoride, iron, and manganese, were derived from rock and sediment in aquifers. Elevated nitrate concentrations were most likely derived from human sources such as fertilizer and human or animal waste. Information about the geologic unit or aquifer in which a well was completed generally was not provided by data sources. However, limited data indicate that Quaternary deposits in Garfield and Mesa Counties, the Wasatch Formation in Garfield County, and the Green River Formation in Rio Blanco County had some of the highest median concentrations of selected constituents. Variations in concentration with depth could not be evaluated because of the general lack of well-depth and water-level data. Concentrations of several important constituents, such as arsenic, manganese, methane, and nitrate, were related to concentrations of dissolved oxygen. Concentrations of arsenic, manganese, and methane were significantly higher in groundwater with low dissolved-oxygen concentrations than in groundwater with high dissolved-oxygen concentrations. In contrast, concentrations of nitrate were significantly higher in groundwater with high dissolved-oxygen concentrations than in groundwater with low dissolved-oxygen concentrations. These results indicate that measurements of dissolved oxygen may be a useful indicator of groundwater vulnerability to some human-derived contaminants and enrichment from some natural constituents. Assessing such a large and diverse dataset as the one available through the repository poses unique challenges for reporting on groundwater quality in the study area. The repository contains data from several studies that differed widely in purpose and scope. In addition to this variability in available data, gaps exist spatially, temporally, and analytically in the repository. For example, groundwater-quality data in the repository were not evenly distributed throughout the study area. Several key water-quality constituents or indicators, such as dissolved oxygen, were underrepresented in the repository. Ancillary information, such as well depth, depth to water, and the geologic unit or aquifer in which a well was completed, was missing for more than 50 percent of samples. Future monitoring could avoid several limitations of the repository by making relatively minor changes to sample- collection and data-reporting protocols. Field measurements for dissolved oxygen could be added to sampling protocols, for example. Information on well construction and the geologic unit or aquifer in which a well was completed should be part of the water-quality dataset. Such changes would increase the comparability of data from different monitoring programs and also add value to each program individually and to that of the regional dataset as a whole. Other changes to monitoring programs could require greater resources, such as sampling for a basic set of constituents that is relevant to major water-quality issues in the regional study area. Creation of such a dataset for the regional study area would help to provide the kinds of information needed to characterize background conditions and the spatial and temporal variability in constituent concentrations associated with those conditions. Without such information, it is difficult to identify departures from background that might be associated with human activities.

White matter hyperintensities and cerebral amyloidosis: necessary and sufficient for clinical expression of Alzheimer disease?

PubMed

Provenzano, Frank A; Muraskin, Jordan; Tosto, Giuseppe; Narkhede, Atul; Wasserman, Ben T; Griffith, Erica Y; Guzman, Vanessa A; Meier, Irene B; Zimmerman, Molly E; Brickman, Adam M

2013-04-01

Current hypothetical models emphasize the importance of β-amyloid in Alzheimer disease (AD) pathogenesis, although amyloid alone is not sufficient to account for the dementia syndrome. The impact of small-vessel cerebrovascular disease, visualized as white matter hyperintensities (WMHs) on magnetic resonance imaging scans, may be a key factor that contributes independently to AD presentation. To determine the impact of WMHs and Pittsburgh Compound B (PIB) positron-emission tomography-derived amyloid positivity on the clinical expression of AD. Baseline PIB-positron-emission tomography values were downloaded from the Alzheimer's Disease Neuroimaging Initiative database. Total WMH volume was derived on accompanying structural magnetic resonance imaging data. We examined whether PIB positivity and total WMHs predicted diagnostic classification of patients with AD (n = 20) and control subjects (n = 21). A second analysis determined whether WMHs discriminated between those with and without the clinical diagnosis of AD among those who were classified as PIB positive (n = 28). A third analysis examined whether WMHs, in addition to PIB status, could be used to predict future risk for AD among subjects with mild cognitive impairment (n = 59). The Alzheimer's Disease Neuroimaging Initiative public database. The study involved data from 21 normal control subjects, 59 subjects with mild cognitive impairment, and 20 participants with clinically defined AD from the Alzheimer Disease's Neuroimaging Initiative database. Clinical AD diagnosis and WMH volume. Pittsburgh Compound B positivity and increased total WMH volume independently predicted AD diagnosis. Among PIB-positive subjects, those diagnosed as having AD had greater WMH volume than normal control subjects. Among subjects with mild cognitive impairment, both WMH and PIB status at baseline conferred risk for future diagnosis of AD. White matter hyperintensities contribute to the presentation of AD and, in the context of significant amyloid deposition, may provide a second hit necessary for the clinical manifestation of the disease. As risk factors for the development of WMHs are modifiable, these findings suggest intervention and prevention strategies for the clinical syndrome of AD.

Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.

PubMed

Giambartolomei, Claudia; Vukcevic, Damjan; Schadt, Eric E; Franke, Lude; Hingorani, Aroon D; Wallace, Chris; Plagnol, Vincent

2014-05-01

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.

The AD775 cosmic event revisited: the Sun is to blame

NASA Astrophysics Data System (ADS)

Usoskin, I. G.; Kromer, B.; Ludlow, F.; Beer, J.; Friedrich, M.; Kovaltsov, G. A.; Solanki, S. K.; Wacker, L.

2013-04-01

Aims: Miyake et al. (2012, Nature, 486, 240, henceforth M12) recently reported, based on 14C data, an extreme cosmic event in about AD775. Using a simple model, M12 claimed that the event was too strong to be caused by a solar flare within the standard theory. This implied a new paradigm of either an impossibly strong solar flare or a very strong cosmic ray event of unknown origin that occurred around AD775. However, as we show, the strength of the event was significantly overestimated by M12. Several subsequent works have attempted to find a possible exotic source for such an event, including a giant cometary impact upon the Sun or a gamma-ray burst, but they are all based on incorrect estimates by M12. We revisit this event with analysis of new datasets and consistent theoretical modelling. Methods: We verified the experimental result for the AD775 cosmic ray event using independent datasets including 10Be series and newly measured 14C annual data. We surveyed available historical chronicles for astronomical observations for the period around the AD770s to identify potential sightings of aurorae borealis and supernovae. We interpreted the 14C measurements using an appropriate carbon cycle model. Results: We show that: (1) The reality of the AD775 event is confirmed by new measurements of 14C in German oak; (2) by using an inappropriate carbon cycle model, M12 strongly overestimated the event's strength; (3) the revised magnitude of the event (the global 14C production Q = (1.1 - 1.5) × 108 atoms/cm2) is consistent with different independent datasets (14C, 10Be, 36Cl) and can be associated with a strong, but not inexplicably strong, solar energetic particle event (or a sequence of events), and provides the first definite evidence for an event of this magnitude (the fluence >30 MeV was about 4.5 × 1010 cm-2) in multiple datasets; (4) this interpretation is in agreement with increased auroral activity identified in historical chronicles. Conclusions: The results point to the likely solar origin of the event, which is now identified as the greatest solar event on a multi-millennial time scale, placing a strong observational constraint on the theory of explosive energy releases on the Sun and cool stars.

The next generation of melanocyte data: Genetic, epigenetic, and transcriptional resource datasets and analysis tools.

PubMed

Loftus, Stacie K

2018-05-01

The number of melanocyte- and melanoma-derived next generation sequence genome-scale datasets have rapidly expanded over the past several years. This resource guide provides a summary of publicly available sources of melanocyte cell derived whole genome, exome, mRNA and miRNA transcriptome, chromatin accessibility and epigenetic datasets. Also highlighted are bioinformatic resources and tools for visualization and data queries which allow researchers a genome-scale view of the melanocyte. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

Methodology for adding glycemic index and glycemic load values to 24-hour dietary recall database.

PubMed

Olendzki, Barbara C; Ma, Yunsheng; Culver, Annie L; Ockene, Ira S; Griffith, Jennifer A; Hafner, Andrea R; Hebert, James R

2006-01-01

We describe a method of adding the glycemic index (GI) and glycemic load (GL) values to the nutrient database of the 24-hour dietary recall interview (24HR), a widely used dietary assessment. We also calculated daily GI and GL values from the 24HR. Subjects were 641 healthy adults from central Massachusetts who completed 9067 24HRs. The 24HR-derived food data were matched to the International Table of Glycemic Index and Glycemic Load Values. The GI values for specific foods not in the table were estimated against similar foods according to physical and chemical factors that determine GI. Mixed foods were disaggregated into individual ingredients. Of 1261 carbohydrate-containing foods in the database, GI values of 602 foods were obtained from a direct match (47.7%), accounting for 22.36% of dietary carbohydrate. GI values from 656 foods (52.1%) were estimated, contributing to 77.64% of dietary carbohydrate. The GI values from three unknown foods (0.2%) could not be assigned. The average daily GI was 84 (SD 5.1, white bread as referent) and the average GL was 196 (SD 63). Using this methodology for adding GI and GL values to nutrient databases, it is possible to assess associations between GI and/or GL and body weight and chronic disease outcomes (diabetes, cancer, heart disease). This method can be used in clinical and survey research settings where 24HRs are a practical means for assessing diet. The implications for using this methodology compel a broader evaluation of diet with disease outcomes.

Automating Geospatial Visualizations with Smart Default Renderers for Data Exploration Web Applications

NASA Astrophysics Data System (ADS)

Ekenes, K.

2017-12-01

This presentation will outline the process of creating a web application for exploring large amounts of scientific geospatial data using modern automated cartographic techniques. Traditional cartographic methods, including data classification, may inadvertently hide geospatial and statistical patterns in the underlying data. This presentation demonstrates how to use smart web APIs that quickly analyze the data when it loads, and provides suggestions for the most appropriate visualizations based on the statistics of the data. Since there are just a few ways to visualize any given dataset well, it is imperative to provide smart default color schemes tailored to the dataset as opposed to static defaults. Since many users don't go beyond default values, it is imperative that they are provided with smart default visualizations. Multiple functions for automating visualizations are available in the Smart APIs, along with UI elements allowing users to create more than one visualization for a dataset since there isn't a single best way to visualize a given dataset. Since bivariate and multivariate visualizations are particularly difficult to create effectively, this automated approach removes the guesswork out of the process and provides a number of ways to generate multivariate visualizations for the same variables. This allows the user to choose which visualization is most appropriate for their presentation. The methods used in these APIs and the renderers generated by them are not available elsewhere. The presentation will show how statistics can be used as the basis for automating default visualizations of data along continuous ramps, creating more refined visualizations while revealing the spread and outliers of the data. Adding interactive components to instantaneously alter visualizations allows users to unearth spatial patterns previously unknown among one or more variables. These applications may focus on a single dataset that is frequently updated, or configurable for a variety of datasets from multiple sources.

Database citation in supplementary data linked to Europe PubMed Central full text biomedical articles.

PubMed

Kafkas, Şenay; Kim, Jee-Hyub; Pi, Xingjun; McEntyre, Johanna R

2015-01-01

In this study, we present an analysis of data citation practices in full text research articles and their corresponding supplementary data files, made available in the Open Access set of articles from Europe PubMed Central. Our aim is to investigate whether supplementary data files should be considered as a source of information for integrating the literature with biomolecular databases. Using text-mining methods to identify and extract a variety of core biological database accession numbers, we found that the supplemental data files contain many more database citations than the body of the article, and that those citations often take the form of a relatively small number of articles citing large collections of accession numbers in text-based files. Moreover, citation of value-added databases derived from submission databases (such as Pfam, UniProt or Ensembl) is common, demonstrating the reuse of these resources as datasets in themselves. All the database accession numbers extracted from the supplementary data are publicly accessible from http://dx.doi.org/10.5281/zenodo.11771. Our study suggests that supplementary data should be considered when linking articles with data, in curation pipelines, and in information retrieval tasks in order to make full use of the entire research article. These observations highlight the need to improve the management of supplemental data in general, in order to make this information more discoverable and useful.

Evaluation of SEVIRI-Derived Rain Rates and Accumulated Rainfall with TRMM-TMI and Rain Gauge Data over West-Africa

NASA Astrophysics Data System (ADS)

Wolters, E. L. A.; Roebeling, R. A.; Stammes, P.; Wang, P.; Ali, A.; Brissebrat, G.

2009-11-01

Clouds are of paramount importance to the hydrological cycle, as they influence the surface energy balance, thereby constraining the amount of energy available for evaporation, and their contribution through precipitation. Especially in regions where water availability is critical, such as in West-Africa, accurate determination of various terms of the hydrological cycle is warranted. At the Royal Netherlands Meteorological Institute (KNMI), an algorithm to retrieve Cloud Physical Properties (CPP) from mainly visible and near-infrared spectral channel radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat-8 and -9 has been developed. Recently, this algorithm as been extended with a rain rate retrieval method. Evaluation of this geophysical quantity has been done with rain radar data over the Netherlands. This paper presents the first results of this rain rate retrieval over West-Africa for June 2006. In addition, the added value of the high temporal and spatial resolution of the SEVIRI instrument is shown. Over land, retrievals are compared with rain gauge observations performed during the African Monsoon Multidisciplinary Analyses (AMMA) project and with a kriged dataset of the Comite Inter-Estate pour la Lutte contre la Secheresse au Sahel (CILSS) rain gauge network, whereas rain rate retrievals over ocean are evaluated using Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) data.

Physical condition for elimination of ambiguity in conditionally convergent lattice sums

NASA Astrophysics Data System (ADS)

Young, K.

1987-02-01

The conditional convergence of the lattice sum defining the Madelung constant gives rise to an ambiguity in its value. It is shown that this ambiguity is related, through a simple and universal integral, to the average charge density on the crystal surface. The physically correct value is obtained by setting the charge density to zero. A simple and universally applicable formula for the Madelung constant is derived as a consequence. It consists of adding up dipole-dipole energies together with a nontrivial correction term.

Mass-independent area (or entropy) and thermodynamic volume products in conformal gravity

NASA Astrophysics Data System (ADS)

Pradhan, Parthapratim

2017-06-01

In this work, we investigate the thermodynamic properties of conformal gravity in four dimensions. We compute the area (or entropy) functional relation for this black hole (BH). We consider both de Sitter (dS) and anti-de Sitter (AdS) cases. We derive the Cosmic-Censorship-Inequality which is an important relation in general relativity that relates the total mass of a spacetime to the area of all the BH horizons. Local thermodynamic stability is studied by computing the specific heat. The second-order phase transition occurs at a certain condition. Various types of second-order phase structure have been given for various values of a and the cosmological constant Λ in the Appendix. When a = 0, one obtains the result of Schwarzschild-dS and Schwarzschild-AdS cases. In the limit aM ≪ 1, one obtains the result of Grumiller spacetime, where a is nontrivial Rindler parameter or Rindler acceleration and M is the mass parameter. The thermodynamic volume functional relation is derived in the extended phase space, where the cosmological constant is treated as a thermodynamic pressure and its conjugate variable as a thermodynamic volume. The mass-independent area (or entropy) functional relation and thermodynamic volume functional relation that we have derived could turn out to be a universal quantity.

Search for β-Secretase Inhibitors from Natural Spices.

PubMed

Matsumura, Shinichi; Murata, Kazuya; Yoshioka, Yuri; Matsuda, Hideaki

2016-04-01

The growing number of Alzheimer's disease (AD) patients prompted us to seek effective natural resources for the prevention of AD. We focused on the inhibition of β-secretase, which is known to catalyze the production of senile plaque. Sixteen spices used in Asian countries were selected for the screening. Among the extracts tested, hexane extracts obtained from turmeric, cardamom, long pepper, cinnamon, Sichuan pepper, betel, white turmeric and aromatic ginger showed potent inhibitory activities. Their active principles were identified as sesquiterpenoids, monoterpenoids, fatty acid derivatives and phenylpropanoids using GC-MS analyses. The chemical structures and IC50 values of the compounds are disclosed. The results suggest that long-term consumption'of aromatic compounds from spices could be effective in the prevention of AD.

Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork.

PubMed

Druka, Arnis; Druka, Ilze; Centeno, Arthur G; Li, Hongqiang; Sun, Zhaohui; Thomas, William T B; Bonar, Nicola; Steffenson, Brian J; Ullrich, Steven E; Kleinhofs, Andris; Wise, Roger P; Close, Timothy J; Potokina, Elena; Luo, Zewei; Wagner, Carola; Schweizer, Günther F; Marshall, David F; Kearsey, Michael J; Williams, Robert W; Waugh, Robbie

2008-11-18

A typical genetical genomics experiment results in four separate data sets; genotype, gene expression, higher-order phenotypic data and metadata that describe the protocols, processing and the array platform. Used in concert, these data sets provide the opportunity to perform genetic analysis at a systems level. Their predictive power is largely determined by the gene expression dataset where tens of millions of data points can be generated using currently available mRNA profiling technologies. Such large, multidimensional data sets often have value beyond that extracted during their initial analysis and interpretation, particularly if conducted on widely distributed reference genetic materials. Besides quality and scale, access to the data is of primary importance as accessibility potentially allows the extraction of considerable added value from the same primary dataset by the wider research community. Although the number of genetical genomics experiments in different plant species is rapidly increasing, none to date has been presented in a form that allows quick and efficient on-line testing for possible associations between genes, loci and traits of interest by an entire research community. Using a reference population of 150 recombinant doubled haploid barley lines we generated novel phenotypic, mRNA abundance and SNP-based genotyping data sets, added them to a considerable volume of legacy trait data and entered them into the GeneNetwork http://www.genenetwork.org. GeneNetwork is a unified on-line analytical environment that enables the user to test genetic hypotheses about how component traits, such as mRNA abundance, may interact to condition more complex biological phenotypes (higher-order traits). Here we describe these barley data sets and demonstrate some of the functionalities GeneNetwork provides as an easily accessible and integrated analytical environment for exploring them. By integrating barley genotypic, phenotypic and mRNA abundance data sets directly within GeneNetwork's analytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (barley) in a database that has been designed for an animal model species (mouse) with a well established genome sequence, we prove the importance of the concept and practice of modular development and interoperability of software engineering for biological data sets.

Value added data archiving

NASA Technical Reports Server (NTRS)

Berard, Peter R.

1993-01-01

Researchers in the Molecular Sciences Research Center (MSRC) of Pacific Northwest Laboratory (PNL) currently generate massive amounts of scientific data. The amount of data that will need to be managed by the turn of the century is expected to increase significantly. Automated tools that support the management, maintenance, and sharing of this data are minimal. Researchers typically manage their own data by physically moving datasets to and from long term storage devices and recording a dataset's historical information in a laboratory notebook. Even though it is not the most efficient use of resources, researchers have tolerated the process. The solution to this problem will evolve over the next three years in three phases. PNL plans to add sophistication to existing multilevel file system (MLFS) software by integrating it with an object database management system (ODBMS). The first phase in the evolution is currently underway. A prototype system of limited scale is being used to gather information that will feed into the next two phases. This paper describes the prototype system, identifies the successes and problems/complications experienced to date, and outlines PNL's long term goals and objectives in providing a permanent solution.

Kernel-PCA data integration with enhanced interpretability

PubMed Central

2014-01-01

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge. PMID:25032747

FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies.

PubMed

Kim, Jiwoong; Kim, Min Soo; Koh, Andrew Y; Xie, Yang; Zhan, Xiaowei

2016-10-10

Given the lack of a complete and comprehensive library of microbial reference genomes, determining the functional profile of diverse microbial communities is challenging. The available functional analysis pipelines lack several key features: (i) an integrated alignment tool, (ii) operon-level analysis, and (iii) the ability to process large datasets. Here we introduce our open-sourced, stand-alone functional analysis pipeline for analyzing whole metagenomic and metatranscriptomic sequencing data, FMAP (Functional Mapping and Analysis Pipeline). FMAP performs alignment, gene family abundance calculations, and statistical analysis (three levels of analyses are provided: differentially-abundant genes, operons and pathways). The resulting output can be easily visualized with heatmaps and functional pathway diagrams. FMAP functional predictions are consistent with currently available functional analysis pipelines. FMAP is a comprehensive tool for providing functional analysis of metagenomic/metatranscriptomic sequencing data. With the added features of integrated alignment, operon-level analysis, and the ability to process large datasets, FMAP will be a valuable addition to the currently available functional analysis toolbox. We believe that this software will be of great value to the wider biology and bioinformatics communities.

Analysis of ground-measured and passive-microwave-derived snow depth variations in midwinter across the Northern Great Plains

USGS Publications Warehouse

Chang, A.T.C.; Kelly, R.E.J.; Josberger, E.G.; Armstrong, R.L.; Foster, J.L.; Mognard, N.M.

2005-01-01

Accurate estimation of snow mass is important for the characterization of the hydrological cycle at different space and time scales. For effective water resources management, accurate estimation of snow storage is needed. Conventionally, snow depth is measured at a point, and in order to monitor snow depth in a temporally and spatially comprehensive manner, optimum interpolation of the points is undertaken. Yet the spatial representation of point measurements at a basin or on a larger distance scale is uncertain. Spaceborne scanning sensors, which cover a wide swath and can provide rapid repeat global coverage, are ideally suited to augment the global snow information. Satellite-borne passive microwave sensors have been used to derive snow depth (SD) with some success. The uncertainties in point SD and areal SD of natural snowpacks need to be understood if comparisons are to be made between a point SD measurement and satellite SD. In this paper three issues are addressed relating satellite derivation of SD and ground measurements of SD in the northern Great Plains of the United States from 1988 to 1997. First, it is shown that in comparing samples of ground-measured point SD data with satellite-derived 25 ?? 25 km2 pixels of SD from the Defense Meteorological Satellite Program Special Sensor Microwave Imager, there are significant differences in yearly SD values even though the accumulated datasets showed similarities. Second, from variogram analysis, the spatial variability of SD from each dataset was comparable. Third, for a sampling grid cell domain of 1?? ?? 1?? in the study terrain, 10 distributed snow depth measurements per cell are required to produce a sampling error of 5 cm or better. This study has important implications for validating SD derivations from satellite microwave observations. ?? 2005 American Meteorological Society.

Regression Equations for Monthly and Annual Mean and Selected Percentile Streamflows for Ungaged Rivers in Maine

USGS Publications Warehouse

Dudley, Robert W.

2015-12-03

The largest average errors of prediction are associated with regression equations for the lowest streamflows derived for months during which the lowest streamflows of the year occur (such as the 5 and 1 monthly percentiles for August and September). The regression equations have been derived on the basis of streamflow and basin characteristics data for unregulated, rural drainage basins without substantial streamflow or drainage modifications (for example, diversions and (or) regulation by dams or reservoirs, tile drainage, irrigation, channelization, and impervious paved surfaces), therefore using the equations for regulated or urbanized basins with substantial streamflow or drainage modifications will yield results of unknown error. Input basin characteristics derived using techniques or datasets other than those documented in this report or using values outside the ranges used to develop these regression equations also will yield results of unknown error.

Modeling habitat distribution from organism occurrences and environmental data: Case study using anemonefishes and their sea anemone hosts

USGS Publications Warehouse

Guinotte, J.M.; Bartley, J.D.; Iqbal, A.; Fautin, D.G.; Buddemeier, R.W.

2006-01-01

We demonstrate the KGSMapper (Kansas Geological Survey Mapper), a straightforward, web-based biogeographic tool that uses environmental conditions of places where members of a taxon are known to occur to find other places containing suitable habitat for them. Using occurrence data for anemonefishes or their host sea anemones, and data for environmental parameters, we generated maps of suitable habitat for the organisms. The fact that the fishes are obligate symbionts of the anemones allowed us to validate the KGSMapper output: we were able to compare the inferred occurrence of the organism to that of the actual occurrence of its symbiont. Characterizing suitable habitat for these organisms in the Indo-West Pacific, the region where they naturally occur, can be used to guide conservation efforts, field work, etc.; defining suitable habitat for them in the Atlantic and eastern Pacific is relevant to identifying areas vulnerable to biological invasions. We advocate distinguishing between these 2 sorts of model output, terming the former maps of realized habitat and the latter maps of potential habitat. Creation of a niche model requires adding biotic data to the environmental data used for habitat maps: we included data on fish occurrences to infer anemone distribution and vice versa. Altering the selection of environmental variables allowed us to investigate which variables may exert the most influence on organism distribution. Adding variables does not necessarily improve precision of the model output. KGSMapper output distinguishes areas that fall within 1 standard deviation (SD) of the mean environmental variable values for places where members of the taxon occur, within 2 SD, and within the entire range of values; eliminating outliers or data known to be imprecise or inaccurate improved output precision mainly in the 2 SD range and beyond. Thus, KGSMapper is robust in the face of questionable data, offering the user a way to recognize and clean such data. It also functions well with sparse datasets. These features make it useful for biogeographic meta-analyses with the diverse, distributed datasets that are typical for marine organisms lacking direct commercial value. ?? Inter-Research 2006.

Benchmarking homogenization algorithms for monthly data

NASA Astrophysics Data System (ADS)

Venema, V. K. C.; Mestre, O.; Aguilar, E.; Auer, I.; Guijarro, J. A.; Domonkos, P.; Vertacnik, G.; Szentimrey, T.; Stepanek, P.; Zahradnicek, P.; Viarre, J.; Müller-Westermeier, G.; Lakatos, M.; Williams, C. N.; Menne, M. J.; Lindau, R.; Rasol, D.; Rustemeier, E.; Kolokythas, K.; Marinova, T.; Andresen, L.; Acquaotta, F.; Fratianni, S.; Cheval, S.; Klancar, M.; Brunetti, M.; Gruber, C.; Prohom Duran, M.; Likso, T.; Esteban, P.; Brandsma, T.

2012-01-01

The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

Benchmarking monthly homogenization algorithms

NASA Astrophysics Data System (ADS)

Venema, V. K. C.; Mestre, O.; Aguilar, E.; Auer, I.; Guijarro, J. A.; Domonkos, P.; Vertacnik, G.; Szentimrey, T.; Stepanek, P.; Zahradnicek, P.; Viarre, J.; Müller-Westermeier, G.; Lakatos, M.; Williams, C. N.; Menne, M.; Lindau, R.; Rasol, D.; Rustemeier, E.; Kolokythas, K.; Marinova, T.; Andresen, L.; Acquaotta, F.; Fratianni, S.; Cheval, S.; Klancar, M.; Brunetti, M.; Gruber, C.; Prohom Duran, M.; Likso, T.; Esteban, P.; Brandsma, T.

2011-08-01

The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random break-type inhomogeneities were added to the simulated datasets modeled as a Poisson process with normally distributed breakpoint sizes. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones.

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform

PubMed Central

Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li

2015-01-01

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert–Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction. PMID:26540059

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform.

PubMed

Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li

2015-11-03

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert-Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500-800 and a m range of 50-300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction.

The HIAPER Pole-to-Pole Observations (HIPPO) Public Data Archive at CDIAC: Carbon Cycle and Greenhouse Gas Data

NASA Astrophysics Data System (ADS)

Christensen, S. W.; Hook, L. A.

2011-12-01

The HIAPER Pole-to-Pole Observations (HIPPO) project is investigating the carbon cycle and greenhouse gases throughout various altitudes in the atmosphere over the Pacific Basin through the annual cycle (Wofsy and the HIPPO Science Team 2011, this session). Aircraft-based data collection occurred during 2009-2011. Data analyses, comparisons, and integration are ongoing. A permanent public archive of HIPPO data has been established at the U. S. DOE Carbon Dioxide Information Analysis Center (CDIAC). Datasets are provided primarily by the Lead Principal Investigator (PI), who draws on a comprehensive set of aircraft navigation information, meteorological measurements, and research instrument and sampling system results from multiple co-investigators to compile integrated and generate value-added products. A website/ftp site has been developed for HIPPO data and metadata (http://hippo.ornl.gov), in coordination with the UCAR website that presents field catalogs and other detailed information about HIPPO missions (http://www.eol.ucar.edu/projects/hippo/dm/). A data policy was adopted that balances the needs of the project investigators with the interests of the scientific user community. A data dictionary was developed to capture the basic characteristics of the hundreds of measurements. Instrument descriptions were compiled. A user's guide is presented for each dataset that also contains data file information enabling users to know when data have been updated. Data are received and provided as space-delimited ASCII files. Metadata records are compiled into a searchable CDIAC index and will be submitted to climate change research data clearinghouses. Each dataset is given a persistent identifier (DOI) to facilitate attribution. We expect that data will continue to be added to the archive for the next year or more. In the future we anticipate creating a database for HIPPO data, with a web interface to facilitate searching and customized data extraction.

Assessment of the chestnut production weather dependence

NASA Astrophysics Data System (ADS)

Pereira, Mário; Caramelo, Liliana; Gouveia, Célia; Gomes-Laranjo, José

2010-05-01

The vegetative cycle of chestnut trees is highly dependent on weather. Photosynthesis and pollen germination are mainly conditioned by the air temperature while heavy precipitation and strong wind have significant impacts during the flushing phase period (Gomes-Laranjo et al., 2005, 2006). In Portugal, chestnut tree orchads are located in mountainous areas of the Northeast region of Trás-os-Montes, between 600 and 1000 m of altitude. Topography controls the atmospheric environment and assures adequate conditions for the chestnut production. In the above mentioned context, remote sensing plays an important role because of its ability to monitor and characterise vegetation dynamics. A number of studies, based on remote sensing, have been conducted in Europe to analyse the year-to-year variations in European vegetation greenness as a function of precipitation and temperature (Gouveia et al., 2008). A previous study focusing on the relationship between meteorological variables and chestnut productivity provides indication that simulation models may benefit from the incorporation of such kind of relationships. The aim of the present work is to provide a detailed description of recent developments, in particular of the added value that may be brought by using satellite data. We have relied on regional fields of the Normalized Difference Vegetation Index (NDVI) dataset, at 8-km resolution, provided by the Global Inventory Monitoring and Modelling System (GIMMS) group. The data are derived from the Advanced Very High Resolution Radiometers (AVHRR), and cover the period from 1982 to 2006. Additionally we have used the chestnut productivity dataset, which includes the annual values of chestnut production and area of production provided by INE, the National Institute of Statistics of Portugal and the meteorological dataset which includes values of several variables from different providers (Meteorod, NCEP/NCAR, ECA&D and national Meteorological Institute). Results show that satellite and meteorological data are complementary in what respects to the evaluation of the spatial and temporal evolution of the chestnut production. The satellite data proves to be very useful to monitor the spatial and temporal evolution of the vegetation state in the locations of the chestnut orchads and when tested as potential predictors by means of correlation and regression analysis. Gomes-Laranjo, J., Coutinho, J.P., Ferreira-Cardoso, J., Pimentel-Pereira, M., Ramos, C., Torres-Pereira, J.(2005) "Assessment to a new concept of chestnut orchard management in vegetative wall.". Acta Hort. 693: 707-712. Gomes-Laranjo, J.C.E., Peixoto, F., Wong Fong Sang, H.W., Torres-Pereira, J.M.G.(2006) "Study of the temperature effect in three chestnut (Castanea sativa Mill.) cultivars' behavior". J. Plant Physiol. 163: 945-955. Gouveia C., Trigo R.M., DaCamara C.C., Libonati R., Pereira J.M.C., 2008b. The North Atlantic Oscillation and European vegetation dynamics. International Journal of Climatology, vol. 28, issue 14, pp. 1835-1847, DOI: 10.1002/joc.1682.

Evaluation of antituberculosis activity and DFT study on dipyrromethane-derived hydrazone derivatives

NASA Astrophysics Data System (ADS)

Rawat, Poonam; Singh, R. N.; Niranjan, Priydarshni; Ranjan, Alok; Holguín, Norma Rosario Flores

2017-12-01

This paper evaluates the anti-tubercular activity of dipyrromethane-derived hydrazones derivatives (3a-d) against strain of Mycobacterium tuberculosis H37Rv. The newly synthesized compounds have been obtained in good yield based on the condensation of aromatic aldehyde derivatives with pyrrole hydrazone in presence of catalyst and well characterized with spectroscopic methods (1H, 13C NMR, Mass spectrometry) and elemental analysis. The singlet observed in the experimental 1H and 13C NMR spectra in the range of 5.3-5.7 ppm and 30-33.86 ppm, respectively, indicating that two pyrrole units are joined at meso position. The electronic transitions observed in the experimental spectra are n→π* and π →π* in nature. Experimental and theoretical findings corroborate well with each other. The substitution of acceptor group (-NO2) at ortho- and meta-positions of benzene ring, present at meso-position of dipyrromethane is responsible for variation in β0 values. The calculated NLO of (3a-d) are much greater than those of p-nitroaniline (PNA). The solvent induced effects on the non-linear optical properties were studied and found to enhance NLO properties of the molecules as dielectric constants of the solvents increases. On the basis of results it is anticipated that these dipyrromethanes will be useful for both antimicrobial and non-linear optical (NLO) applications. With the help of Microplate Alamar Blue assay (MABA) method all (3a-d) compounds were screened for their anti-tubercular activity and found that 3b and 3d have higher inhibitory activity against strain of M. tuberculosis H37Rv.

A case study using kinematic quantities derived from a triangle of VHF Doppler wind profilers

NASA Technical Reports Server (NTRS)

Carlson, Catherine A.; Forbes, Gregory S.

1989-01-01

Horizontal divergence, relative vorticity, kinematic vertical velocity, and geostrophic and ageostrophic winds are computed from Colorado profiler network data to investigate an upslope snowstorm in northeastern Colorado. Horizontal divergence and relative vorticity are computed using the Gauss and Stokes theorems, respectively. Kinematic vertical velocities are obtained from the surface to 9 km by vertically integrating the continuity equation. The geostrophic and ageostrophic winds are computed by applying a finite differencing technique to evaluate the derivatives in the horizontal equations of motion. Comparison of the synoptic-scale data with the profiler network data reveals that the two datasets are generally consistent. Also, the profiler-derived quantities exhibit coherent vertical and temporal patterns consistent with conceptual and theoretical flow fields of various meteorological phenomena. It is suggested that the profiler-derived quantities are of potential use to weather forecasters in that they enable the dynamic and kinematic interpretation of weather system structure to be made and thus have nowcasting and short-term forecasting value.

Analysis of plant-derived miRNAs in animal small RNA datasets

PubMed Central

2012-01-01

Background Plants contain significant quantities of small RNAs (sRNAs) derived from various sRNA biogenesis pathways. Many of these sRNAs play regulatory roles in plants. Previous analysis revealed that numerous sRNAs in corn, rice and soybean seeds have high sequence similarity to animal genes. However, exogenous RNA is considered to be unstable within the gastrointestinal tract of many animals, thus limiting potential for any adverse effects from consumption of dietary RNA. A recent paper reported that putative plant miRNAs were detected in animal plasma and serum, presumably acquired through ingestion, and may have a functional impact in the consuming organisms. Results To address the question of how common this phenomenon could be, we searched for plant miRNAs sequences in public sRNA datasets from various tissues of mammals, chicken and insects. Our analyses revealed that plant miRNAs were present in the animal sRNA datasets, and significantly miR168 was extremely over-represented. Furthermore, all or nearly all (>96%) miR168 sequences were monocot derived for most datasets, including datasets for two insects reared on dicot plants in their respective experiments. To investigate if plant-derived miRNAs, including miR168, could accumulate and move systemically in insects, we conducted insect feeding studies for three insects including corn rootworm, which has been shown to be responsive to plant-produced long double-stranded RNAs. Conclusions Our analyses suggest that the observed plant miRNAs in animal sRNA datasets can originate in the process of sequencing, and that accumulation of plant miRNAs via dietary exposure is not universal in animals. PMID:22873950

Novel bis-(−)-nor-meptazinol derivatives act as dual binding site AChE inhibitors with metal-complexing property

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

Zheng, Wei; NPFPC Key Laboratory of Contraceptives and Devices, Shanghai Institute of Planned Parenthood Research, 2140 Xietu Road, Shanghai 200032; Li, Juan

The strategy of dual binding site acetylcholinesterase (AChE) inhibition along with metal chelation may represent a promising direction for multi-targeted interventions in the pathophysiological processes of Alzheimer's disease (AD). In the present study, two derivatives (ZLA and ZLB) of a potent dual binding site AChE inhibitor bis-(−)-nor-meptazinol (bis-MEP) were designed and synthesized by introducing metal chelating pharmacophores into the middle chain of bis-MEP. They could inhibit human AChE activity with IC{sub 50} values of 9.63 μM (for ZLA) and 8.64 μM (for ZLB), and prevent AChE-induced amyloid-β (Aβ) aggregation with IC{sub 50} values of 49.1 μM (for ZLA) and 55.3more » μM (for ZLB). In parallel, molecular docking analysis showed that they are capable of interacting with both the catalytic and peripheral anionic sites of AChE. Furthermore, they exhibited abilities to complex metal ions such as Cu(II) and Zn(II), and inhibit Aβ aggregation triggered by these metals. Collectively, these results suggest that ZLA and ZLB may act as dual binding site AChEIs with metal-chelating potency, and may be potential leads of value for further study on disease-modifying treatment of AD. -- Highlights: ► Two novel bis-(−)-nor-meptazinol derivatives are designed and synthesized. ► ZLA and ZLB may act as dual binding site AChEIs with metal-chelating potency. ► They are potential leads for disease-modifying treatment of Alzheimer's disease.« less

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Influence of outliers on accuracy estimation in genomic prediction in plant breeding.

PubMed

Estaghvirou, Sidi Boubacar Ould; Ogutu, Joseph O; Piepho, Hans-Peter

2014-10-01

Outliers often pose problems in analyses of data in plant breeding, but their influence on the performance of methods for estimating predictive accuracy in genomic prediction studies has not yet been evaluated. Here, we evaluate the influence of outliers on the performance of methods for accuracy estimation in genomic prediction studies using simulation. We simulated 1000 datasets for each of 10 scenarios to evaluate the influence of outliers on the performance of seven methods for estimating accuracy. These scenarios are defined by the number of genotypes, marker effect variance, and magnitude of outliers. To mimic outliers, we added to one observation in each simulated dataset, in turn, 5-, 8-, and 10-times the error SD used to simulate small and large phenotypic datasets. The effect of outliers on accuracy estimation was evaluated by comparing deviations in the estimated and true accuracies for datasets with and without outliers. Outliers adversely influenced accuracy estimation, more so at small values of genetic variance or number of genotypes. A method for estimating heritability and predictive accuracy in plant breeding and another used to estimate accuracy in animal breeding were the most accurate and resistant to outliers across all scenarios and are therefore preferable for accuracy estimation in genomic prediction studies. The performances of the other five methods that use cross-validation were less consistent and varied widely across scenarios. The computing time for the methods increased as the size of outliers and sample size increased and the genetic variance decreased. Copyright © 2014 Ould Estaghvirou et al.

Precipitation intercomparison of a set of satellite- and raingauge-derived datasets, ERA Interim reanalysis, and a single WRF regional climate simulation over Europe and the North Atlantic

NASA Astrophysics Data System (ADS)

Skok, Gregor; Žagar, Nedjeljka; Honzak, Luka; Žabkar, Rahela; Rakovec, Jože; Ceglar, Andrej

2016-01-01

The study presents a precipitation intercomparison based on two satellite-derived datasets (TRMM 3B42, CMORPH), four raingauge-based datasets (GPCC, E-OBS, Willmott & Matsuura, CRU), ERA Interim reanalysis (ERAInt), and a single climate simulation using the WRF model. The comparison was performed for a domain encompassing parts of Europe and the North Atlantic over the 11-year period of 2000-2010. The four raingauge-based datasets are similar to the TRMM dataset with biases over Europe ranging from -7 % to +4 %. The spread among the raingauge-based datasets is relatively small over most of Europe, although areas with greater uncertainty (more than 30 %) exist, especially near the Alps and other mountainous regions. There are distinct differences between the datasets over the European land area and the Atlantic Ocean in comparison to the TRMM dataset. ERAInt has a small dry bias over the land; the WRF simulation has a large wet bias (+30 %), whereas CMORPH is characterized by a large and spatially consistent dry bias (-21 %). Over the ocean, both ERAInt and CMORPH have a small wet bias (+8 %) while the wet bias in WRF is significantly larger (+47 %). ERAInt has the highest frequency of low-intensity precipitation while the frequency of high-intensity precipitation is the lowest due to its lower native resolution. Both satellite-derived datasets have more low-intensity precipitation over the ocean than over the land, while the frequency of higher-intensity precipitation is similar or larger over the land. This result is likely related to orography, which triggers more intense convective precipitation, while the Atlantic Ocean is characterized by more homogenous large-scale precipitation systems which are associated with larger areas of lower intensity precipitation. However, this is not observed in ERAInt and WRF, indicating the insufficient representation of convective processes in the models. Finally, the Fraction Skill Score confirmed that both models perform better over the Atlantic Ocean with ERAInt outperforming the WRF at low thresholds and WRF outperforming ERAInt at higher thresholds. The diurnal cycle is simulated better in the WRF simulation than in ERAInt, although WRF could not reproduce well the amplitude of the diurnal cycle. While the evaluation of the WRF model confirms earlier findings related to the model's wet bias over European land, the applied satellite-derived precipitation datasets revealed differences between the land and ocean areas along with uncertainties in the observation datasets.

A novel derivative of doxorubicin, AD198, inhibits canine transitional cell carcinoma and osteosarcoma cells in vitro

PubMed Central

Rathore, Kusum; Cekanova, Maria

2015-01-01

Doxorubicin (DOX) is one of the most commonly used chemotherapeutic treatments for a wide range of cancers. N-benzyladriamycin-14-valerate (AD198) is a lipophilic anthracycline that has been shown to target conventional and novel isoforms of protein kinase C (PKC) in cytoplasm of cells. Because of the adverse effects of DOX, including hair loss, nausea, vomiting, liver dysfunction, and cardiotoxicity, novel derivatives of DOX have been synthesized and validated. In this study, we evaluated the effects of DOX and its derivative, AD198, on cell viability of three canine transitional cell carcinoma (K9TCC) (K9TCC#1-Lillie, K9TCC#2-Dakota, K9TCC#4-Molly) and three canine osteosarcoma (K9OSA) (K9OSA#1-Zoe, K9OSA#2-Nashville, K9OSA#3-JJ) primary cancer cell lines. DOX and AD198 significantly inhibited cell proliferation in all tested K9TCC and K9OSA cell lines in a dose-dependent manner. AD198 inhibited cell viability of tested K9TCC and K9OSA cell lines more efficiently as compared to DOX at the same concentration using MTS (3-(4,5-dimethyl-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2h-tetrazolium) assay. AD198 had lower IC50 values as compared to DOX for all tested K9TCC and K9OSA cell lines. In addition, AD198 increased apoptosis in all tested K9TCC and K9OSA cell lines. AD198 increased the caspase activity in tested K9TCC and K9OSA cell lines, which was confirmed by caspase-3/7 assay, and cleavage of poly (ADP-ribose) polymerase (PARP) was confirmed by Western blotting analysis. In addition, AD198 cleaved PKC-δ, which subsequently activated the p38 signaling pathway, resulting in the apoptosis of tested K9TCC and K9OSA cell lines. Inhibition of the p38 signaling pathway by SB203580 rescued DOX- and AD198-induced apoptosis in tested K9TCC and K9OSA cell lines. Our in vitro results suggest that AD198 might be considered as a new treatment option for K9TCC and K9OSA cell lines cancers in vivo. PMID:26451087

A novel derivative of doxorubicin, AD198, inhibits canine transitional cell carcinoma and osteosarcoma cells in vitro.

PubMed

Rathore, Kusum; Cekanova, Maria

2015-01-01

Doxorubicin (DOX) is one of the most commonly used chemotherapeutic treatments for a wide range of cancers. N-benzyladriamycin-14-valerate (AD198) is a lipophilic anthracycline that has been shown to target conventional and novel isoforms of protein kinase C (PKC) in cytoplasm of cells. Because of the adverse effects of DOX, including hair loss, nausea, vomiting, liver dysfunction, and cardiotoxicity, novel derivatives of DOX have been synthesized and validated. In this study, we evaluated the effects of DOX and its derivative, AD198, on cell viability of three canine transitional cell carcinoma (K9TCC) (K9TCC#1-Lillie, K9TCC#2-Dakota, K9TCC#4-Molly) and three canine osteosarcoma (K9OSA) (K9OSA#1-Zoe, K9OSA#2-Nashville, K9OSA#3-JJ) primary cancer cell lines. DOX and AD198 significantly inhibited cell proliferation in all tested K9TCC and K9OSA cell lines in a dose-dependent manner. AD198 inhibited cell viability of tested K9TCC and K9OSA cell lines more efficiently as compared to DOX at the same concentration using MTS (3-(4,5-dimethyl-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2h-tetrazolium) assay. AD198 had lower IC50 values as compared to DOX for all tested K9TCC and K9OSA cell lines. In addition, AD198 increased apoptosis in all tested K9TCC and K9OSA cell lines. AD198 increased the caspase activity in tested K9TCC and K9OSA cell lines, which was confirmed by caspase-3/7 assay, and cleavage of poly (ADP-ribose) polymerase (PARP) was confirmed by Western blotting analysis. In addition, AD198 cleaved PKC-δ, which subsequently activated the p38 signaling pathway, resulting in the apoptosis of tested K9TCC and K9OSA cell lines. Inhibition of the p38 signaling pathway by SB203580 rescued DOX- and AD198-induced apoptosis in tested K9TCC and K9OSA cell lines. Our in vitro results suggest that AD198 might be considered as a new treatment option for K9TCC and K9OSA cell lines cancers in vivo.

A method to derive vegetation distribution maps for pollen dispersion models using birch as an example

NASA Astrophysics Data System (ADS)

Pauling, A.; Rotach, M. W.; Gehrig, R.; Clot, B.

2012-09-01

Detailed knowledge of the spatial distribution of sources is a crucial prerequisite for the application of pollen dispersion models such as, for example, COSMO-ART (COnsortium for Small-scale MOdeling - Aerosols and Reactive Trace gases). However, this input is not available for the allergy-relevant species such as hazel, alder, birch, grass or ragweed. Hence, plant distribution datasets need to be derived from suitable sources. We present an approach to produce such a dataset from existing sources using birch as an example. The basic idea is to construct a birch dataset using a region with good data coverage for calibration and then to extrapolate this relationship to a larger area by using land use classes. We use the Swiss forest inventory (1 km resolution) in combination with a 74-category land use dataset that covers the non-forested areas of Switzerland as well (resolution 100 m). Then we assign birch density categories of 0%, 0.1%, 0.5% and 2.5% to each of the 74 land use categories. The combination of this derived dataset with the birch distribution from the forest inventory yields a fairly accurate birch distribution encompassing entire Switzerland. The land use categories of the Global Land Cover 2000 (GLC2000; Global Land Cover 2000 database, 2003, European Commission, Joint Research Centre; resolution 1 km) are then calibrated with the Swiss dataset in order to derive a Europe-wide birch distribution dataset and aggregated onto the 7 km COSMO-ART grid. This procedure thus assumes that a certain GLC2000 land use category has the same birch density wherever it may occur in Europe. In order to reduce the strict application of this crucial assumption, the birch density distribution as obtained from the previous steps is weighted using the mean Seasonal Pollen Index (SPI; yearly sums of daily pollen concentrations). For future improvement, region-specific birch densities for the GLC2000 categories could be integrated into the mapping procedure.

Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models.

PubMed

Cotten, Cameron; Reed, Jennifer L

2013-01-30

Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used. In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism. This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets.

Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models

PubMed Central

2013-01-01

Background Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used. Results In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism. Conclusions This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets. PMID:23360254

SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919-2014.

PubMed

Zeng, Lili; Wang, Dongxiao; Chen, Ju; Wang, Weiqiang; Chen, Rongyu

2016-04-26

In addition to the oceanographic data available for the South China Sea (SCS) from the World Ocean Database (WOD) and Array for Real-time Geostrophic Oceanography (Argo) floats, a suite of observations has been made by the South China Sea Institute of Oceanology (SCSIO) starting from the 1970s. Here, we assemble a SCS Physical Oceanographic Dataset (SCSPOD14) based on 51,392 validated temperature and salinity profiles collected from these three datasets for the period 1919-2014. A gridded dataset of climatological monthly mean temperature, salinity, and mixed and isothermal layer depth derived from an objective analysis of profiles is also presented. Comparisons with the World Ocean Atlas (WOA) and IFREMER/LOS Mixed Layer Depth Climatology confirm the reliability of the new dataset. This unique dataset offers an invaluable baseline perspective on the thermodynamic processes, spatial and temporal variability of water masses, and basin-scale and mesoscale oceanic structures in the SCS. We anticipate improvements and regular updates to this product as more observations become available from existing and future in situ networks.

SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919–2014

PubMed Central

Zeng, Lili; Wang, Dongxiao; Chen, Ju; Wang, Weiqiang; Chen, Rongyu

2016-01-01

In addition to the oceanographic data available for the South China Sea (SCS) from the World Ocean Database (WOD) and Array for Real-time Geostrophic Oceanography (Argo) floats, a suite of observations has been made by the South China Sea Institute of Oceanology (SCSIO) starting from the 1970s. Here, we assemble a SCS Physical Oceanographic Dataset (SCSPOD14) based on 51,392 validated temperature and salinity profiles collected from these three datasets for the period 1919–2014. A gridded dataset of climatological monthly mean temperature, salinity, and mixed and isothermal layer depth derived from an objective analysis of profiles is also presented. Comparisons with the World Ocean Atlas (WOA) and IFREMER/LOS Mixed Layer Depth Climatology confirm the reliability of the new dataset. This unique dataset offers an invaluable baseline perspective on the thermodynamic processes, spatial and temporal variability of water masses, and basin-scale and mesoscale oceanic structures in the SCS. We anticipate improvements and regular updates to this product as more observations become available from existing and future in situ networks. PMID:27116565

Indoor radon measurements in south west England explained by topsoil and stream sediment geochemistry, airborne gamma-ray spectroscopy and geology.

PubMed

Ferreira, Antonio; Daraktchieva, Zornitza; Beamish, David; Kirkwood, Charles; Lister, T Robert; Cave, Mark; Wragg, Joanna; Lee, Kathryn

2018-01-01

Predictive mapping of indoor radon potential often requires the use of additional datasets. A range of geological, geochemical and geophysical data may be considered, either individually or in combination. The present work is an evaluation of how much of the indoor radon variation in south west England can be explained by four different datasets: a) the geology (G), b) the airborne gamma-ray spectroscopy (AGR), c) the geochemistry of topsoil (TSG) and d) the geochemistry of stream sediments (SSG). The study area was chosen since it provides a large (197,464) indoor radon dataset in association with the above information. Geology provides information on the distribution of the materials that may contribute to radon release while the latter three items provide more direct observations on the distributions of the radionuclide elements uranium (U), thorium (Th) and potassium (K). In addition, (c) and (d) provide multi-element assessments of geochemistry which are also included in this study. The effectiveness of datasets for predicting the existing indoor radon data is assessed through the level (the higher the better) of explained variation (% of variance or ANOVA) obtained from the tested models. A multiple linear regression using a compositional data (CODA) approach is carried out to obtain the required measure of determination for each analysis. Results show that, amongst the four tested datasets, the soil geochemistry (TSG, i.e. including all the available 41 elements, 10 major - Al, Ca, Fe, K, Mg, Mn, Na, P, Si, Ti - plus 31 trace) provides the highest explained variation of indoor radon (about 40%); more than double the value provided by U alone (ca. 15%), or the sub composition U, Th, K (ca. 16%) from the same TSG data. The remaining three datasets provide values ranging from about 27% to 32.5%. The enhanced prediction of the AGR model relative to the U, Th, K in soils suggests that the AGR signal captures more than just the U, Th and K content in the soil. The best result is obtained by including the soil geochemistry with geology and AGR (TSG + G + AGR, ca. 47%). However, adding G and AGR to the TSG model only slightly improves the prediction (ca. +7%), suggesting that the geochemistry of soils already contain most of the information given by geology and airborne datasets together, at least with regard to the explanation of indoor radon. From the present analysis performed in the SW of England, it may be concluded that each one of the four datasets is likely to be useful for radon mapping purposes, whether alone or in combination with others. The present work also suggest that the complete soil geochemistry dataset (TSG) is more effective for indoor radon modelling than using just the U (+Th, K) concentration in soil. Copyright © 2016 Natural Environment Research Council. Published by Elsevier Ltd.. All rights reserved.

Disaster Debris Recovery Database - Recovery

EPA Pesticide Factsheets

The US EPA Region 5 Disaster Debris Recovery Database includes public datasets of over 6,000 composting facilities, demolition contractors, transfer stations, landfills and recycling facilities for construction and demolition materials, electronics, household hazardous waste, metals, tires, and vehicles in the states of Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, North Dakota, Ohio, Pennsylvania, South Dakota, West Virginia and Wisconsin.In this update, facilities in the 7 states that border the EPA Region 5 states were added to assist interstate disaster debris management. Also, the datasets for composters, construction and demolition recyclers, demolition contractors, and metals recyclers were verified and source information added for each record using these sources: AGC, Biocycle, BMRA, CDRA, ISRI, NDA, USCC, FEMA Debris Removal Contractor Registry, EPA Facility Registry System, and State and local listings.

Disaster Debris Recovery Database - Landfills

EPA Pesticide Factsheets

The US EPA Region 5 Disaster Debris Recovery Database includes public datasets of over 6,000 composting facilities, demolition contractors, transfer stations, landfills and recycling facilities for construction and demolition materials, electronics, household hazardous waste, metals, tires, and vehicles in the states of Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, North Dakota, Ohio, Pennsylvania, South Dakota, West Virginia and Wisconsin.In this update, facilities in the 7 states that border the EPA Region 5 states were added to assist interstate disaster debris management. Also, the datasets for composters, construction and demolition recyclers, demolition contractors, and metals recyclers were verified and source information added for each record using these sources: AGC, Biocycle, BMRA, CDRA, ISRI, NDA, USCC, FEMA Debris Removal Contractor Registry, EPA Facility Registry System, and State and local listings.

QSAR-Based Models for Designing Quinazoline/Imidazothiazoles/Pyrazolopyrimidines Based Inhibitors against Wild and Mutant EGFR

PubMed Central

Chauhan, Jagat Singh; Dhanda, Sandeep Kumar; Singla, Deepak; Agarwal, Subhash M.; Raghava, Gajendra P. S.

2014-01-01

Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on dataset containing 128 quinazoline based inhibitors. This dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train dataset while performance was evaluated on the wild_valid called validation dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC50) of 0.90 on validation dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid dataset and achieved a maximum correlation between 0.834 to 0.850 on these datasets. Finally, an integrated hybrid model has been developed on a dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalone (http://osddlinux.osdd.net/) and Galaxy (http://osddlinux.osdd.net:8001) version of software. We hope our webserver (http://crdd.osdd.net/oscadd/ntegfr/) will play a vital role in designing new anticancer drugs. PMID:24992720

Predicting mortality in sick African children: the FEAST Paediatric Emergency Triage (PET) Score.

PubMed

George, Elizabeth C; Walker, A Sarah; Kiguli, Sarah; Olupot-Olupot, Peter; Opoka, Robert O; Engoru, Charles; Akech, Samuel O; Nyeko, Richard; Mtove, George; Reyburn, Hugh; Berkley, James A; Mpoya, Ayub; Levin, Michael; Crawley, Jane; Gibb, Diana M; Maitland, Kathryn; Babiker, Abdel G

2015-07-31

Mortality in paediatric emergency care units in Africa often occurs within the first 24 h of admission and remains high. Alongside effective triage systems, a practical clinical bedside risk score to identify those at greatest risk could contribute to reducing mortality. Data collected during the Fluid As Expansive Supportive Therapy (FEAST) trial, a multi-centre trial involving 3,170 severely ill African children, were analysed to identify clinical and laboratory prognostic factors for mortality. Multivariable Cox regression was used to build a model in this derivation dataset based on clinical parameters that could be quickly and easily assessed at the bedside. A score developed from the model coefficients was externally validated in two admissions datasets from Kilifi District Hospital, Kenya, and compared to published risk scores using Area Under the Receiver Operating Curve (AUROC) and Hosmer-Lemeshow tests. The Net Reclassification Index (NRI) was used to identify additional laboratory prognostic factors. A risk score using 8 clinical variables (temperature, heart rate, capillary refill time, conscious level, severe pallor, respiratory distress, lung crepitations, and weak pulse volume) was developed. The score ranged from 0-10 and had an AUROC of 0.82 (95 % CI, 0.77-0.87) in the FEAST trial derivation set. In the independent validation datasets, the score had an AUROC of 0.77 (95 % CI, 0.72-0.82) amongst admissions to a paediatric high dependency ward and 0.86 (95 % CI, 0.82-0.89) amongst general paediatric admissions. This discriminative ability was similar to, or better than other risk scores in the validation datasets. NRI identified lactate, blood urea nitrogen, and pH to be important prognostic laboratory variables that could add information to the clinical score. Eight clinical prognostic factors that could be rapidly assessed by healthcare staff for triage were combined to create the FEAST Paediatric Emergency Triage (PET) score and externally validated. The score discriminated those at highest risk of fatal outcome at the point of hospital admission and compared well to other published risk scores. Further laboratory tests were also identified as prognostic factors which could be added if resources were available or as indices of severity for comparison between centres in future research studies.

Remote Measurement of Heat Flux from Power Plant Cooling Lakes

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

Garrett, Alfred J.; Kurzeja, Robert J.; Villa-Aleman, Eliel

2013-06-01

Laboratory experiments have demonstrated a correlation between the rate of heat loss q" from an experimental fluid to the air above and the standard deviation σ of the thermal variability in images of the fluid surface. These experimental results imply that q" can be derived directly from thermal imagery by computing σ. This paper analyses thermal imagery collected over two power plant cooling lakes to determine if the same relationship exists. Turbulent boundary layer theory predicts a linear relationship between q" and σ when both forced (wind driven) and free (buoyancy driven) convection are present. Datasets derived from ground- andmore » helicopter-based imagery collections had correlation coefficients between σ and q" of 0.45 and 0.76, respectively. Values of q" computed from a function of σ and friction velocity u* derived from turbulent boundary layer theory had higher correlations with measured values of q" (0.84 and 0.89). Finally, this research may be applicable to the problem of calculating losses of heat from the ocean to the atmosphere during high-latitude cold-air outbreaks because it does not require the information typically needed to compute sensible, evaporative, and thermal radiation energy losses to the atmosphere.« less

FY13 Summary Report on the Augmentation of the Spent Fuel Composition Dataset for Nuclear Forensics: SFCOMPO/NF

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

Brady Raap, Michaele C.; Lyons, Jennifer A.; Collins, Brian A.

This report documents the FY13 efforts to enhance a dataset of spent nuclear fuel isotopic composition data for use in developing intrinsic signatures for nuclear forensics. A review and collection of data from the open literature was performed in FY10. In FY11, the Spent Fuel COMPOsition (SFCOMPO) excel-based dataset for nuclear forensics (NF), SFCOMPO/NF was established and measured data for graphite production reactors, Boiling Water Reactors (BWRs) and Pressurized Water Reactors (PWRs) were added to the dataset and expanded to include a consistent set of data simulated by calculations. A test was performed to determine whether the SFCOMPO/NF dataset willmore » be useful for the analysis and identification of reactor types from isotopic ratios observed in interdicted samples.« less

Production of astaxanthin from corn fiber as a value-added co-product of fuel ethanol fermentation

USDA-ARS?s Scientific Manuscript database

Five strains of the yeast Phaffia rhodozyma, NRRL Y-17268, NRRL Y-17270, ATCC 96594 (CBS 6938), ATCC 24202 (UCD 67-210), and ATCC 74219 (UBV-AX2) were tested for astaxanthin production using the major sugars derived from corn fiber, a byproduct from the wet milling of corn kernels that contains prim...

Using Delphi Surveying Techniques to Gather Input from Non-Academics for Development of a Modern Dairy Manufacturing Curriculum

ERIC Educational Resources Information Center

Joyner, Helen S.; Smith, Denise

2015-01-01

The current face of the dairy manufacturing industry has changed from its traditional conception. Industry emphasis is moving away from traditional dairy products, such as fluid milk, ice cream, and butter, and moving toward yogurts, dairy beverages, and value-added products incorporating ingredients derived from milk and whey. However, many…

[Gaussian process regression and its application in near-infrared spectroscopy analysis].

PubMed

Feng, Ai-Ming; Fang, Li-Min; Lin, Min

2011-06-01

Gaussian process (GP) is applied in the present paper as a chemometric method to explore the complicated relationship between the near infrared (NIR) spectra and ingredients. After the outliers were detected by Monte Carlo cross validation (MCCV) method and removed from dataset, different preprocessing methods, such as multiplicative scatter correction (MSC), smoothing and derivate, were tried for the best performance of the models. Furthermore, uninformative variable elimination (UVE) was introduced as a variable selection technique and the characteristic wavelengths obtained were further employed as input for modeling. A public dataset with 80 NIR spectra of corn was introduced as an example for evaluating the new algorithm. The optimal models for oil, starch and protein were obtained by the GP regression method. The performance of the final models were evaluated according to the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (r). The models give good calibration ability with r values above 0.99 and the prediction ability is also satisfactory with r values higher than 0.96. The overall results demonstrate that GP algorithm is an effective chemometric method and is promising for the NIR analysis.

Modelling surface-water depression storage in a Prairie Pothole Region

USGS Publications Warehouse

Hay, Lauren E.; Norton, Parker A.; Viger, Roland; Markstrom, Steven; Regan, R. Steven; Vanderhoof, Melanie

2018-01-01

In this study, the Precipitation-Runoff Modelling System (PRMS) was used to simulate changes in surface-water depression storage in the 1,126-km2 Upper Pipestem Creek basin located within the Prairie Pothole Region of North Dakota, USA. The Prairie Pothole Region is characterized by millions of small water bodies (or surface-water depressions) that provide numerous ecosystem services and are considered an important contribution to the hydrologic cycle. The Upper Pipestem PRMS model was extracted from the U.S. Geological Survey's (USGS) National Hydrologic Model (NHM), developed to support consistent hydrologic modelling across the conterminous United States. The Geospatial Fabric database, created for the USGS NHM, contains hydrologic model parameter values derived from datasets that characterize the physical features of the entire conterminous United States for 109,951 hydrologic response units. Each hydrologic response unit in the Geospatial Fabric was parameterized using aggregated surface-water depression area derived from the National Hydrography Dataset Plus, an integrated suite of application-ready geospatial datasets. This paper presents a calibration strategy for the Upper Pipestem PRMS model that uses normalized lake elevation measurements to calibrate the parameters influencing simulated fractional surface-water depression storage. Results indicate that inclusion of measurements that give an indication of the change in surface-water depression storage in the calibration procedure resulted in accurate changes in surface-water depression storage in the water balance. Regionalized parameterization of the USGS NHM will require a proxy for change in surface-storage to accurately parameterize surface-water depression storage within the USGS NHM.

Prototype global burnt area algorithm using the AVHRR-LTDR time series

NASA Astrophysics Data System (ADS)

López-Saldaña, Gerardo; Pereira, José Miguel; Aires, Filipe

2013-04-01

One of the main limitations of products derived from remotely-sensed data is the length of the data records available for climate studies. The Advanced Very High Resolution Radiometer (AVHRR) long-term data record (LTDR) comprises a daily global atmospherically-corrected surface reflectance dataset at 0.05° spatial resolution and is available for the 1981-1999 time period. Fire is strong cause of land surface change and emissions of greenhouse gases around the globe. A global long-term identification of areas affected by fire is needed to analyze trends and fire-clime relationships. A burnt area algorithm can be seen as a change point detection problem where there is an abrupt change in the surface reflectance due to the biomass burning. Using the AVHRR-LTDR dataset, a time series of bidirectional reflectance distribution function (BRDF) corrected surface reflectance was generated using the daily observations and constraining the BRDF model inversion using a climatology of BRDF parameters derived from 12 years of MODIS data. The identification of the burnt area was performed using a t-test in the pre- and post-fire reflectance values and a change point detection algorithm, then spectral constraints were applied to flag changes caused by natural land processes like vegetation seasonality or flooding. Additional temporal constraints are applied focusing in the persistence of the affected areas. Initial results for year 1998, which was selected because of a positive fire anomaly, show spatio-temporal coherence but further analysis is required and a formal rigorous validation will be applied using burn scars identified from high-resolution datasets.

Shifts of Start and End of Season in Response to Air Temperature Variation Based on Gimms Dataset in Hyrcanian Forests

NASA Astrophysics Data System (ADS)

Kiapasha, K. H.; Darvishsefat, A. A.; Zargham, N.; Julien, Y.; Sobrino, J. A.; Nadi, M.

2017-09-01

Climate change is one of the most important environmental challenges in the world and forest as a dynamic phenomenon is influenced by environmental changes. The Hyrcanian forests is a unique natural heritage of global importance and we need monitoring this region. The objective of this study was to detect start and end of season trends in Hyrcanian forests of Iran based on biweekly GIMMS (Global Inventory Modeling and Mapping Studies) NDVI3g in the period 1981-2012. In order to find response of vegetation activity to local temperature variations, we used air temperature provided from I.R. Iran Meteorological Organization (IRIMO). At the first step in order to remove the existing gap from the original time series, the iterative Interpolation for Data Reconstruction (IDR) model was applied to GIMMS and temperature dataset. Then we applied significant Mann Kendall test to determine significant trend for each pixel of GIMMS and temperature datasets over the Hyrcanian forests. The results demonstrated that start and end of season (SOS & EOS respectively) derived from GIMMS3g NDVI time series increased by -0.16 and +0.41 days per year respectively. The trends derived from temperature time series indicated increasing trend in the whole of this region. Results of this study showed that global warming and its effect on growth and photosynthetic activity can increased the vegetation activity in our study area. Otherwise extension of the growing season, including an earlier start of the growing season, later autumn and higher rate of production increased NDVI value during the study period.

Synthesis of new 1,2,4-triazole compounds containing Schiff and Mannich bases (morpholine) with antioxidant and antimicrobial activities.

PubMed

Ünver, Yasemin; Deniz, Sadik; Çelik, Fatih; Akar, Zeynep; Küçük, Murat; Sancak, Kemal

2016-01-01

Compound 2 was synthesized by reacting CS 2 /KOH with compound 1. The treatment of compound 2 with hydrazine hydrate produced compound 3. Then, compound 3 was converted to Schiff bases (4a-d) by the handling with several aromatic aldehydes. The treatment of triazole compounds 4a-d containing Schiff base with morpholine gave compounds 5a-d. All compounds were tested for their antioxidant and antimicrobial activities. The antioxidant test results of DPPH• radical scavenging and ferric reducing/antioxidant power methods showed good antioxidant activity. The triazole-thiol (3) was the most active, and the effect of the substituent type of the thiophene ring on the activity was same for both Schiff bases (4a-d) and Mannich bases (5a-d). Among the newly synthesized triazole derivatives, the Schiff base 4d and the Mannich base 5d carrying nitro substituent on the thiophene ring showed promising antibacterial and antifungal activity, with lower MIC values than the standard antibacterial ampicillin.

Stable quantum systems in anti-de Sitter space: Causality, independence, and spectral properties

NASA Astrophysics Data System (ADS)

Buchholz, Detlev; Summers, Stephen J.

2004-12-01

If a state is passive for uniformly accelerated observers in n-dimensional (n⩾2) anti-de Sitter (Ads) space-time (i.e., cannot be used by them to operate a perpetuum mobile), they will (a) register a universal value of the Unruh temperature, (b) discover a PCT symmetry, and (c) find that observables in complementary wedge-shaped regions necessarily commute with each other in this state. The stability properties of such a passive state induce a "geodesic causal structure" on AdS and concommitant locality relations. It is shown that observables in these complementary wedge-shaped regions fulfill strong additional independence conditions. In two-dimensional AdS these even suffice to enable the derivation of a nontrivial, local, covariant net indexed by bounded space-time regions. All these results are model-independent and hold in any theory which is compatible with a weak notion of space-time localization. Examples are provided of models satisfying the hypotheses of these theorems.

Association of δ¹³C in fingerstick blood with added-sugar and sugar-sweetened beverage intake.

PubMed

Davy, Brenda M; Jahren, A Hope; Hedrick, Valisa E; Comber, Dana L

2011-06-01

A reliance on self-reported dietary intake measures is a common research limitation, thus the need for dietary biomarkers. Added-sugar intake may play a role in the development and progression of obesity and related comorbidities; common sweeteners include corn and sugar cane derivatives. These plants contain a high amount of ¹³C, a naturally occurring stable carbon isotope. Consumption of these sweeteners, of which sugar-sweetened beverages are the primary dietary source, might be reflected in the δ¹³C value of blood. Fingerstick blood represents an ideal substrate for bioassay because of its ease of acquisition. The objective of this investigation was to determine if the δ¹³C value of fingerstick blood is a potential biomarker of added-sugar and sugar-sweetened beverage intake. Individuals aged 21 years and older (n = 60) were recruited to attend three laboratory visits; assessments completed at each visit depended upon a randomly assigned sequence (sequence one or two). The initial visit included assessment of height, weight, and dietary intake (sequence one: beverage intake questionnaire, sequence two: 4-day food intake record). Sequence one participants completed a food intake record at visit two, and nonfasting blood samples were obtained via routine fingersticks at visits one and three. Sequence two participants completed a beverage intake questionnaire at visit two, and provided fingerstick blood samples at visits two and three. Samples were analyzed for δ¹³C value using natural abundance stable isotope mass spectrometry. δ¹³C value was compared to dietary outcomes in all participants, as well as among those in the highest and lowest tertile of added-sugar intake. Reported mean added-sugar consumption was 66 ± 5 g/day, and sugar-sweetened beverage consumption was 330 ± 53 g/day and 134 ± 25 kcal/day. Mean fingerstick δ¹³C value was -19.94‰ ± 0.10‰, which differed by body mass index status. δ¹³C value was associated (all P < 0.05) with intake of total added sugars (g, r = 0.37; kcal, r = 0.37), soft drinks (g, r = 0.26; kcal, r = 0.27), and total sugar-sweetened beverage (g, r = 0.28; kcal, r = 0.35). The δ¹³C value in the lowest and the highest added-sugar intake tertiles were significantly different (mean difference = -0.48‰; P = 0.028). Although there are several potential dietary sources for blood carbon, the δ¹³C value of fingerstick blood shows promise as a noninvasive biomarker of added-sugar and sugar-sweetened beverage intake based on these findings. Copyright © 2011 American Dietetic Association. Published by Elsevier Inc. All rights reserved.

Optimization of hydrolysis conditions, isolation, and identification of neuroprotective peptides derived from seahorse Hippocampus trimaculatus.

PubMed

Pangestuti, Ratih; Ryu, Bomi; Himaya, Swa; Kim, Se-Kwon

2013-08-01

Hippocampus trimaculatus is one of the most heavily traded seahorse species for traditional medicine purposes in many countries. In the present study, we showed neuroprotective effects of peptide derived from H. trimaculatus against amyloid-β42 (Aβ42) toxicity which are central to the pathogenesis of Alzheimer's diseases (AD). Firstly, H. trimaculatus was separately hydrolyzed by four different enzymes and tested for their protective effect on Aβ42-induced neurotoxicity in differentiated PC12 cells. Pronase E hydrolysate exerted highest protection with cell viability value of 88.33 ± 3.33 %. Furthermore, we used response surface methodology to optimize pronase E hydrolysis conditions and found that temperature at 36.69 °C with the hydrolysis time 20.01 h, enzyme to substrate (E/S) ratio of 2.02 % and pH 7.34 were the most optimum conditions. Following several purification steps, H. trimaculatus-derived neuroprotective peptides (HTP-1) sequence was identified as Gly-Thr-Glu-Asp-Glu-Leu-Asp-Lys (906.4 Da). HTP-1 protected PC12 cells from Aβ42-induced neuronal death with the cell viability value of 85.52 ± 2.22 % and up-regulated pro-survival gene (Bcl-2) expressions. These results suggest that HTP-1 has the potential to be used in treatment of neurodegenerative diseases, particularly AD. Identification, characterization, and synthesis of bioactive components derived from H. trimaculatus have the potential to replace or at least complement the use of seahorse as traditional medicine, which further may become an approach to minimize seahorse exploitation in traditional medicine.

Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets

DOE PAGES

Wang, Kai; Mao, Jiafu; Dickinson, Robert; ...

2013-06-05

This paper examines a land surface solar radiation partitioning scheme, i.e., that of the Community Land Model version 4 (CLM4) with coupled carbon and nitrogen cycles. Taking advantage of a unique 30-year fraction of absorbed photosynthetically active radiation (FPAR) dataset derived from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) data set, multiple other remote sensing datasets, and site level observations, we evaluated the CLM4 FPAR ’s seasonal cycle, diurnal cycle, long-term trends and spatial patterns. These findings show that the model generally agrees with observations in the seasonal cycle, long-term trends, and spatial patterns,more » but does not reproduce the diurnal cycle. Discrepancies also exist in seasonality magnitudes, peak value months, and spatial heterogeneity. Here, we identify the discrepancy in the diurnal cycle as, due to, the absence of dependence on sun angle in the model. Implementation of sun angle dependence in a one-dimensional (1-D) model is proposed. The need for better relating of vegetation to climate in the model, indicated by long-term trends, is also noted. Evaluation of the CLM4 land surface solar radiation partitioning scheme using remote sensing and site level FPAR datasets provides targets for future development in its representation of this naturally complicated process.« less

Can DCE-MRI Explain the Heterogeneity in Radiopeptide Uptake Imaged by SPECT in a Pancreatic Neuroendocrine Tumor Model?

PubMed Central

Groen, Harald C.; Niessen, Wiro J.; Bernsen, Monique R.; de Jong, Marion; Veenland, Jifke F.

2013-01-01

Although efficient delivery and distribution of treatment agents over the whole tumor is essential for successful tumor treatment, the distribution of most of these agents cannot be visualized. However, with single-photon emission computed tomography (SPECT), both delivery and uptake of radiolabeled peptides can be visualized in a neuroendocrine tumor model overexpressing somatostatin receptors. A heterogeneous peptide uptake is often observed in these tumors. We hypothesized that peptide distribution in the tumor is spatially related to tumor perfusion, vessel density and permeability, as imaged and quantified by DCE-MRI in a neuroendocrine tumor model. Four subcutaneous CA20948 tumor-bearing Lewis rats were injected with the somatostatin-analog 111In-DTPA-Octreotide (50 MBq). SPECT-CT and MRI scans were acquired and MRI was spatially registered to SPECT-CT. DCE-MRI was analyzed using semi-quantitative and quantitative methods. Correlation between SPECT and DCE-MRI was investigated with 1) Spearman’s rank correlation coefficient; 2) SPECT uptake values grouped into deciles with corresponding median DCE-MRI parametric values and vice versa; and 3) linear regression analysis for median parameter values in combined datasets. In all tumors, areas with low peptide uptake correlated with low perfusion/density/ /permeability for all DCE-MRI-derived parameters. Combining all datasets, highest linear regression was found between peptide uptake and semi-quantitative parameters (R2>0.7). The average correlation coefficient between SPECT and DCE-MRI-derived parameters ranged from 0.52-0.56 (p<0.05) for parameters primarily associated with exchange between blood and extracellular extravascular space. For these parameters a linear relation with peptide uptake was observed. In conclusion, the ‘exchange-related’ DCE-MRI-derived parameters seemed to predict peptide uptake better than the ‘contrast amount- related’ parameters. Consequently, fast and efficient diffusion through the vessel wall into tissue is an important factor for peptide delivery. DCE-MRI helps to elucidate the relation between vascular characteristics, peptide delivery and treatment efficacy, and may form a basis to predict targeting efficiency. PMID:24116203

Understanding the Effects of Urban Expansion on Spatio-temporal Variations of Vegetation Phenology at Global Scale from 1993 to 2014

NASA Astrophysics Data System (ADS)

Qiu, T.; Song, C.

2017-12-01

Many studies have examined the urbanization-induced vegetation phenology changes in urban environments at regional scales. However, relatively few studies have investigated the effects of urban expansion on vegetation phenology at global scale. In this study, we used times series of NASA Vegetation Index and Phenology (VIP) and ESA Climate Change Initiative Land Cover datasets to quantify how urban expansion affects growing seasons of vegetation in 14 different biomes along both latitude and urbanization gradients from 1993 to 2014. First, we calculated the percentages of impervious surface area (ISA) at 0.05˚ grid to match the spatial resolution of VIP dataset. We then applied logistic models to the ISA series to characterize the time periods of stable ISA, pre-urbanization and post-urbanization for each grid. The amplitudes of urbanization were also derived from the fitted ISA series. We then calculated the mean values of the Start of Season (SOS), End of Season (EOS) and Length of Season (LOS) from VIP datasets within each period. Linear regressions were used to quantify the correlations between ISA and SOS/EOS/LOS in 14 biomes along the latitude gradient for each period. We also calculated the differences of SOS/EOS/LOS between pre-urbanization and post-urbanization periods and applied quantile regressions to characterize the relationships between amplitudes of urbanization and those differences. We found significant correlations (p-value < 0.05) between ISA and the growing seasons of a) boreal forests at 55-60 ˚N; b) temperate broadleaf and mixed forests at 30-55 ˚N; c) temperate coniferous forests at 30-45 ˚N; d) temperate grasslands, savannas, and shrublands at 35-60 ˚N and 30-35 ˚S. We also found a significant positive correlation (p-value <0.05) between amplitudes of urbanization and LOS as well as a significant negative correlation (p-value<0.05) between amplitudes of urbanization and SOS in temperate broadleaf and mixed forest.

Osteosarcoma enters a post genomic era with in silico opportunities: Generation of the High Dimensional Database for facilitating sarcoma biology research: A report from the Children's Oncology Group and the QuadW Foundation

PubMed Central

Glover, Jason; Man, Tsz-Kwong; Barkauskas, Donald A.; Hall, David; Tello, Tanya; Sullivan, Mary Beth; Gorlick, Richard; Janeway, Katherine; Grier, Holcombe; Lau, Ching; Toretsky, Jeffrey A.; Borinstein, Scott C.; Khanna, Chand

2017-01-01

The prospective banking of osteosarcoma tissue samples to promote research endeavors has been realized through the establishment of a nationally centralized biospecimen repository, the Children’s Oncology Group (COG) biospecimen bank located at the Biopathology Center (BPC)/Nationwide Children’s Hospital in Columbus, Ohio. Although the physical inventory of osteosarcoma biospecimens is substantive (>15,000 sample specimens), the nature of these resources remains exhaustible. Despite judicious allocation of these high-value biospecimens for conducting sarcoma-related research, a deeper understanding of osteosarcoma biology, in particular metastases, remains unrealized. In addition the identification and development of novel diagnostics and effective therapeutics remain elusive. The QuadW-COG Childhood Sarcoma Biostatistics and Annotation Office (CSBAO) has developed the High Dimensional Data (HDD) platform to complement the existing physical inventory and to promote in silico hypothesis testing in sarcoma biology. The HDD is a relational biologic database derived from matched osteosarcoma biospecimens in which diverse experimental readouts have been generated and digitally deposited. As proof-of-concept, we demonstrate that the HDD platform can be utilized to address previously unrealized biologic questions though the systematic juxtaposition of diverse datasets derived from shared biospecimens. The continued population of the HDD platform with high-value, high-throughput and mineable datasets allows a shared and reusable resource for researchers, both experimentalists and bioinformatics investigators, to propose and answer questions in silico that advance our understanding of osteosarcoma biology. PMID:28732082

Comparison of retracked coastal altimetry sea levels against high frequency radar on the continental shelf of the Great Barrier Reef, Australia

NASA Astrophysics Data System (ADS)

Idris, Nurul Hazrina; Deng, Xiaoli; Idris, Nurul Hawani

2017-07-01

Comparison of Jason-1 altimetry retracked sea levels and high frequency (HF) radar velocity is examined within the region of the Great Barrier Reef, Australia. The comparison between both datasets is not direct because the altimetry derives only the geostrophic component, while the HF radar velocity includes information on both geostrophic and ageostrophic components, such as tides and winds. The comparison of altimetry and HF radar data is performed based on the parameter of surface velocity inferred from both datasets. The results show that 48% (10 out of 21 cases) of data have high (≥0.5) spatial correlation. The mean of spatial correlation for all 21 cases is 0.43. This value is within the range (0.42 to 0.5) observed by other studies. Low correlation is observed due to disagreement in the trend of velocity signals in which sometimes they have contradictions in the signal direction and the position of the peak is shifted. In terms of standard deviation of difference and root mean square error, both datasets show reasonable agreement with ≤2.5 cm s-1.

Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques.

PubMed

Mobasheri, Amin; Huang, Haosheng; Degrossi, Lívia Castro; Zipf, Alexander

2018-02-08

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset ("ground truth dataset"). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions.

Integrated microbial processes for biofuels and high value-added products: the way to improve the cost effectiveness of biofuel production.

PubMed

da Silva, Teresa Lopes; Gouveia, Luísa; Reis, Alberto

2014-02-01

The production of microbial biofuels is currently under investigation, as they are alternative sources to fossil fuels, which are diminishing and their use has a negative impact on the environment. However, so far, biofuels derived from microbes are not economically competitive. One way to overcome this bottleneck is the use of microorganisms to transform substrates into biofuels and high value-added products, and simultaneously taking advantage of the various microbial biomass components to produce other products of interest, as an integrated process. In this way, it is possible to maximize the economic value of the whole process, with the desired reduction of the waste streams produced. It is expected that this integrated system makes the biofuel production economically sustainable and competitive in the near future. This review describes the investigation on integrated microbial processes (based on bacteria, yeast, and microalgal cultivations) that have been experimentally developed, highlighting the importance of this approach as a way to optimize microbial biofuel production process.

Informative graphing of continuous safety variables relative to normal reference limits.

PubMed

Breder, Christopher D

2018-05-16

Interpreting graphs of continuous safety variables can be complicated because differences in age, gender, and testing site methodologies data may give rise to multiple reference limits. Furthermore, data below the lower limit of normal are compressed relative to those points above the upper limit of normal. The objective of this study is to develop a graphing technique that addresses these issues and is visually intuitive. A mock dataset with multiple reference ranges is initially used to develop the graphing technique. Formulas are developed for conditions where data are above the upper limit of normal, normal, below the lower limit of normal, and below the lower limit of normal when the data value equals zero. After the formulae are developed, an anonymized dataset from an actual set of trials for an approved drug is evaluated comparing the technique developed in this study to standard graphical methods. Formulas are derived for the novel graphing method based on multiples of the normal limits. The formula for values scaled between the upper and lower limits of normal is a novel application of a readily available scaling formula. The formula for the lower limit of normal is novel and addresses the issue of this value potentially being indeterminate when the result to be scaled as a multiple is zero. The formulae and graphing method described in this study provides a visually intuitive method to graph continuous safety data including laboratory values, vital sign data.

Decoding how a soil bacterium extracts building blocks and metabolic energy from ligninolysis provides road map for lignin valorization (Towards Lignin valorization: How a soil bacterium extracts building blocks and metabolic energy from "Lignolysis")

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

Varman, Arul M.; He, Lian; Follenfant, Rhiannon

Lignin is a major resources for the production of next generation renewable aromatics. Sphingobium sp. SYK-6 is a bacterium that has been well-studied for the breakdown of lignin-derived compounds. There has been a lot of interest in SYK-6 lignolytic activity and many recent works have focused on understanding the unique catabolic pathway it possesses for the degradation of lignin derived monomers and oligomers. Furthermore, there has been no prior effort in understanding the central fluxome based on lignin derived substrates into value-added chemicals.

Decoding how a soil bacterium extracts building blocks and metabolic energy from ligninolysis provides road map for lignin valorization (Towards Lignin valorization: How a soil bacterium extracts building blocks and metabolic energy from "Lignolysis")

DOE PAGES

Varman, Arul M.; He, Lian; Follenfant, Rhiannon; ...

2016-09-15

Lignin is a major resources for the production of next generation renewable aromatics. Sphingobium sp. SYK-6 is a bacterium that has been well-studied for the breakdown of lignin-derived compounds. There has been a lot of interest in SYK-6 lignolytic activity and many recent works have focused on understanding the unique catabolic pathway it possesses for the degradation of lignin derived monomers and oligomers. Furthermore, there has been no prior effort in understanding the central fluxome based on lignin derived substrates into value-added chemicals.

Statistical link between external climate forcings and modes of ocean variability

NASA Astrophysics Data System (ADS)

Malik, Abdul; Brönnimann, Stefan; Perona, Paolo

2017-07-01

In this study we investigate statistical link between external climate forcings and modes of ocean variability on inter-annual (3-year) to centennial (100-year) timescales using de-trended semi-partial-cross-correlation analysis technique. To investigate this link we employ observations (AD 1854-1999), climate proxies (AD 1600-1999), and coupled Atmosphere-Ocean-Chemistry Climate Model simulations with SOCOL-MPIOM (AD 1600-1999). We find robust statistical evidence that Atlantic multi-decadal oscillation (AMO) has intrinsic positive correlation with solar activity in all datasets employed. The strength of the relationship between AMO and solar activity is modulated by volcanic eruptions and complex interaction among modes of ocean variability. The observational dataset reveals that El Niño southern oscillation (ENSO) has statistically significant negative intrinsic correlation with solar activity on decadal to multi-decadal timescales (16-27-year) whereas there is no evidence of a link on a typical ENSO timescale (2-7-year). In the observational dataset, the volcanic eruptions do not have a link with AMO on a typical AMO timescale (55-80-year) however the long-term datasets (proxies and SOCOL-MPIOM output) show that volcanic eruptions have intrinsic negative correlation with AMO on inter-annual to multi-decadal timescales. The Pacific decadal oscillation has no link with solar activity, however, it has positive intrinsic correlation with volcanic eruptions on multi-decadal timescales (47-54-year) in reconstruction and decadal to multi-decadal timescales (16-32-year) in climate model simulations. We also find evidence of a link between volcanic eruptions and ENSO, however, the sign of relationship is not consistent between observations/proxies and climate model simulations.

Development and validation of the San Diego Early Test Score to predict acute and early HIV infection risk in men who have sex with men.

PubMed

Hoenigl, Martin; Weibel, Nadir; Mehta, Sanjay R; Anderson, Christy M; Jenks, Jeffrey; Green, Nella; Gianella, Sara; Smith, Davey M; Little, Susan J

2015-08-01

Although men who have sex with men (MSM) represent a dominant risk group for human immunodeficiency virus (HIV), the risk of HIV infection within this population is not uniform. The objective of this study was to develop and validate a score to estimate incident HIV infection risk. Adult MSM who were tested for acute and early HIV (AEH) between 2008 and 2014 were retrospectively randomized 2:1 to a derivation and validation dataset, respectively. Using the derivation dataset, each predictor associated with an AEH outcome in the multivariate prediction model was assigned a point value that corresponded to its odds ratio. The score was validated on the validation dataset using C-statistics. Data collected at a single HIV testing encounter from 8326 unique MSM were analyzed, including 200 with AEH (2.4%). Four risk behavior variables were significantly associated with an AEH diagnosis (ie, incident infection) in multivariable analysis and were used to derive the San Diego Early Test (SDET) score: condomless receptive anal intercourse (CRAI) with an HIV-positive MSM (3 points), the combination of CRAI plus ≥5 male partners (3 points), ≥10 male partners (2 points), and diagnosis of bacterial sexually transmitted infection (2 points)-all as reported for the prior 12 months. The C-statistic for this risk score was >0.7 in both data sets. The SDET risk score may help to prioritize resources and target interventions, such as preexposure prophylaxis, to MSM at greatest risk of acquiring HIV infection. The SDET risk score is deployed as a freely available tool at http://sdet.ucsd.edu. © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Chemical Conversions of Biomass-Derived Platform Chemicals over Copper-Silica Nanocomposite Catalysts.

PubMed

Upare, Pravin P; Hwang, Young Kyu; Lee, Jong-Min; Hwang, Dong Won; Chang, Jong-San

2015-07-20

Biomass and biomass-derived carbohydrates have a high extent of functionality, unlike petroleum, which has limited functionality. In biorefinery applications, the development of methods to control the extent of functionality in final products intended for use as fuels and chemicals is a challenge. In the chemical industry, heterogeneous catalysis is an important tool for the defunctionalization of functionalized feedstocks and biomass-derived platform chemicals to produce value-added chemicals. Herein, we review the recent progress in this field, mainly of vapor phase chemical conversion of biomass-derived C4 -C6 carboxylic acids and esters using copper-silica nanocomposite catalysts. We also demonstrate that these nanocomposite catalysts very efficiently convert biomass-derived platform chemicals into cyclic compounds, such as lactones and hydrofurans, with high selectivities and yields. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Study of Derivative Filters Using the Discrete Fourier Transform. Final Report M. S. Thesis

NASA Technical Reports Server (NTRS)

Ioup, G. E.

1980-01-01

Important properties of derivative (difference) filters using the discrete Fourier transform are investigated. The filters are designed using the derivative theorem of Fourier analysis. Because physical data are generally degraded by noise, the derivative filter is modified to diminish the effects of the noise, especially the noise amplification which normally occurs while differencing. The basis for these modifications is the reduction of those Fourier components for which the noise most dominates the data. The various filters are tested by applying them to find differences of two-dimensional data to which various amounts of signal dependent noise, as measured by a root mean square value, have been added. The modifications, circular and square ideal low-pass filters and a cut-off pyramid filter, are all found to reduce noise in the derivative without significantly degrading the result.

A global multicenter study on reference values: 1. Assessment of methods for derivation and comparison of reference intervals.

PubMed

Ichihara, Kiyoshi; Ozarda, Yesim; Barth, Julian H; Klee, George; Qiu, Ling; Erasmus, Rajiv; Borai, Anwar; Evgina, Svetlana; Ashavaid, Tester; Khan, Dilshad; Schreier, Laura; Rolle, Reynan; Shimizu, Yoshihisa; Kimura, Shogo; Kawano, Reo; Armbruster, David; Mori, Kazuo; Yadav, Binod K

2017-04-01

The IFCC Committee on Reference Intervals and Decision Limits coordinated a global multicenter study on reference values (RVs) to explore rational and harmonizable procedures for derivation of reference intervals (RIs) and investigate the feasibility of sharing RIs through evaluation of sources of variation of RVs on a global scale. For the common protocol, rather lenient criteria for reference individuals were adopted to facilitate harmonized recruitment with planned use of the latent abnormal values exclusion (LAVE) method. As of July 2015, 12 countries had completed their study with total recruitment of 13,386 healthy adults. 25 analytes were measured chemically and 25 immunologically. A serum panel with assigned values was measured by all laboratories. RIs were derived by parametric and nonparametric methods. The effect of LAVE methods is prominent in analytes which reflect nutritional status, inflammation and muscular exertion, indicating that inappropriate results are frequent in any country. The validity of the parametric method was confirmed by the presence of analyte-specific distribution patterns and successful Gaussian transformation using the modified Box-Cox formula in all countries. After successful alignment of RVs based on the panel test results, nearly half the analytes showed variable degrees of between-country differences. This finding, however, requires confirmation after adjusting for BMI and other sources of variation. The results are reported in the second part of this paper. The collaborative study enabled us to evaluate rational methods for deriving RIs and comparing the RVs based on real-world datasets obtained in a harmonized manner. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Physico-chemical characterisation of material fractions in household waste: Overview of data in literature.

PubMed

Götze, Ramona; Boldrin, Alessio; Scheutz, Charlotte; Astrup, Thomas Fruergaard

2016-03-01

State-of-the-art environmental assessment of waste management systems rely on data for the physico-chemical composition of individual material fractions comprising the waste in question. To derive the necessary inventory data for different scopes and systems, literature data from different sources and backgrounds are consulted and combined. This study provides an overview of physico-chemical waste characterisation data for individual waste material fractions available in literature and thereby aims to support the selection of data fitting to a specific scope and the selection of uncertainty ranges related to the data selection from literature. Overall, 97 publications were reviewed with respect to employed characterisation method, regional origin of the waste, number of investigated parameters and material fractions and other qualitative aspects. Descriptive statistical analysis of the reported physico-chemical waste composition data was performed to derive value ranges and data distributions for element concentrations (e.g. Cd content) and physical parameters (e.g. heating value). Based on 11,886 individual data entries, median values and percentiles for 47 parameters in 11 individual waste fractions are presented. Exceptional values and publications are identified and discussed. Detailed datasets are attached to this study, allowing further analysis and new applications of the data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Generic NICA-Donnan model parameters for metal-ion binding by humic substances.

PubMed

Milne, Christopher J; Kinniburgh, David G; van Riemsdijk, Willem H; Tipping, Edward

2003-03-01

A total of 171 datasets of literature and experimental data for metal-ion binding by fulvic and humic acids have been digitized and re-analyzed using the NICA-Donnan model. Generic parameter values have been derived that can be used for modeling in the absence of specific metalion binding measurements. These values complement the previously derived generic descriptions of proton binding. For ions where the ranges of pH, concentration, and ionic strength conditions are well covered by the available data,the generic parameters successfully describe the metalion binding behavior across a very wide range of conditions and for different humic and fulvic acids. Where published data for other metal ions are too sparse to constrain the model well, generic parameters have been estimated by interpolating trends observable in the parameter values of the well-defined data. Recommended generic NICA-Donnan model parameters are provided for 23 metal ions (Al, Am, Ba, Ca, Cd, Cm, Co, CrIII, Cu, Dy, Eu, FeII, FeIII, Hg, Mg, Mn, Ni, Pb, Sr, Thv, UVIO2, VIIIO, and Zn) for both fulvic and humic acids. These parameters probably represent the best NICA-Donnan description of metal-ion binding that can be achieved using existing data.

Photolysis Rate Coefficient Calculations in Support of SOLVE Campaign

NASA Technical Reports Server (NTRS)

Lloyd, Steven A.; Swartz, William H.

2001-01-01

The objectives for this SOLVE project were 3-fold. First, we sought to calculate a complete set of photolysis rate coefficients (j-values) for the campaign along the ER-2 and DC-8 flight tracks. En route to this goal, it would be necessary to develop a comprehensive set of input geophysical conditions (e.g., ozone profiles), derived from various climatological, aircraft, and remotely sensed datasets, in order to model the radiative transfer of the atmosphere accurately. These j-values would then need validation by comparison with flux-derived j-value measurements. The second objective was to analyze chemistry along back trajectories using the NASA/Goddard chemistry trajectory model initialized with measurements of trace atmospheric constituents. This modeling effort would provide insight into the completeness of current measurements and the chemistry of Arctic wintertime ozone loss. Finally, we sought to coordinate stellar occultation measurements of ozone (and thus ozone loss) during SOLVE using the Midcourse Space Experiment(MSX)/Ultraviolet and Visible Imagers and Spectrographic Imagers (UVISI) satellite instrument. Such measurements would determine ozone loss during the Arctic polar night and represent the first significant science application of space-based stellar occultation in the Earth's atmosphere.

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

PubMed

Zhang, Hua; Kurgan, Lukasz

2014-12-01

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.

PubMed

Zhang, Buzhong; Li, Linqing; Lü, Qiang

2018-05-25

Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson's correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset.

Low rank approach to computing first and higher order derivatives using automatic differentiation

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

Reed, J. A.; Abdel-Khalik, H. S.; Utke, J.

2012-07-01

This manuscript outlines a new approach for increasing the efficiency of applying automatic differentiation (AD) to large scale computational models. By using the principles of the Efficient Subspace Method (ESM), low rank approximations of the derivatives for first and higher orders can be calculated using minimized computational resources. The output obtained from nuclear reactor calculations typically has a much smaller numerical rank compared to the number of inputs and outputs. This rank deficiency can be exploited to reduce the number of derivatives that need to be calculated using AD. The effective rank can be determined according to ESM by computingmore » derivatives with AD at random inputs. Reduced or pseudo variables are then defined and new derivatives are calculated with respect to the pseudo variables. Two different AD packages are used: OpenAD and Rapsodia. OpenAD is used to determine the effective rank and the subspace that contains the derivatives. Rapsodia is then used to calculate derivatives with respect to the pseudo variables for the desired order. The overall approach is applied to two simple problems and to MATWS, a safety code for sodium cooled reactors. (authors)« less

Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets.

PubMed

Martínez-Santiago, O; Marrero-Ponce, Y; Vivas-Reyes, R; Rivera-Borroto, O M; Hurtado, E; Treto-Suarez, M A; Ramos, Y; Vergara-Murillo, F; Orozco-Ugarriza, M E; Martínez-López, Y

2017-05-01

Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q 2 ext ) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q 2 ext values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.

Hydrography change detection: the usefulness of surface channels derived From LiDAR DEMs for updating mapped hydrography

USGS Publications Warehouse

Poppenga, Sandra K.; Gesch, Dean B.; Worstell, Bruce B.

2013-01-01

The 1:24,000-scale high-resolution National Hydrography Dataset (NHD) mapped hydrography flow lines require regular updating because land surface conditions that affect surface channel drainage change over time. Historically, NHD flow lines were created by digitizing surface water information from aerial photography and paper maps. Using these same methods to update nationwide NHD flow lines is costly and inefficient; furthermore, these methods result in hydrography that lacks the horizontal and vertical accuracy needed for fully integrated datasets useful for mapping and scientific investigations. Effective methods for improving mapped hydrography employ change detection analysis of surface channels derived from light detection and ranging (LiDAR) digital elevation models (DEMs) and NHD flow lines. In this article, we describe the usefulness of surface channels derived from LiDAR DEMs for hydrography change detection to derive spatially accurate and time-relevant mapped hydrography. The methods employ analyses of horizontal and vertical differences between LiDAR-derived surface channels and NHD flow lines to define candidate locations of hydrography change. These methods alleviate the need to analyze and update the nationwide NHD for time relevant hydrography, and provide an avenue for updating the dataset where change has occurred.

Dataset definition for CMS operations and physics analyses

NASA Astrophysics Data System (ADS)

Franzoni, Giovanni; Compact Muon Solenoid Collaboration

2016-04-01

Data recorded at the CMS experiment are funnelled into streams, integrated in the HLT menu, and further organised in a hierarchical structure of primary datasets and secondary datasets/dedicated skims. Datasets are defined according to the final-state particles reconstructed by the high level trigger, the data format and the use case (physics analysis, alignment and calibration, performance studies). During the first LHC run, new workflows have been added to this canonical scheme, to exploit at best the flexibility of the CMS trigger and data acquisition systems. The concepts of data parking and data scouting have been introduced to extend the physics reach of CMS, offering the opportunity of defining physics triggers with extremely loose selections (e.g. dijet resonance trigger collecting data at a 1 kHz). In this presentation, we review the evolution of the dataset definition during the LHC run I, and we discuss the plans for the run II.

Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000

NASA Astrophysics Data System (ADS)

Reba, Meredith; Reitsma, Femke; Seto, Karen C.

2016-06-01

How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional environments? In order to understand the current era of urbanization, we must understand long-term historical urbanization trends and patterns. However, to date there is no comprehensive record of spatially explicit, historic, city-level population data at the global scale. Here, we developed the first spatially explicit dataset of urban settlements from 3700 BC to AD 2000, by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data previously published in tabular form by Chandler and Modelski. The dataset creation process also required data cleaning and harmonization procedures to make the data internally consistent. Additionally, we created a reliability ranking for each geocoded location to assess the geographic uncertainty of each data point. The dataset provides the first spatially explicit archive of the location and size of urban populations over the last 6,000 years and can contribute to an improved understanding of contemporary and historical urbanization trends.

Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000

PubMed Central

Reba, Meredith; Reitsma, Femke; Seto, Karen C.

2016-01-01

How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional environments? In order to understand the current era of urbanization, we must understand long-term historical urbanization trends and patterns. However, to date there is no comprehensive record of spatially explicit, historic, city-level population data at the global scale. Here, we developed the first spatially explicit dataset of urban settlements from 3700 BC to AD 2000, by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data previously published in tabular form by Chandler and Modelski. The dataset creation process also required data cleaning and harmonization procedures to make the data internally consistent. Additionally, we created a reliability ranking for each geocoded location to assess the geographic uncertainty of each data point. The dataset provides the first spatially explicit archive of the location and size of urban populations over the last 6,000 years and can contribute to an improved understanding of contemporary and historical urbanization trends. PMID:27271481

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods

PubMed Central

Hancock, Matthew C.; Magnan, Jerry F.

2016-01-01

Abstract. In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists’ annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (±1.14)%, which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (±0.012), which increases to 0.949 (±0.007) when diameter and volume features are included and has an accuracy of 88.08 (±1.11)%. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification. PMID:27990453

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.

PubMed

Hancock, Matthew C; Magnan, Jerry F

2016-10-01

In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 [Formula: see text], which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 ([Formula: see text]), which increases to 0.949 ([Formula: see text]) when diameter and volume features are included and has an accuracy of 88.08 [Formula: see text]. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

Comparison of epicardial adipose tissue radiodensity threshold between contrast and non-contrast enhanced computed tomography scans: A cohort study of derivation and validation.

PubMed

Xu, Lingyu; Xu, Yuancheng; Coulden, Richard; Sonnex, Emer; Hrybouski, Stanislau; Paterson, Ian; Butler, Craig

2018-05-11

Epicardial adipose tissue (EAT) volume derived from contrast enhanced (CE) computed tomography (CT) scans is not well validated. We aim to establish a reliable threshold to accurately quantify EAT volume from CE datasets. We analyzed EAT volume on paired non-contrast (NC) and CE datasets from 25 patients to derive appropriate Hounsfield (HU) cutpoints to equalize two EAT volume estimates. The gold standard threshold (-190HU, -30HU) was used to assess EAT volume on NC datasets. For CE datasets, EAT volumes were estimated using three previously reported thresholds: (-190HU, -30HU), (-190HU, -15HU), (-175HU, -15HU) and were analyzed by a semi-automated 3D Fat analysis software. Subsequently, we applied a threshold correction to (-190HU, -30HU) based on mean differences in radiodensity between NC and CE images (ΔEATrd = CE radiodensity - NC radiodensity). We then validated our findings on EAT threshold in 21 additional patients with paired CT datasets. EAT volume from CE datasets using previously published thresholds consistently underestimated EAT volume from NC dataset standard by a magnitude of 8.2%-19.1%. Using our corrected threshold (-190HU, -3HU) in CE datasets yielded statistically identical EAT volume to NC EAT volume in the validation cohort (186.1 ± 80.3 vs. 185.5 ± 80.1 cm 3 , Δ = 0.6 cm 3 , 0.3%, p = 0.374). Estimating EAT volume from contrast enhanced CT scans using a corrected threshold of -190HU, -3HU provided excellent agreement with EAT volume from non-contrast CT scans using a standard threshold of -190HU, -30HU. Copyright © 2018. Published by Elsevier B.V.

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

Saaban, Azizan; Zainudin, Lutfi; Bakar, Mohd Nazari Abu

This paper intends to reveal the ability of the linear interpolation method to predict missing values in solar radiation time series. Reliable dataset is equally tends to complete time series observed dataset. The absence or presence of radiation data alters long-term variation of solar radiation measurement values. Based on that change, the opportunities to provide bias output result for modelling and the validation process is higher. The completeness of the observed variable dataset has significantly important for data analysis. Occurrence the lack of continual and unreliable time series solar radiation data widely spread and become the main problematic issue. However,more » the limited number of research quantity that has carried out to emphasize and gives full attention to estimate missing values in the solar radiation dataset.« less

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

Fast, J; Zhang, Q; Tilp, A

Significantly improved returns in their aerosol chemistry data can be achieved via the development of a value-added product (VAP) of deriving OA components, called Organic Aerosol Components (OACOMP). OACOMP is primarily based on multivariate analysis of the measured organic mass spectral matrix. The key outputs of OACOMP are the concentration time series and the mass spectra of OA factors that are associated with distinct sources, formation and evolution processes, and physicochemical properties.

Missing value imputation for microarray data: a comprehensive comparison study and a web tool.

PubMed

Chiu, Chia-Chun; Chan, Shih-Yao; Wang, Chung-Ching; Wu, Wei-Sheng

2013-01-01

Microarray data are usually peppered with missing values due to various reasons. However, most of the downstream analyses for microarray data require complete datasets. Therefore, accurate algorithms for missing value estimation are needed for improving the performance of microarray data analyses. Although many algorithms have been developed, there are many debates on the selection of the optimal algorithm. The studies about the performance comparison of different algorithms are still incomprehensive, especially in the number of benchmark datasets used, the number of algorithms compared, the rounds of simulation conducted, and the performance measures used. In this paper, we performed a comprehensive comparison by using (I) thirteen datasets, (II) nine algorithms, (III) 110 independent runs of simulation, and (IV) three types of measures to evaluate the performance of each imputation algorithm fairly. First, the effects of different types of microarray datasets on the performance of each imputation algorithm were evaluated. Second, we discussed whether the datasets from different species have different impact on the performance of different algorithms. To assess the performance of each algorithm fairly, all evaluations were performed using three types of measures. Our results indicate that the performance of an imputation algorithm mainly depends on the type of a dataset but not on the species where the samples come from. In addition to the statistical measure, two other measures with biological meanings are useful to reflect the impact of missing value imputation on the downstream data analyses. Our study suggests that local-least-squares-based methods are good choices to handle missing values for most of the microarray datasets. In this work, we carried out a comprehensive comparison of the algorithms for microarray missing value imputation. Based on such a comprehensive comparison, researchers could choose the optimal algorithm for their datasets easily. Moreover, new imputation algorithms could be compared with the existing algorithms using this comparison strategy as a standard protocol. In addition, to assist researchers in dealing with missing values easily, we built a web-based and easy-to-use imputation tool, MissVIA (http://cosbi.ee.ncku.edu.tw/MissVIA), which supports many imputation algorithms. Once users upload a real microarray dataset and choose the imputation algorithms, MissVIA will determine the optimal algorithm for the users' data through a series of simulations, and then the imputed results can be downloaded for the downstream data analyses.

Advancing Collaboration through Hydrologic Data and Model Sharing

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Idaszak, R.; Horsburgh, J. S.; Ames, D. P.; Goodall, J. L.; Band, L. E.; Merwade, V.; Couch, A.; Hooper, R. P.; Maidment, D. R.; Dash, P. K.; Stealey, M.; Yi, H.; Gan, T.; Castronova, A. M.; Miles, B.; Li, Z.; Morsy, M. M.

2015-12-01

HydroShare is an online, collaborative system for open sharing of hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around "resources" which are defined primarily by standardized metadata, content data models for each resource type, and an overarching resource data model based on the Open Archives Initiative's Object Reuse and Exchange (OAI-ORE) standard and a hierarchical file packaging system called "BagIt". HydroShare expands the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated to include geospatial and multidimensional space-time datasets commonly used in hydrology. HydroShare also includes new capability for sharing models, model components, and analytical tools and will take advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. It also supports web services and server/cloud based computation operating on resources for the execution of hydrologic models and analysis and visualization of hydrologic data. HydroShare uses iRODS as a network file system for underlying storage of datasets and models. Collaboration is enabled by casting datasets and models as "social objects". Social functions include both private and public sharing, formation of collaborative groups of users, and value-added annotation of shared datasets and models. The HydroShare web interface and social media functions were developed using the Django web application framework coupled to iRODS. Data visualization and analysis is supported through the Tethys Platform web GIS software stack. Links to external systems are supported by RESTful web service interfaces to HydroShare's content. This presentation will introduce the HydroShare functionality developed to date and describe ongoing development of functionality to support collaboration and integration of data and models.

Bioreactors for lignocellulose conversion into fermentable sugars for production of high added value products.

PubMed

Liguori, Rossana; Ventorino, Valeria; Pepe, Olimpia; Faraco, Vincenza

2016-01-01

Lignocellulosic biomasses derived from dedicated crops and agro-industrial residual materials are promising renewable resources for the production of fuels and other added value bioproducts. Due to the tolerance to a wide range of environments, the dedicated crops can be cultivated on marginal lands, avoiding conflict with food production and having beneficial effects on the environment. Besides, the agro-industrial residual materials represent an abundant, available, and cheap source of bioproducts that completely cut out the economical and environmental issues related to the cultivation of energy crops. Different processing steps like pretreatment, hydrolysis and microbial fermentation are needed to convert biomass into added value bioproducts. The reactor configuration, the operative conditions, and the operation mode of the conversion processes are crucial parameters for a high yield and productivity of the biomass bioconversion process. This review summarizes the last progresses in the bioreactor field, with main attention on the new configurations and the agitation systems, for conversion of dedicated energy crops (Arundo donax) and residual materials (corn stover, wheat straw, mesquite wood, agave bagasse, fruit and citrus peel wastes, sunflower seed hull, switchgrass, poplar sawdust, cogon grass, sugarcane bagasse, sunflower seed hull, and poplar wood) into sugars and ethanol. The main novelty of this review is its focus on reactor components and properties.

A cross-country Exchange Market Pressure (EMP) dataset.

PubMed

Desai, Mohit; Patnaik, Ila; Felman, Joshua; Shah, Ajay

2017-06-01

The data presented in this article are related to the research article titled - "An exchange market pressure measure for cross country analysis" (Patnaik et al. [1]). In this article, we present the dataset for Exchange Market Pressure values (EMP) for 139 countries along with their conversion factors, ρ (rho). Exchange Market Pressure, expressed in percentage change in exchange rate, measures the change in exchange rate that would have taken place had the central bank not intervened. The conversion factor ρ can interpreted as the change in exchange rate associated with $1 billion of intervention. Estimates of conversion factor ρ allow us to calculate a monthly time series of EMP for 139 countries. Additionally, the dataset contains the 68% confidence interval (high and low values) for the point estimates of ρ 's. Using the standard errors of estimates of ρ 's, we obtain one sigma intervals around mean estimates of EMP values. These values are also reported in the dataset.

A Unified Probabilistic Framework for Dose-Response Assessment of Human Health Effects.

PubMed

Chiu, Weihsueh A; Slob, Wout

2015-12-01

When chemical health hazards have been identified, probabilistic dose-response assessment ("hazard characterization") quantifies uncertainty and/or variability in toxicity as a function of human exposure. Existing probabilistic approaches differ for different types of endpoints or modes-of-action, lacking a unifying framework. We developed a unified framework for probabilistic dose-response assessment. We established a framework based on four principles: a) individual and population dose responses are distinct; b) dose-response relationships for all (including quantal) endpoints can be recast as relating to an underlying continuous measure of response at the individual level; c) for effects relevant to humans, "effect metrics" can be specified to define "toxicologically equivalent" sizes for this underlying individual response; and d) dose-response assessment requires making adjustments and accounting for uncertainty and variability. We then derived a step-by-step probabilistic approach for dose-response assessment of animal toxicology data similar to how nonprobabilistic reference doses are derived, illustrating the approach with example non-cancer and cancer datasets. Probabilistically derived exposure limits are based on estimating a "target human dose" (HDMI), which requires risk management-informed choices for the magnitude (M) of individual effect being protected against, the remaining incidence (I) of individuals with effects ≥ M in the population, and the percent confidence. In the example datasets, probabilistically derived 90% confidence intervals for HDMI values span a 40- to 60-fold range, where I = 1% of the population experiences ≥ M = 1%-10% effect sizes. Although some implementation challenges remain, this unified probabilistic framework can provide substantially more complete and transparent characterization of chemical hazards and support better-informed risk management decisions.

Radiation processing of natural polymers: The IAEA contribution

NASA Astrophysics Data System (ADS)

Haji-Saeid, Mohammad; Safrany, Agnes; Sampa, Maria Helena de O.; Ramamoorthy, Natesan

2010-03-01

Radiation processing offers a clean and additive-free method for preparation of value-added novel materials based on renewable, non-toxic, and biodegradable natural polymers. Crosslinked natural polymers can be used as hydrogel wound dressings, face cleaning cosmetic masks, adsorbents of toxins, and non-bedsore mats; while low molecular weight products show antibiotic, antioxidant, and plant-growth promoting properties. Recognizing the potential benefits that radiation technology can offer for processing of natural polymers into useful products, the IAEA implemented a coordinated research project (CRP) on "Development of Radiation-processed products of Natural Polymers for application in Agriculture, Healthcare, Industry and Environment". This CRP was launched at the end of 2007 with participation of 16 MS to help connecting radiation technology and end-users to derive enhanced benefits from these new value-added products of radiation-processed natural materials. In this paper the results of activities in participating MS related to this work will be presented.

Chapter 17: Adding Value to the Biorefinery with Lignin: An Engineer's Perspective

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

Biddy, Mary J

There is a long-standing belief that 'you can make anything out of lignin...except money.' This chapter serves to highlight that opportunities for making money from biomass-derived lignin exist both with current technology in the production of steam and power to new emerging areas of R&D focused on value-added chemical and material coproducts from lignin. To understand and quantify the economic potential for lignin valorization, the techno-economic analysis methodology is first described in detail. As demonstrated in the provided case study, these types of economic evaluations serve not only to estimate the economic impacts that lignin conversion could have for anmore » integrated biorefinery and outline drivers for further cost reduction but also identify data gaps and R&D needs for improving the design basis and reducing the risk for process scale-up.« less

Relative age effect in elite soccer: More early-born players, but no better valued, and no paragon clubs or countries

PubMed Central

Doyle, John R.

2018-01-01

The paper analyses two datasets of elite soccer players (top 1000 professionals and UEFA Under-19 Youth League). In both, we find a Relative Age Effect (RAE) for frequency, but not for value. That is, while there are more players born at the start of the competition year, their transfer values are no higher, nor are they given more game time. We use Poisson regression to derive a transparent index of the discrimination present in RAE. Also, because Poisson is valid for small frequency counts, it supports analysis at the disaggregated levels of country and club. From this, we conclude there are no paragon clubs or countries immune to RAE; that is clubs and countries do not differ systematically in the RAE they experience; also, that Poisson regression is a powerful and flexible method of analysing RAE data. PMID:29420576

Unmanned Aerial Systems, Moored Balloons, and the U.S. Department of Energy ARM Facilities in Alaska

NASA Astrophysics Data System (ADS)

Ivey, Mark; Verlinde, Johannes

2014-05-01

The U.S. Department of Energy (DOE), through its scientific user facility, the Atmospheric Radiation Measurement (ARM) Climate Research Facility, provides scientific infrastructure and data to the international Arctic research community via its research sites located on the North Slope of Alaska. Facilities and infrastructure to support operations of unmanned aerial systems for science missions in the Arctic and North Slope of Alaska were established at Oliktok Point Alaska in 2013. Tethered instrumented balloons will be used in the near future to make measurements of clouds in the boundary layer including mixed-phase clouds. The DOE ARM Program has operated an atmospheric measurement facility in Barrow, Alaska, since 1998. Major upgrades to this facility, including scanning radars, were added in 2010. Arctic Observing Networks are essential to meet growing policy, social, commercial, and scientific needs. Calibrated, high-quality arctic geophysical datasets that span ten years or longer are especially important for climate studies, climate model initializations and validations, and for related climate policy activities. For example, atmospheric data and derived atmospheric forcing estimates are critical for sea-ice simulations. International requirements for well-coordinated, long-term, and sustained Arctic Observing Networks and easily-accessible data sets collected by those networks have been recognized by many high-level workshops and reports (Arctic Council Meetings and workshops, National Research Council reports, NSF workshops and others). The recent Sustaining Arctic Observation Network (SAON) initiative sponsored a series of workshops to "develop a set of recommendations on how to achieve long-term Arctic-wide observing activities that provide free, open, and timely access to high-quality data that will realize pan-Arctic and global value-added services and provide societal benefits." This poster will present information on opportunities for members of the arctic research community to make atmospheric measurements using unmanned aerial systems or tethered balloons.

Flight-determined aerodynamic derivatives of the AD-1 oblique-wing research airplane

NASA Technical Reports Server (NTRS)

Sim, A. G.; Curry, R. E.

1984-01-01

The AD-1 is a variable-sweep oblique-wing research airplane that exhibits unconventional stability and control characteristics. In this report, flight-determined and predicted stability and control derivatives for the AD-1 airplane are compared. The predictions are based on both wind tunnel and computational results. A final best estimate of derivatives is presented.

Topic modeling for cluster analysis of large biological and medical datasets

PubMed Central

2014-01-01

Background The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. Results In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Conclusion Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets. PMID:25350106

Topic modeling for cluster analysis of large biological and medical datasets.

PubMed

Zhao, Weizhong; Zou, Wen; Chen, James J

2014-01-01

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets.

Evaluating the Consistency of the 1982–1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II

PubMed Central

Alcaraz-Segura, Domingo; Liras, Elisa; Tabik, Siham; Paruelo, José; Cabello, Javier

2010-01-01

Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations’ carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982–1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast. PMID:22205868

Evaluating the consistency of the 1982-1999 NDVI trends in the Iberian Peninsula across four time-series derived from the AVHRR sensor: LTDR, GIMMS, FASIR, and PAL-II.

PubMed

Alcaraz-Segura, Domingo; Liras, Elisa; Tabik, Siham; Paruelo, José; Cabello, Javier

2010-01-01

Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations' carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982-1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast.

A Novel Model for Predicting Rehospitalization Risk Incorporating Physical Function, Cognitive Status, and Psychosocial Support Using Natural Language Processing.

PubMed

Greenwald, Jeffrey L; Cronin, Patrick R; Carballo, Victoria; Danaei, Goodarz; Choy, Garry

2017-03-01

With the increasing focus on reducing hospital readmissions in the United States, numerous readmissions risk prediction models have been proposed, mostly developed through analyses of structured data fields in electronic medical records and administrative databases. Three areas that may have an impact on readmission but are poorly captured using structured data sources are patients' physical function, cognitive status, and psychosocial environment and support. The objective of the study was to build a discriminative model using information germane to these 3 areas to identify hospitalized patients' risk for 30-day all cause readmissions. We conducted clinician focus groups to identify language used in the clinical record regarding these 3 areas. We then created a dataset including 30,000 inpatients, 10,000 from each of 3 hospitals, and searched those records for the focus group-derived language using natural language processing. A 30-day readmission prediction model was developed on 75% of the dataset and validated on the other 25% and also on hospital specific subsets. Focus group language was aggregated into 35 variables. The final model had 16 variables, a validated C-statistic of 0.74, and was well calibrated. Subset validation of the model by hospital yielded C-statistics of 0.70-0.75. Deriving a 30-day readmission risk prediction model through identification of physical, cognitive, and psychosocial issues using natural language processing yielded a model that performs similarly to the better performing models previously published with the added advantage of being based on clinically relevant factors and also automated and scalable. Because of the clinical relevance of the variables in the model, future research may be able to test if targeting interventions to identified risks results in reductions in readmissions.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

EAARL topography-Potato Creek watershed, Georgia, 2010

USGS Publications Warehouse

Bonisteel-Cormier, J.M.; Nayegandhi, Amar; Fredericks, Xan; Jones, J.W.; Wright, C.W.; Brock, J.C.; Nagle, D.B.

2011-01-01

This DVD contains lidar-derived first-surface (FS) and bare-earth (BE) topography GIS datasets of a portion of the Potato Creek watershed in the Apalachicola-Chattahoochee-Flint River basin, Georgia. These datasets were acquired on February 27, 2010.

Does using different modern climate datasets impact pollen-based paleoclimate reconstructions in North America during the past 2,000 years

NASA Astrophysics Data System (ADS)

Ladd, Matthew; Viau, Andre

2013-04-01

Paleoclimate reconstructions rely on the accuracy of modern climate datasets for calibration of fossil records under the assumption of climate normality through time, which means that the modern climate operates in a similar manner as over the past 2,000 years. In this study, we show how using different modern climate datasets have an impact on a pollen-based reconstruction of mean temperature of the warmest month (MTWA) during the past 2,000 years for North America. The modern climate datasets used to explore this research question include the: Whitmore et al., (2005) modern climate dataset; North American Regional Reanalysis (NARR); National Center For Environmental Prediction (NCEP); European Center for Medium Range Weather Forecasting (ECMWF) ERA-40 reanalysis; WorldClim, Global Historical Climate Network (GHCN) and New et al., which is derived from the CRU dataset. Results show that some caution is advised in using the reanalysis data on large-scale reconstructions. Station data appears to dampen out the variability of the reconstruction produced using station based datasets. The reanalysis or model-based datasets are not recommended for paleoclimate large-scale North American reconstructions as they appear to lack some of the dynamics observed in station datasets (CRU) which resulted in warm-biased reconstructions as compared to the station-based reconstructions. The Whitmore et al. (2005) modern climate dataset appears to be a compromise between CRU-based datasets and model-based datasets except for the ERA-40. In addition, an ultra-high resolution gridded climate dataset such as WorldClim may only be useful if the pollen calibration sites in North America have at least the same spatial precision. We reconstruct the MTWA to within +/-0.01°C by using an average of all curves derived from the different modern climate datasets, demonstrating the robustness of the procedure used. It may be that the use of an average of different modern datasets may reduce the impact of uncertainty of paleoclimate reconstructions, however, this is yet to be determined with certainty. Future evaluation using for example the newly developed Berkeley earth surface temperature datasets should be tested against the paleoclimate record.

clearScience: Infrastructure for Communicating Data-Intensive Science.

PubMed

Bot, Brian M; Burdick, David; Kellen, Michael; Huang, Erich S

2013-01-01

Progress in biomedical research requires effective scientific communication to one's peers and to the public. Current research routinely encompasses large datasets and complex analytic processes, and the constraints of traditional journal formats limit useful transmission of these elements. We are constructing a framework through which authors can not only provide the narrative of what was done, but the primary and derivative data, the source code, the compute environment, and web-accessible virtual machines. This infrastructure allows authors to "hand their machine"- prepopulated with libraries, data, and code-to those interested in reviewing or building off of their work. This project, "clearScience," seeks to provide an integrated system that accommodates the ad hoc nature of discovery in the data-intensive sciences and seamless transitions from working to reporting. We demonstrate that rather than merely describing the science being reported, one can deliver the science itself.

Algorithmic vs. finite difference Jacobians for infrared atmospheric radiative transfer

NASA Astrophysics Data System (ADS)

Schreier, Franz; Gimeno García, Sebastián; Vasquez, Mayte; Xu, Jian

2015-10-01

Jacobians, i.e. partial derivatives of the radiance and transmission spectrum with respect to the atmospheric state parameters to be retrieved from remote sensing observations, are important for the iterative solution of the nonlinear inverse problem. Finite difference Jacobians are easy to implement, but computationally expensive and possibly of dubious quality; on the other hand, analytical Jacobians are accurate and efficient, but the implementation can be quite demanding. GARLIC, our "Generic Atmospheric Radiation Line-by-line Infrared Code", utilizes algorithmic differentiation (AD) techniques to implement derivatives w.r.t. atmospheric temperature and molecular concentrations. In this paper, we describe our approach for differentiation of the high resolution infrared and microwave spectra and provide an in-depth assessment of finite difference approximations using "exact" AD Jacobians as a reference. The results indicate that the "standard" two-point finite differences with 1 K and 1% perturbation for temperature and volume mixing ratio, respectively, can exhibit substantial errors, and central differences are significantly better. However, these deviations do not transfer into the truncated singular value decomposition solution of a least squares problem. Nevertheless, AD Jacobians are clearly recommended because of the superior speed and accuracy.

Measurement of δ13C values of soil amino acids by GC-C-IRMS using trimethylsilylation: a critical assessment.

PubMed

Rubino, Mauro; Milin, Sylvie; D'Onofrio, Antonio; Signoret, Patrick; Hatté, Christine; Balesdent, Jérôme

2014-01-01

In this study, we evaluated trimethylsilyl (TMS) derivatives as derivatization reagents for the compound-specific stable carbon isotope analysis of soil amino acids by gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). We used non-proteinogenic amino acids to show that the extraction-derivatization-analysis procedure provides a reliable method to measure δ(13)C values of amino acids extracted from soil. However, we found a number of drawbacks that significantly increase the final total uncertainty. These include the following: production of multiple peaks for each amino acid, identified as di-, tri- and tetra-TMS derivatives; a number of TMS-carbon (TMS-C) atoms added lower than the stoichiometric one, possibly due to incomplete combustion; different TMS-C δ(13)C for di-, tri- and tetra-TMS derivatives. For soil samples, only four amino acids (leucine, valine, threonine and serine) provide reliable δ(13)C values with a total average uncertainty of 1.3 ‰. We conclude that trimethylsilyl derivatives are only suitable for determining the (13)C incorporation in amino acids within experiments using (13)C-labelled tracers but cannot be applied for amino acids with natural carbon isotope abundance until the drawbacks described here are overcome and the measured total uncertainty significantly decreased.

Substituent and ring effects on enthalpies of formation: 2-methyl- and 2-ethylbenzimidazoles versus benzene- and imidazole-derivatives

NASA Astrophysics Data System (ADS)

Jiménez, Pilar; Roux, María Victoria; Dávalos, Juan Z.; Temprado, Manuel; Ribeiro da Silva, Manuel A. V.; Ribeiro da Silva, Maria Das Dores M. C.; Amaral, Luísa M. P. F.; Cabildo, Pilar; Claramunt, Rosa M.; Mó, Otilia; Yáñez, Manuel; Elguero, José

The enthalpies of combustion, heat capacities, enthalpies of sublimation and enthalpies of formation of 2-methylbenzimidazole (2MeBIM) and 2-ethylbenzimidazole (2EtBIM) are reported and the results compared with those of benzimidazole itself (BIM). Theoretical estimates of the enthalpies of formation were obtained through the use of atom equivalent schemes. The necessary energies were obtained in single-point calculations at the B3LYP/6-311+G(d,p) on B3LYP/6-31G* optimized geometries. The comparison of experimental and calculated values of benzenes, imidazoles and benzimidazoles bearing H (unsubstituted), methyl and ethyl groups shows remarkable homogeneity. The energetic group contribution transferability is not followed, but either using it or adding an empirical interaction term, it is possible to generate an enormous collection of reasonably accurate data for different substituted heterocycles (pyrazole-derivatives, pyridine-derivatives, etc.) from the large amount of values available for substituted benzenes and those of the parent (pyrazole, pyridine) heterocycles.

An effective approach for gap-filling continental scale remotely sensed time-series

PubMed Central

Weiss, Daniel J.; Atkinson, Peter M.; Bhatt, Samir; Mappin, Bonnie; Hay, Simon I.; Gething, Peter W.

2014-01-01

The archives of imagery and modeled data products derived from remote sensing programs with high temporal resolution provide powerful resources for characterizing inter- and intra-annual environmental dynamics. The impressive depth of available time-series from such missions (e.g., MODIS and AVHRR) affords new opportunities for improving data usability by leveraging spatial and temporal information inherent to longitudinal geospatial datasets. In this research we develop an approach for filling gaps in imagery time-series that result primarily from cloud cover, which is particularly problematic in forested equatorial regions. Our approach consists of two, complementary gap-filling algorithms and a variety of run-time options that allow users to balance competing demands of model accuracy and processing time. We applied the gap-filling methodology to MODIS Enhanced Vegetation Index (EVI) and daytime and nighttime Land Surface Temperature (LST) datasets for the African continent for 2000–2012, with a 1 km spatial resolution, and an 8-day temporal resolution. We validated the method by introducing and filling artificial gaps, and then comparing the original data with model predictions. Our approach achieved R2 values above 0.87 even for pixels within 500 km wide introduced gaps. Furthermore, the structure of our approach allows estimation of the error associated with each gap-filled pixel based on the distance to the non-gap pixels used to model its fill value, thus providing a mechanism for including uncertainty associated with the gap-filling process in downstream applications of the resulting datasets. PMID:25642100

Cross-Platform Toxicogenomics for the Prediction of Non-Genotoxic Hepatocarcinogenesis in Rat

PubMed Central

Metzger, Ute; Templin, Markus F.; Plummer, Simon; Ellinger-Ziegelbauer, Heidrun; Zell, Andreas

2014-01-01

In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens. PMID:24830643

CoINcIDE: A framework for discovery of patient subtypes across multiple datasets.

PubMed

Planey, Catherine R; Gevaert, Olivier

2016-03-09

Patient disease subtypes have the potential to transform personalized medicine. However, many patient subtypes derived from unsupervised clustering analyses on high-dimensional datasets are not replicable across multiple datasets, limiting their clinical utility. We present CoINcIDE, a novel methodological framework for the discovery of patient subtypes across multiple datasets that requires no between-dataset transformations. We also present a high-quality database collection, curatedBreastData, with over 2,500 breast cancer gene expression samples. We use CoINcIDE to discover novel breast and ovarian cancer subtypes with prognostic significance and novel hypothesized ovarian therapeutic targets across multiple datasets. CoINcIDE and curatedBreastData are available as R packages.

Heart rate time series characteristics for early detection of infections in critically ill patients.

PubMed

Tambuyzer, T; Guiza, F; Boonen, E; Meersseman, P; Vervenne, H; Hansen, T K; Bjerre, M; Van den Berghe, G; Berckmans, D; Aerts, J M; Meyfroidt, G

2017-04-01

It is difficult to make a distinction between inflammation and infection. Therefore, new strategies are required to allow accurate detection of infection. Here, we hypothesize that we can distinguish infected from non-infected ICU patients based on dynamic features of serum cytokine concentrations and heart rate time series. Serum cytokine profiles and heart rate time series of 39 patients were available for this study. The serum concentration of ten cytokines were measured using blood sampled every 10 min between 2100 and 0600 hours. Heart rate was recorded every minute. Ten metrics were used to extract features from these time series to obtain an accurate classification of infected patients. The predictive power of the metrics derived from the heart rate time series was investigated using decision tree analysis. Finally, logistic regression methods were used to examine whether classification performance improved with inclusion of features derived from the cytokine time series. The AUC of a decision tree based on two heart rate features was 0.88. The model had good calibration with 0.09 Hosmer-Lemeshow p value. There was no significant additional value of adding static cytokine levels or cytokine time series information to the generated decision tree model. The results suggest that heart rate is a better marker for infection than information captured by cytokine time series when the exact stage of infection is not known. The predictive value of (expensive) biomarkers should always be weighed against the routinely monitored data, and such biomarkers have to demonstrate added value.

Method applied to the background analysis of energy data to be considered for the European Reference Life Cycle Database (ELCD).

PubMed

Fazio, Simone; Garraín, Daniel; Mathieux, Fabrice; De la Rúa, Cristina; Recchioni, Marco; Lechón, Yolanda

2015-01-01

Under the framework of the European Platform on Life Cycle Assessment, the European Reference Life-Cycle Database (ELCD - developed by the Joint Research Centre of the European Commission), provides core Life Cycle Inventory (LCI) data from front-running EU-level business associations and other sources. The ELCD contains energy-related data on power and fuels. This study describes the methods to be used for the quality analysis of energy data for European markets (available in third-party LC databases and from authoritative sources) that are, or could be, used in the context of the ELCD. The methodology was developed and tested on the energy datasets most relevant for the EU context, derived from GaBi (the reference database used to derive datasets for the ELCD), Ecoinvent, E3 and Gemis. The criteria for the database selection were based on the availability of EU-related data, the inclusion of comprehensive datasets on energy products and services, and the general approval of the LCA community. The proposed approach was based on the quality indicators developed within the International Reference Life Cycle Data System (ILCD) Handbook, further refined to facilitate their use in the analysis of energy systems. The overall Data Quality Rating (DQR) of the energy datasets can be calculated by summing up the quality rating (ranging from 1 to 5, where 1 represents very good, and 5 very poor quality) of each of the quality criteria indicators, divided by the total number of indicators considered. The quality of each dataset can be estimated for each indicator, and then compared with the different databases/sources. The results can be used to highlight the weaknesses of each dataset and can be used to guide further improvements to enhance the data quality with regard to the established criteria. This paper describes the application of the methodology to two exemplary datasets, in order to show the potential of the methodological approach. The analysis helps LCA practitioners to evaluate the usefulness of the ELCD datasets for their purposes, and dataset developers and reviewers to derive information that will help improve the overall DQR of databases.

In-situ databases and comparison of ESA Ocean Colour Climate Change Initiative (OC-CCI) products with precursor data, towards an integrated approach for ocean colour validation and climate studies

NASA Astrophysics Data System (ADS)

Brotas, Vanda; Valente, André; Couto, André B.; Grant, Mike; Chuprin, Andrei; Jackson, Thomas; Groom, Steve; Sathyendranath, Shubha

2014-05-01

Ocean colour (OC) is an Oceanic Essential Climate Variable, which is used by climate modellers and researchers. The European Space Agency (ESA) Climate Change Initiative project, is the ESA response for the need of climate-quality satellite data, with the goal of providing stable, long-term, satellite-based ECV data products. The ESA Ocean Colour CCI focuses on the production of Ocean Colour ECV uses remote sensing reflectances to derive inherent optical properties and chlorophyll a concentration from ESA's MERIS (2002-2012) and NASA's SeaWiFS (1997 - 2010) and MODIS (2002-2012) sensor archives. This work presents an integrated approach by setting up a global database of in situ measurements and by inter-comparing OC-CCI products with pre-cursor datasets. The availability of in situ databases is fundamental for the validation of satellite derived ocean colour products. A global distribution in situ database was assembled, from several pre-existing datasets, with data spanning between 1997 and 2012. It includes in-situ measurements of remote sensing reflectances, concentration of chlorophyll-a, inherent optical properties and diffuse attenuation coefficient. The database is composed from observations of the following datasets: NOMAD, SeaBASS, MERMAID, AERONET-OC, BOUSSOLE and HOTS. The result was a merged dataset tuned for the validation of satellite-derived ocean colour products. This was an attempt to gather, homogenize and merge, a large high-quality bio-optical marine in situ data, as using all datasets in a single validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. An inter-comparison analysis between OC-CCI chlorophyll-a product and satellite pre-cursor datasets was done with single missions and merged single mission products. Single mission datasets considered were SeaWiFS, MODIS-Aqua and MERIS; merged mission datasets were obtained from the GlobColour (GC) as well as the Making Earth Science Data Records for Use in Research Environments (MEaSUREs). OC-CCI product was found to be most similar to SeaWiFS record, and generally, the OC-CCI record was most similar to records derived from single mission than merged mission initiatives. Results suggest that CCI product is a more consistent dataset than other available merged mission initiatives. In conclusion, climate related science, requires long term data records to provide robust results, OC-CCI product proves to be a worthy data record for climate research, as it combines multi-sensor OC observations to provide a >15-year global error-characterized record.

Temporal Stability of the NDVI-LAI Relationship in a Napa Valley Vineyard

NASA Technical Reports Server (NTRS)

Johnson, L. F.

2003-01-01

Remotely sensed normalized difference vegetation index (NDVI) values, derived from high-resolution satellite images, were compared with ground measurements of vineyard leaf area index (LAI) periodically during the 2001 growing season. The two variables were strongly related at six ground calibration sites on each of four occasions (r squared = 0.91 to 0.98). Linear regression equations relating the two variables did not significantly differ by observation date, and a single equation accounted for 92 percent of the variance in the combined dataset. Temporal stability of the relationship opens the possibility of transforming NDVI maps to LAI in the absence of repeated ground calibration fieldwork. In order to take advantage of this circumstance, however, steps should be taken to assure temporal consistency in spectral data values comprising the NDVI.

GRACE-Based Analysis of Total Water Storage Trends and Groundwater Fluctuations in the North-Western Sahara Aquifer System (NWSAS) and Tindouf Aquifer in Northwest Africa

NASA Astrophysics Data System (ADS)

Lezzaik, K. A.; Milewski, A.

2013-12-01

Optimal water management practices and strategies, in arid and semi-arid environments, are often hindered by a lack of quantitative and qualitative understanding of hydrological processes. Moreover, progressive overexploitation of groundwater resources to meet agricultural, industrial, and domestic requirements is drawing concern over the sustainability of such exhaustive abstraction levels, especially in environments where groundwater is a major source of water. NASA's GRACE (gravity recovery and climate change experiment) mission, since March 2002, has advanced the understanding of hydrological events, especially groundwater depletion, through integrated measurements and modeling of terrestrial water mass. In this study, GLDAS variables (rainfall rate, evapotranspiration rate, average soil moisture), and TRMM 3B42.V7A precipitation satellite data, were used in combination with 95 GRACE-generated gravitational anomalies maps, to quantify total water storage change (TWSC) and groundwater storage change (GWSC) from January 2003 to December 2010 (excluding June 2003), in the North-Western Sahara Aquifer System (NWSAS) and Tindouf Aquifer System in northwestern Africa. Separately processed and computed GRACE products by JPL (Jet Propulsion Laboratory, NASA), CSR (Center of Space Research, UT Austin), and GFZ (German Research Centre for Geoscience, Potsdam), were used to determine which GRACE dataset(s) best reflect total water storage and ground water changes in northwest Africa. First-order estimates of annual TWSC for NWSAS (JPL: +5.297 BCM; CSR: -5.33 BCM; GFZ: -9.96 BCM) and Tindouf Aquifer System (JPL: +1.217 BCM; CSR: +0.203 BCM; GFZ: +1.019 BCM), were computed using zonal averaging over a span of eight years. Preliminary findings of annual GWSC for NWSAS (JPL: +2.45 BCM; CSR: -2.278 BCM; GFZ: -6.913 BCM) and Tindouf Aquifer System (JPL: +1.108 BCM; CSR: +0.094 BCM; GFZ: +0.910 BCM), were calculating using a water budget approach, parameterized by GLDAS-derived soil moisture and evapotranspiration values with GRACE-based TWSC. Initial results suggest CSR-processed datasets as being most representative of TWSC/GWSC values in the NWSAS, given groundwater abstraction estimates of 2.5 BCM/year, a conservative estimate considering it does not include unaccounted abstractions or increased consumption in recent years. Conversely, high abstraction rates and negligibly low recharge rates indicate the positive TWSC/GWSC values generated from JPL-processed datasets are not accurately representative of hydrologic changes in NWSAS. Consistently positive TWSC/GWSC values for the Tindouf Aquifer System, by JPL, CSR, and GFZ datasets are indicative of sustainable groundwater abstraction levels (recharge rate > abstraction rate). GWSC time series, computed for each of the three different processed datasets (JPL, CSR, GFZ), account for significant withdrawals in groundwater in both NWSAS (February 2006 and from August 2008 to January 2009) and Tindouf Aquifer System (November/October 2003, February/March 2006, and September/October 2010).

Understanding patient outcomes after acute respiratory distress syndrome: identifying subtypes of physical, cognitive and mental health outcomes.

PubMed

Brown, Samuel M; Wilson, Emily L; Presson, Angela P; Dinglas, Victor D; Greene, Tom; Hopkins, Ramona O; Needham, Dale M

2017-12-01

With improving short-term mortality in acute respiratory distress syndrome (ARDS), understanding survivors' posthospitalisation outcomes is increasingly important. However, little is known regarding associations among physical, cognitive and mental health outcomes. Identification of outcome subtypes may advance understanding of post-ARDS morbidities. We analysed baseline variables and 6-month health status for participants in the ARDS Network Long-Term Outcomes Study. After division into derivation and validation datasets, we used weighted network analysis to identify subtypes from predictors and outcomes in the derivation dataset. We then used recursive partitioning to develop a subtype classification rule and assessed adequacy of the classification rule using a kappa statistic with the validation dataset. Among 645 ARDS survivors, 430 were in the derivation and 215 in the validation datasets. Physical and mental health status, but not cognitive status, were closely associated. Four distinct subtypes were apparent (percentages in the derivation cohort): (1) mildly impaired physical and mental health (22% of patients), (2) moderately impaired physical and mental health (39%), (3) severely impaired physical health with moderately impaired mental health (15%) and (4) severely impaired physical and mental health (24%). The classification rule had high agreement (kappa=0.89 in validation dataset). Female Latino smokers had the poorest status, while male, non-Latino non-smokers had the best status. We identified four post-ARDS outcome subtypes that were predicted by sex, ethnicity, pre-ARDS smoking status and other baseline factors. These subtypes may help develop tailored rehabilitation strategies, including investigation of combined physical and mental health interventions, and distinct interventions to improve cognitive outcomes. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Joining Forces for Food Security - Linking Earth Observation and Crowd-sourcing for improved Decision-support

NASA Astrophysics Data System (ADS)

Enenkel, M.; Dorigo, W.; See, L. M.; Vinck, P.; Papp, A.

2014-12-01

Droughts statistically exceed all other natural disasters in complexity, spatio-temporal extent and number of people affected. Triggered by crop failure, food insecurity is a major manifestation of agricultural drought and water scarcity. However, other socio-economic precursors, such as chronically low levels of disaster preparedness, hampered access to food security or a lack of social safety nets are equally important factors. We will present the first results of the SATIDA (Satellite Technologies for Improved Drought-Risk Assessment) project, which advances three complementary developments. First, an existing drought indicator is enhanced by replacing in-situ measurements on rainfall and surface air temperature with satellite-derived datasets. We identify the vegetation status via a new noise-corrected and gap-filled vegetation index. In addition, we introduce a soil moisture component to close the gap between rainfall deficiencies, extreme temperature and the first visible impacts of atmospheric anomalies on vegetation. Second, once calibrated, the index is forced with seasonal forecasts to quantify their uncertainty and added value in the regions of interest. Third, a mobile application is developed to disseminate relevant visualizations to decision-makers in affected areas, to collect additional information about socio-economic conditions and to validate the output of the drought index in real conditions. Involving Doctors without Borders (MSF) as a key user, SATIDA aims at decreasing uncertainties in decision-making via a more holistic risk framework, resulting in longer lead times for disaster logistics in the preparedness phase.

Establishing a threshold for the number of missing days using 7 d pedometer data.

PubMed

Kang, Minsoo; Hart, Peter D; Kim, Youngdeok

2012-11-01

The purpose of this study was to examine the threshold of the number of missing days of recovery using the individual information (II)-centered approach. Data for this study came from 86 participants, aged from 17 to 79 years old, who had 7 consecutive days of complete pedometer (Yamax SW 200) wear. Missing datasets (1 d through 5 d missing) were created by a SAS random process 10,000 times each. All missing values were replaced using the II-centered approach. A 7 d average was calculated for each dataset, including the complete dataset. Repeated measure ANOVA was used to determine the differences between 1 d through 5 d missing datasets and the complete dataset. Mean absolute percentage error (MAPE) was also computed. Mean (SD) daily step count for the complete 7 d dataset was 7979 (3084). Mean (SD) values for the 1 d through 5 d missing datasets were 8072 (3218), 8066 (3109), 7968 (3273), 7741 (3050) and 8314 (3529), respectively (p > 0.05). The lower MAPEs were estimated for 1 d missing (5.2%, 95% confidence interval (CI) 4.4-6.0) and 2 d missing (8.4%, 95% CI 7.0-9.8), while all others were greater than 10%. The results of this study show that the 1 d through 5 d missing datasets, with replaced values, were not significantly different from the complete dataset. Based on the MAPE results, it is not recommended to replace more than two days of missing step counts.

Added clinical value of applying myocardial deformation imaging to assess right ventricular function.

PubMed

Sokalskis, Vladislavs; Peluso, Diletta; Jagodzinski, Annika; Sinning, Christoph

2017-06-01

Right heart dysfunction has been found to be a strong prognostic factor predicting adverse outcome in various cardiopulmonary diseases. Conventional echocardiographic measurements can be limited by geometrical assumptions and impaired reproducibility. Speckle tracking-derived strain provides a robust quantification of right ventricular function. It explicitly evaluates myocardial deformation, as opposed to tissue Doppler-derived strain, which is computed from tissue velocity gradients. Right ventricular longitudinal strain provides a sensitive tool for detecting right ventricular dysfunction, even at subclinical levels. Moreover, the longitudinal strain can be applied for prognostic stratification of patients with pulmonary hypertension, pulmonary embolism, and congestive heart failure. Speckle tracking-derived right atrial strain, right ventricular longitudinal strain-derived mechanical dyssynchrony, and three-dimensional echocardiography-derived strain are emerging imaging parameters and methods. Their application in research is paving the way for their clinical use. © 2017, Wiley Periodicals, Inc.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

PubMed

Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

2016-01-01

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Combination of image descriptors for the exploration of cultural photographic collections

NASA Astrophysics Data System (ADS)

Bhowmik, Neelanjan; Gouet-Brunet, Valérie; Bloch, Gabriel; Besson, Sylvain

2017-01-01

The rapid growth of image digitization and collections in recent years makes it challenging and burdensome to organize, categorize, and retrieve similar images from voluminous collections. Content-based image retrieval (CBIR) is immensely convenient in this context. A considerable number of local feature detectors and descriptors are present in the literature of CBIR. We propose a model to anticipate the best feature combinations for image retrieval-related applications. Several spatial complementarity criteria of local feature detectors are analyzed and then engaged in a regression framework to find the optimal combination of detectors for a given dataset and are better adapted for each given image; the proposed model is also useful to optimally fix some other parameters, such as the k in k-nearest neighbor retrieval. Three public datasets of various contents and sizes are employed to evaluate the proposal, which is legitimized by improving the quality of retrieval notably facing classical approaches. Finally, the proposed image search engine is applied to the cultural photographic collections of a French museum, where it demonstrates its added value for the exploration and promotion of these contents at different levels from their archiving up to their exhibition in or ex situ.

Targeted and non-targeted detection of lemon juice adulteration by LC-MS and chemometrics.

PubMed

Wang, Zhengfang; Jablonski, Joseph E

2016-01-01

Economically motivated adulteration (EMA) of lemon juice was detected by LC-MS and principal component analysis (PCA). Twenty-two batches of freshly squeezed lemon juice were adulterated by adding an aqueous solution containing 5% citric acid and 6% sucrose to pure lemon juice to obtain 30%, 60% and 100% lemon juice samples. Their total titratable acidities, °Brix and pH values were measured, and then all the lemon juice samples were subject to LC-MS analysis. Concentrations of hesperidin and eriocitrin, major phenolic components of lemon juice, were quantified. The PCA score plots for LC-MS datasets were used to preview the classification of pure and adulterated lemon juice samples. Results showed a large inherent variability in the chemical properties among 22 batches of 100% lemon juice samples. Measurement or quantitation of one or several chemical properties (targeted detection) was not effective in detecting lemon juice adulteration. However, by using the LC-MS datasets, including both chromatographic and mass spectrometric information, 100% lemon juice samples were successfully differentiated from adulterated samples containing 30% lemon juice in the PCA score plot. LC-MS coupled with chemometric analysis can be a complement to existing methods for detecting juice adulteration.