Effective classification of the prevalence of Schistosoma mansoni.

PubMed

Mitchell, Shira A; Pagano, Marcello

2012-12-01

To present an effective classification method based on the prevalence of Schistosoma mansoni in the community. We created decision rules (defined by cut-offs for number of positive slides), which account for imperfect sensitivity, both with a simple adjustment of fixed sensitivity and with a more complex adjustment of changing sensitivity with prevalence. To reduce screening costs while maintaining accuracy, we propose a pooled classification method. To estimate sensitivity, we use the De Vlas model for worm and egg distributions. We compare the proposed method with the standard method to investigate differences in efficiency, measured by number of slides read, and accuracy, measured by probability of correct classification. Modelling varying sensitivity lowers the lower cut-off more significantly than the upper cut-off, correctly classifying regions as moderate rather than lower, thus receiving life-saving treatment. The classification method goes directly to classification on the basis of positive pools, avoiding having to know sensitivity to estimate prevalence. For model parameter values describing worm and egg distributions among children, the pooled method with 25 slides achieves an expected 89.9% probability of correct classification, whereas the standard method with 50 slides achieves 88.7%. Among children, it is more efficient and more accurate to use the pooled method for classification of S. mansoni prevalence than the current standard method. © 2012 Blackwell Publishing Ltd.

Effects of two classification strategies on a Benthic Community Index for streams in the Northern Lakes and Forests Ecoregion

USGS Publications Warehouse

Butcher, Jason T.; Stewart, Paul M.; Simon, Thomas P.

2003-01-01

Ninety-four sites were used to analyze the effects of two different classification strategies on the Benthic Community Index (BCI). The first, a priori classification, reflected the wetland status of the streams; the second, a posteriori classification, used a bio-environmental analysis to select classification variables. Both classifications were examined by measuring classification strength and testing differences in metric values with respect to group membership. The a priori (wetland) classification strength (83.3%) was greater than the a posteriori (bio-environmental) classification strength (76.8%). Both classifications found one metric that had significant differences between groups. The original index was modified to reflect the wetland classification by re-calibrating the scoring criteria for percent Crustacea and Mollusca. A proposed refinement to the original Benthic Community Index is suggested. This study shows the importance of using hypothesis-driven classifications, as well as exploratory statistical analysis, to evaluate alternative ways to reveal environmental variability in biological assessment tools.

Ecosystem classifications based on summer and winter conditions.

PubMed

Andrew, Margaret E; Nelson, Trisalyn A; Wulder, Michael A; Hobart, George W; Coops, Nicholas C; Farmer, Carson J Q

2013-04-01

Ecosystem classifications map an area into relatively homogenous units for environmental research, monitoring, and management. However, their effectiveness is rarely tested. Here, three classifications are (1) defined and characterized for Canada along summertime productivity (moderate-resolution imaging spectrometer fraction of absorbed photosynthetically active radiation) and wintertime snow conditions (special sensor microwave/imager snow water equivalent), independently and in combination, and (2) comparatively evaluated to determine the ability of each classification to represent the spatial and environmental patterns of alternative schemes, including the Canadian ecozone framework. All classifications depicted similar patterns across Canada, but detailed class distributions differed. Class spatial characteristics varied with environmental conditions within classifications, but were comparable between classifications. There was moderate correspondence between classifications. The strongest association was between productivity classes and ecozones. The classification along both productivity and snow balanced these two sets of variables, yielding intermediate levels of association in all pairwise comparisons. Despite relatively low spatial agreement between classifications, they successfully captured patterns of the environmental conditions underlying alternate schemes (e.g., snow classes explained variation in productivity and vice versa). The performance of ecosystem classifications and the relevance of their input variables depend on the environmental patterns and processes used for applications and evaluation. Productivity or snow regimes, as constructed here, may be desirable when summarizing patterns controlled by summer- or wintertime conditions, respectively, or of climate change responses. General purpose ecosystem classifications should include both sets of drivers. Classifications should be carefully, quantitatively, and comparatively evaluated relative to a particular application prior to their implementation as monitoring and assessment frameworks.

Use of geographic information systems for applications on gas pipeline rights-of-way. Final report, December 1989--December 1991

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

Thompson, P.J.

1991-12-01

Geographic information system (GIS) applications for the siting and monitoring of gas pipeline rights-of-way for this project (ROWs) were developed for areas near Rio Vista, California. The data layers developed for this project represent geographic features, such as landcover, elevation, aspect, slope, soils, hydrography, transportation, endangered species, wetlands, and public line surveys. A GIS was used to develop and store spatial data from several sources; to manipulate spatial data to evaluate environmental and engineering issues associated with the siting, permitting, construction, maintenance, and monitoring of gas pipeline ROWS; and to graphically display analysis results. Examples of these applications include (1)more » determination of environmentally sensitive areas, such as endangered species habitat, wetlands, and areas of highly erosive soils; (2) evaluation of engineering constraints, including shallow depth to bedrock, major hydrographic features, and shallow water table; (3) classification of satellite imagery for landuse/landcover that will affect ROWs; and (4) identification of alternative ROW corridors that avoid environmentally sensitive areas or areas with severe engineering constraints.« less

Use of geographic information systems for applications on gas pipeline rights-of-way

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

Thompson, P.J.

1991-12-01

Geographic information system (GIS) applications for the siting and monitoring of gas pipeline rights-of-way for this project (ROWs) were developed for areas near Rio Vista, California. The data layers developed for this project represent geographic features, such as landcover, elevation, aspect, slope, soils, hydrography, transportation, endangered species, wetlands, and public line surveys. A GIS was used to develop and store spatial data from several sources; to manipulate spatial data to evaluate environmental and engineering issues associated with the siting, permitting, construction, maintenance, and monitoring of gas pipeline ROWS; and to graphically display analysis results. Examples of these applications include (1)more » determination of environmentally sensitive areas, such as endangered species habitat, wetlands, and areas of highly erosive soils; (2) evaluation of engineering constraints, including shallow depth to bedrock, major hydrographic features, and shallow water table; (3) classification of satellite imagery for landuse/landcover that will affect ROWs; and (4) identification of alternative ROW corridors that avoid environmentally sensitive areas or areas with severe engineering constraints.« less

Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.

PubMed

Lu, Huijuan; Wei, Shasha; Zhou, Zili; Miao, Yanzi; Lu, Yi

2015-01-01

The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification.

PubMed

Liu, Yanqiu; Lu, Huijuan; Yan, Ke; Xia, Haixia; An, Chunlin

2016-01-01

Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we extend our previous work, that is, Dissimilar ELM (D-ELM), by introducing misclassification costs into the classifier. We name the proposed algorithm as the cost-sensitive D-ELM (CS-D-ELM). Furthermore, we embed rejection cost into the CS-D-ELM to increase the classification stability of the proposed algorithm. Experimental results show that the rejection cost embedded CS-D-ELM algorithm effectively reduces the average and overall cost of the classification process, while the classification accuracy still remains competitive. The proposed method can be extended to classification problems of other redundant and imbalanced data.

Comparison of sampling methods used for MRSA-classification of herds with breeding pigs.

PubMed

Broens, E M; Graat, E A M; Engel, B; van Oosterom, R A A; van de Giessen, A W; van der Wolf, P J

2011-01-27

Since the first report on methicillin resistant Staphylococcus aureus (MRSA) CC398 in pigs, several countries have determined the prevalence of MRSA-positive pig herds using different sampling and laboratory techniques. The objective of the study was to compare three sampling methods for MRSA-classification of herds. Therefore, nasal swabs of pigs and environmental wipes were collected from 147 herds with breeding pigs. Per herd, laboratory examination was done on 10 pools of 6 nasal swabs (NASAL), 5 single environmental wipes (ENVSINGLE) and one pool of 5 environmental wipes (ENVPOOL). Large differences in apparent prevalence of MRSA-positive herds between methods were found: 19.1% for ENVPOOL, 53.1% for ENVSINGLE, and 70.8% for NASAL. Pairwise comparisons of methods resulted in relative sensitivities of 26.9% (ENVPOOL vs. NASAL), 34.6% (ENVPOOL vs. ENVSINGLE), and 72.1% (ENVSINGLE vs. NASAL) with relative specificities of respectively 100%, 98.6% and 93.0%. Cohen's kappa was respectively 0.18, 0.32 and 0.55, thus varying between very poor and moderate agreement. Examination of environmental wipes is an easy and non-invasive method to classify herds for MRSA. The number of environmental wipes needed depends on e.g. required detection limits and within-herd prevalence. In low prevalent herds (e.g. herds with <3 positive pools of nasal swabs), 25 single environmental wipes are required to be 90% sure that MRSA is detected at a detection limit similar to analyzing 10 pools of nasal swabs. Individual analysis of environmental wipes is highly recommended, as pooling 5 environmental samples resulted in a substantial reduction of the apparent prevalence. Copyright © 2010 Elsevier B.V. All rights reserved.

Comparing performance of mothers using simplified mid-upper arm circumference (MUAC) classification devices with an improved MUAC insertion tape in Isiolo County, Kenya.

PubMed

Grant, Angeline; Njiru, James; Okoth, Edgar; Awino, Imelda; Briend, André; Murage, Samuel; Abdirahman, Saida; Myatt, Mark

2018-01-01

A novel approach for improving community case-detection of acute malnutrition involves mothers/caregivers screening their children for acute malnutrition using a mid-upper arm circumference (MUAC) insertion tape. The objective of this study was to test three simple MUAC classification devices to determine whether they improved the sensitivity of mothers/caregivers at detecting acute malnutrition. Prospective, non-randomised, partially-blinded, clinical diagnostic trial describing and comparing the performance of three "Click-MUAC" devices and a MUAC insertion tape. The study took place in twenty-one health facilities providing integrated management of acute malnutrition (IMAM) services in Isiolo County, Kenya. Mothers/caregivers classified their child ( n =1040), aged 6-59 months, using the "Click-MUAC" devices and a MUAC insertion tape. These classifications were compared to a "gold standard" classification (the mean of three measurements taken by a research assistant using the MUAC insertion tape). The sensitivity of mother/caregiver classifications was high for all devices (>93% for severe acute malnutrition (SAM), defined by MUAC < 115 mm, and > 90% for global acute malnutrition (GAM), defined by MUAC < 125 mm). Mother/caregiver sensitivity for SAM and GAM classification was higher using the MUAC insertion tape (100% sensitivity for SAM and 99% sensitivity for GAM) than using "Click-MUAC" devices. Younden's J for SAM classification, and sensitivity for GAM classification, were significantly higher for the MUAC insertion tape (99% and 99% respectively). Specificity was high for all devices (>96%) with no significant difference between the "Click-MUAC" devices and the MUAC insertion tape. The results of this study indicate that, although the "Click-MUAC" devices performed well, the MUAC insertion tape performed best. The results for sensitivity are higher than found in previous studies. The high sensitivity for both SAM and GAM classification by mothers/caregivers with the MUAC insertion tape could be due to the use of an improved MUAC tape design which has a number of new design features. The one-on-one demonstration provided to mothers/caregivers on the use of the devices may also have helped improve sensitivity. The results of this study provide evidence that mothers/caregivers can perform sensitive and specific classifications of their child's nutritional status using MUAC. Clinical trials registration number: NCT02833740.

Coupling Self-Organizing Maps with a Naïve Bayesian classifier: A case study for classifying Vermont streams using geomorphic, habitat and biological assessment data

NASA Astrophysics Data System (ADS)

Fytilis, N.; Rizzo, D. M.

2012-12-01

Environmental managers are increasingly required to forecast the long-term effects and the resilience or vulnerability of biophysical systems to human-generated stresses. Mitigation strategies for hydrological and environmental systems need to be assessed in the presence of uncertainty. An important aspect of such complex systems is the assessment of variable uncertainty on the model response outputs. We develop a new classification tool that couples a Naïve Bayesian Classifier with a modified Kohonen Self-Organizing Map to tackle this challenge. For proof-of-concept, we use rapid geomorphic and reach-scale habitat assessments data from over 2500 Vermont stream reaches (~1371 stream miles) assessed by the Vermont Agency of Natural Resources (VTANR). In addition, the Vermont Department of Environmental Conservation (VTDEC) estimates stream habitat biodiversity indices (macro-invertebrates and fish) and a variety of water quality data. Our approach fully utilizes the existing VTANR and VTDEC data sets to improve classification of stream-reach habitat and biological integrity. The combined SOM-Naïve Bayesian architecture is sufficiently flexible to allow for continual updates and increased accuracy associated with acquiring new data. The Kohonen Self-Organizing Map (SOM) is an unsupervised artificial neural network that autonomously analyzes properties inherent in a given a set of data. It is typically used to cluster data vectors into similar categories when a priori classes do not exist. The ability of the SOM to convert nonlinear, high dimensional data to some user-defined lower dimension and mine large amounts of data types (i.e., discrete or continuous, biological or geomorphic data) makes it ideal for characterizing the sensitivity of river networks in a variety of contexts. The procedure is data-driven, and therefore does not require the development of site-specific, process-based classification stream models, or sets of if-then-else rules associated with expert systems. This has the potential to save time and resources, while enabling a truly adaptive management approach using existing knowledge (expressed as prior probabilities) and new information (expressed as likelihood functions) to update estimates (i.e., in this case, improved stream classifications expressed as posterior probabilities). The distribution parameters of these posterior probabilities are used to quantify uncertainty associated with environmental data. Since classification plays a leading role in the future development of data-enabled science and engineering, such a computational tool is applicable to a variety of engineering applications. The ability of the new classification neural network to characterize streams with high environmental risk is essential for a proactive adaptive watershed management approach.

CLASSIFICATION FRAMEWORK FOR COASTAL SYSTEMS

EPA Science Inventory

U.S. Environmental Protection Agency. Classification Framework for Coastal Systems. EPA/600/R-04/061. U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division, Narragansett, RI, Gulf Ecology Division, Gulf Bree...

Use of geographic information systems for applications on gas pipeline rights-of-way

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

Sydelko, P.J.; Wilkey, P.L.

1992-12-01

Geographic information system (GIS) applications for the siting and monitoring of gas pipeline rights-of-way (ROWS) were developed for areas near Rio Vista, California. The data layers developed for this project represent geographic features, such as landcover, elevation, aspect, slope, soils, hydrography, transportation, endangered species, wetlands, and public line surveys. A GIS was used to develop and store spatial data from several sources; to manipulate spatial data to evaluate environmental and engineering issues associated with the siting, permitting, construction, maintenance, and monitoring of gas pipeline ROWS; and to graphically display analysis results. Examples of these applications include (1) determination of environmentallymore » sensitive areas, such as endangered species habitat, wetlands, and areas of highly erosive soils; (2) evaluation of engineering constraints, including shallow depth to bedrock, major hydrographic features, and shallow water table; (3) classification of satellite imagery for landuse/landcover that will affect ROWS; and (4) identification of alternative ROW corridors that avoid environmentally sensitive areas or areas with severe engineering constraints.« less

GIS least-cost analysis approach for siting gas pipeline ROWs

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

Sydelko, P.J.; Wilkey, P.L.

1994-09-01

Geographic-information-system applications for the siting and monitoring of gas pipeline rights-of-way (ROWS) were developed for areas near Rio Vista, California. The data layers developed for this project represent geographic features, such as landcover, elevation, aspect, slope, soils, hydrography, transportation corridors, endangered species habitats, wetlands, and public line surveys. A geographic information system was used to develop and store spatial data from several sources; to manipulate spatial data to evaluate environmental and engineering issues associated with the siting, permitting, construction, maintenance, and monitoring of gas-pipeline ROWS; and to graphically display analysis results. Examples of these applications include (1) determination of environmentallymore » sensitive areas, such as endangered species habitat, wetlands, and areas of highly erosive soils; (2) evaluation of engineering constraints, including shallow depth to bedrock, major hydrographic features, and shallow water table; (3) classification of satellite imagery for landuse/landcover that will affect ROWS; and (4) identification of alternative ROW corridors that avoid environmentally sensitive areas or areas with severe engineering constraints.« less

Use of geographic information systems for applications on gas pipeline rights-of-way

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

Sydelko, P.J.

1993-10-01

Geographic information system (GIS) applications for the siting and monitoring of gas pipeline rights-of-way (ROWS) were developed for areas near Rio Vista, California. The data layers developed for this project represent geographic features, such as landcover, elevation, aspect, slope, soils, hydrography, transportation, endangered species, wetlands, and public line surveys. A GIS was used to develop and store spatial data from several sources; to manipulate spatial data to evaluate environmental and engineering issues associated with the siting, permitting, construction, maintenance, and monitoring of gas pipeline ROWS; and to graphically display analysis results. Examples of these applications include (1) determination of environmentallymore » sensitive areas, such as endangered species habitat, wetlands, and areas of highly erosive soils; (2) evaluation of engineering constraints, including shallow depth to bedrock, major hydrographic features, and shallow water table; (3) classification of satellite imagery for land use/landcover that will affect ROWS; and (4) identification of alternative ROW corridors that avoid environmentally sensitive areas or areas with severe engineering constraints.« less

Use of geographic information systems for applications on gas pipeline rights-of-way

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

Sydelko, P.J.; Wilkey, P.L.

1992-01-01

Geographic information system (GIS) applications for the siting and monitoring of gas pipeline rights-of-way (ROWS) were developed for areas near Rio Vista, California. The data layers developed for this project represent geographic features, such as landcover, elevation, aspect, slope, soils, hydrography, transportation, endangered species, wetlands, and public line surveys. A GIS was used to develop and store spatial data from several sources; to manipulate spatial data to evaluate environmental and engineering issues associated with the siting, permitting, construction, maintenance, and monitoring of gas pipeline ROWS; and to graphically display analysis results. Examples of these applications include (1) determination of environmentallymore » sensitive areas, such as endangered species habitat, wetlands, and areas of highly erosive soils; (2) evaluation of engineering constraints, including shallow depth to bedrock, major hydrographic features, and shallow water table; (3) classification of satellite imagery for landuse/landcover that will affect ROWS; and (4) identification of alternative ROW corridors that avoid environmentally sensitive areas or areas with severe engineering constraints.« less

75 FR 47897 - Proposed Collection; Comment Request for Regulation Project

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-09

...-Classification (Section 301.7701-4). DATES: Written comments should be received on or before October 8, 2010 to...: Title: Environmental Settlement Funds-Classification. OMB Number: 1545-1465. Regulation Project Number... classification of trusts formed to collect and disburse amounts for environmental remediation of an existing...

Dynamic species classification of microorganisms across time, abiotic and biotic environments—A sliding window approach

PubMed Central

Griffiths, Jason I.; Fronhofer, Emanuel A.; Garnier, Aurélie; Seymour, Mathew; Altermatt, Florian; Petchey, Owen L.

2017-01-01

The development of video-based monitoring methods allows for rapid, dynamic and accurate monitoring of individuals or communities, compared to slower traditional methods, with far reaching ecological and evolutionary applications. Large amounts of data are generated using video-based methods, which can be effectively processed using machine learning (ML) algorithms into meaningful ecological information. ML uses user defined classes (e.g. species), derived from a subset (i.e. training data) of video-observed quantitative features (e.g. phenotypic variation), to infer classes in subsequent observations. However, phenotypic variation often changes due to environmental conditions, which may lead to poor classification, if environmentally induced variation in phenotypes is not accounted for. Here we describe a framework for classifying species under changing environmental conditions based on the random forest classification. A sliding window approach was developed that restricts temporal and environmentally conditions to improve the classification. We tested our approach by applying the classification framework to experimental data. The experiment used a set of six ciliate species to monitor changes in community structure and behavior over hundreds of generations, in dozens of species combinations and across a temperature gradient. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, as phenotypic differences driven by environmental change, could be captured by the classifier. Importantly, classification using the random forest algorithm showed comparable success when validated against traditional, slower, manual identification. Our framework allows for reliable classification in dynamic environments, and may help to improve strategies for long-term monitoring of species in changing environments. Our classification pipeline can be applied in fields assessing species community dynamics, such as eco-toxicology, ecology and evolutionary ecology. PMID:28472193

Microbial source tracking in impaired watersheds using PhyloChip and machine-learning classification.

PubMed

Dubinsky, Eric A; Butkus, Steven R; Andersen, Gary L

2016-11-15

Sources of fecal indicator bacteria are difficult to identify in watersheds that are impacted by a variety of non-point sources. We developed a molecular source tracking test using the PhyloChip microarray that detects and distinguishes fecal bacteria from humans, birds, ruminants, horses, pigs and dogs with a single test. The multiplexed assay targets 9001 different 25-mer fragments of 16S rRNA genes that are common to the bacterial community of each source type. Both random forests and SourceTracker were tested as discrimination tools, with SourceTracker classification producing superior specificity and sensitivity for all source types. Validation with 12 different mammalian sources in mixtures found 100% correct identification of the dominant source and 84-100% specificity. The test was applied to identify sources of fecal indicator bacteria in the Russian River watershed in California. We found widespread contamination by human sources during the wet season proximal to settlements with antiquated septic infrastructure and during the dry season at beaches during intense recreational activity. The test was more sensitive than common fecal indicator tests that failed to identify potential risks at these sites. Conversely, upstream beaches and numerous creeks with less reliance on onsite wastewater treatment contained no fecal signal from humans or other animals; however these waters did contain high counts of fecal indicator bacteria after rain. Microbial community analysis revealed that increased E. coli and enterococci at these locations did not co-occur with common fecal bacteria, but rather co-varied with copiotrophic bacteria that are common in freshwaters with high nutrient and carbon loading, suggesting runoff likely promoted the growth of environmental strains of E. coli and enterococci. These results indicate that machine-learning classification of PhyloChip microarray data can outperform conventional single marker tests that are used to assess health risks, and is an effective tool for distinguishing numerous fecal and environmental sources of pathogen indicators. Copyright © 2016 Elsevier Ltd. All rights reserved.

Pyrotechnic hazards classification and evaluation program. Phase 3, segments 1-4: Investigation of sensitivity test methods and procedures for pyrotechnic hazards evaluation and classification, part A

NASA Technical Reports Server (NTRS)

1971-01-01

The findings, conclusions, and recommendations relative to the investigations conducted to evaluate tests for classifying pyrotechnic materials and end items as to their hazard potential are presented. Information required to establish an applicable means of determining the potential hazards of pyrotechnics is described. Hazard evaluations are based on the peak overpressure or impulse resulting from the explosion as a function of distance from the source. Other hazard classification tests include dust ignition sensitivity, impact ignition sensitivity, spark ignition sensitivity, and differential thermal analysis.

Regime Shifts and Weakened Environmental Gradients in Open Oak and Pine Ecosystems

PubMed Central

Hanberry, Brice B.; Dey, Dan C.; He, Hong S.

2012-01-01

Fire suppression allows tree species that are intolerant of fire stress to increase their distribution, potentially resulting in disruption of historical species-environmental relationships. To measure changes between historical General Land Office surveys (1815 to 1850) and current USDA Forest Inventory and Assessment surveys (2004 to 2008), we compared composition, distribution, and site factors of 21 tree species or species groups in the Missouri Ozarks. We used 24 environmental variables and random forests as a classification method to model distributions. Eastern redcedar, elms, maples, and other fire-sensitive species have increased in dominance in oak forests, with concurrent reductions by oak species; specific changes varied by ecological subsection. Ordinations displayed loss of separation between formerly distinctive oak and fire-sensitive tree species groups. Distribution maps showed decreased presence of disturbance-dependent oak and pine species and increased presence of fire-sensitive species that generally expanded from subsections protected from fire along rivers to upland areas, except for eastern redcedar, which expanded into these subsections. Large scale differences in spatial gradients between past and present communities paralleled reduced influence of local topographic gradients in the varied relief of the Missouri Ozarks, as fire-sensitive species have moved to higher, drier, and sunnier sites away from riverine corridors. Due to changes in land use, landscapes in the Missouri Ozarks, eastern United States, and world-wide are changing from open oak and pine-dominated ecosystems to novel oak-mixed species forests, although at fine scales, forests are becoming more diverse in tree species today. Fire suppression weakened the influence by environmental gradients over species dominance, allowing succession from disturbance-dependent oaks to an alternative state of fire-sensitive species. Current and future research and conservation that rely on historical relationships and ecological principles based on disturbance across the landscape will need to incorporate modern interactions among species for resources into management plans and projections. PMID:22848467

Regime shifts and weakened environmental gradients in open oak and pine ecosystems.

PubMed

Hanberry, Brice B; Dey, Dan C; He, Hong S

2012-01-01

Fire suppression allows tree species that are intolerant of fire stress to increase their distribution, potentially resulting in disruption of historical species-environmental relationships. To measure changes between historical General Land Office surveys (1815 to 1850) and current USDA Forest Inventory and Assessment surveys (2004 to 2008), we compared composition, distribution, and site factors of 21 tree species or species groups in the Missouri Ozarks. We used 24 environmental variables and random forests as a classification method to model distributions. Eastern redcedar, elms, maples, and other fire-sensitive species have increased in dominance in oak forests, with concurrent reductions by oak species; specific changes varied by ecological subsection. Ordinations displayed loss of separation between formerly distinctive oak and fire-sensitive tree species groups. Distribution maps showed decreased presence of disturbance-dependent oak and pine species and increased presence of fire-sensitive species that generally expanded from subsections protected from fire along rivers to upland areas, except for eastern redcedar, which expanded into these subsections. Large scale differences in spatial gradients between past and present communities paralleled reduced influence of local topographic gradients in the varied relief of the Missouri Ozarks, as fire-sensitive species have moved to higher, drier, and sunnier sites away from riverine corridors. Due to changes in land use, landscapes in the Missouri Ozarks, eastern United States, and world-wide are changing from open oak and pine-dominated ecosystems to novel oak-mixed species forests, although at fine scales, forests are becoming more diverse in tree species today. Fire suppression weakened the influence by environmental gradients over species dominance, allowing succession from disturbance-dependent oaks to an alternative state of fire-sensitive species. Current and future research and conservation that rely on historical relationships and ecological principles based on disturbance across the landscape will need to incorporate modern interactions among species for resources into management plans and projections.

Central Sensitization-Based Classification for Temporomandibular Disorders: A Pathogenetic Hypothesis

PubMed Central

Cattaneo, Ruggero; Marci, Maria Chiara; Pietropaoli, Davide; Ortu, Eleonora

2017-01-01

Dysregulation of Autonomic Nervous System (ANS) and central pain pathways in temporomandibular disorders (TMD) is a growing evidence. Authors include some forms of TMD among central sensitization syndromes (CSS), a group of pathologies characterized by central morphofunctional alterations. Central Sensitization Inventory (CSI) is useful for clinical diagnosis. Clinical examination and CSI cannot identify the central site(s) affected in these diseases. Ultralow frequency transcutaneous electrical nerve stimulation (ULFTENS) is extensively used in TMD and in dental clinical practice, because of its effects on descending pain modulation pathways. The Diagnostic Criteria for TMD (DC/TMD) are the most accurate tool for diagnosis and classification of TMD. However, it includes CSI to investigate central aspects of TMD. Preliminary data on sensory ULFTENS show it is a reliable tool for the study of central and autonomic pathways in TMD. An alternative classification based on the presence of Central Sensitization and on individual response to sensory ULFTENS is proposed. TMD may be classified into 4 groups: (a) TMD with Central Sensitization ULFTENS Responders; (b) TMD with Central Sensitization ULFTENS Nonresponders; (c) TMD without Central Sensitization ULFTENS Responders; (d) TMD without Central Sensitization ULFTENS Nonresponders. This pathogenic classification of TMD may help to differentiate therapy and aetiology. PMID:28932132

Characterization and classification of lupus patients based on plasma thermograms

PubMed Central

Chaires, Jonathan B.; Mekmaysy, Chongkham S.; DeLeeuw, Lynn; Sivils, Kathy L.; Harley, John B.; Rovin, Brad H.; Kulasekera, K. B.; Jarjour, Wael N.

2017-01-01

Objective Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent health conditions and SLE diagnostic criteria. Methods Thermograms were visualized graphically for important differences between covariates and summarized using various measures. A modified linear discriminant analysis was used to segregate SLE versus control subjects on the basis of the thermograms. Classification accuracy was measured based on multiple training/test splits of the data and compared to classification based on SLE serological markers. Results Median sensitivity, specificity, and overall accuracy based on classification using plasma thermograms was 86%, 83%, and 84% compared to 78%, 95%, and 86% based on a combination of five antibody tests. Combining thermogram and serology information together improved sensitivity from 78% to 86% and overall accuracy from 86% to 89% relative to serology alone. Predictive accuracy of thermograms for distinguishing SLE and osteoarthritis / rheumatoid arthritis patients was comparable. Both gender and anemia significantly interacted with disease status for plasma thermograms (p<0.001), with greater separation between SLE and control thermograms for females relative to males and for patients with anemia relative to patients without anemia. Conclusion Plasma thermograms constitute an additional biomarker which may help improve diagnosis of SLE patients, particularly when coupled with standard diagnostic testing. Differences in thermograms according to patient sex, ethnicity, clinical and environmental factors are important considerations for application of thermograms in a clinical setting. PMID:29149219

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

Comparing the usefulness of the 1997 and 2009 WHO dengue case classification: a systematic literature review.

PubMed

Horstick, Olaf; Jaenisch, Thomas; Martinez, Eric; Kroeger, Axel; See, Lucy Lum Chai; Farrar, Jeremy; Ranzinger, Silvia Runge

2014-09-01

The 1997 and 2009 WHO dengue case classifications were compared in a systematic review with 12 eligible studies (4 prospective). Ten expert opinion articles were used for discussion. For the 2009 WHO classification studies show: when determining severe dengue sensitivity ranges between 59-98% (88%/98%: prospective studies), specificity between 41-99% (99%: prospective study) - comparing the 1997 WHO classification: sensitivity 24.8-89.9% (24.8%/74%: prospective studies), specificity: 25%/100% (100%: prospective study). The application of the 2009 WHO classification is easy, however for (non-severe) dengue there may be a risk of monitoring increased case numbers. Warning signs validation studies are needed. For epidemiological/pathogenesis research use of the 2009 WHO classification, opinion papers show that ease of application, increased sensitivity (severe dengue) and international comparability are advantageous; 3 severe dengue criteria (severe plasma leakage, severe bleeding, severe organ manifestation) are useful research endpoints. The 2009 WHO classification has clear advantages for clinical use, use in epidemiology is promising and research use may at least not be a disadvantage. © The American Society of Tropical Medicine and Hygiene.

Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China.

PubMed

Yang, Xiaoyan; Chen, Longgao; Li, Yingkui; Xi, Wenjia; Chen, Longqian

2015-07-01

Land use/land cover (LULC) inventory provides an important dataset in regional planning and environmental assessment. To efficiently obtain the LULC inventory, we compared the LULC classifications based on single satellite imagery with a rule-based classification based on multi-seasonal imagery in Lianyungang City, a coastal city in China, using CBERS-02 (the 2nd China-Brazil Environmental Resource Satellites) images. The overall accuracies of the classification based on single imagery are 78.9, 82.8, and 82.0% in winter, early summer, and autumn, respectively. The rule-based classification improves the accuracy to 87.9% (kappa 0.85), suggesting that combining multi-seasonal images can considerably improve the classification accuracy over any single image-based classification. This method could also be used to analyze seasonal changes of LULC types, especially for those associated with tidal changes in coastal areas. The distribution and inventory of LULC types with an overall accuracy of 87.9% and a spatial resolution of 19.5 m can assist regional planning and environmental assessment efficiently in Lianyungang City. This rule-based classification provides a guidance to improve accuracy for coastal areas with distinct LULC temporal spectral features.

The Ecohydrological Context of Drought and Classification of Plant Responses

NASA Astrophysics Data System (ADS)

Feng, X.; Ackerly, D.; Dawson, T. E.; Manzoni, S.; Skelton, R. P.; Vico, G.; Thompson, S. E.

2017-12-01

Many recent studies on drought-induced vegetation mortality have explored how plant functional traits, and classifications of such traits along axes of, e.g., isohydry - anisohydry, might contribute to predicting drought survival and recovery. As these studies proliferate, concerns are growing about the consistency and predictive value of such classifications. Here, we outline the basis for a systematic classification of drought strategies that accounts for both environmental conditions and functional traits. We (1) identify drawbacks of exiting isohydricity and trait-based metrics, (2) identify major axes of trait and environmental variation that determine drought mortality pathways (hydraulic failure and carbon starvation) using non-dimensional trait groups, and (3) demonstrate that these trait groupings predict physiological drought outcomes using both measured and synthetic data. In doing so we untangle some confounding effects of environment and trait variations that undermine current classification schemes, outline a pathway to progress towards a general classification of drought vulnerability, and advocate for more careful treatment of the environmental conditions within which plant drought responses occur.

Associations among hydrologic classifications and fish traits to support environmental flow standards

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

McManamay, Ryan A; Bevelhimer, Mark S; Frimpong, Dr. Emmanuel A,

2014-01-01

Classification systems are valuable to ecological management in that they organize information into consolidated units thereby providing efficient means to achieve conservation objectives. Of the many ways classifications benefit management, hypothesis generation has been discussed as the most important. However, in order to provide templates for developing and testing ecologically relevant hypotheses, classifications created using environmental variables must be linked to ecological patterns. Herein, we develop associations between a recent US hydrologic classification and fish traits in order to form a template for generating flow ecology hypotheses and supporting environmental flow standard development. Tradeoffs in adaptive strategies for fish weremore » observed across a spectrum of stable, perennial flow to unstable intermittent flow. In accordance with theory, periodic strategists were associated with stable, predictable flow, whereas opportunistic strategists were more affiliated with intermittent, variable flows. We developed linkages between the uniqueness of hydrologic character and ecological distinction among classes, which may translate into predictions between losses in hydrologic uniqueness and ecological community response. Comparisons of classification strength between hydrologic classifications and other frameworks suggested that spatially contiguous classifications with higher regionalization will tend to explain more variation in ecological patterns. Despite explaining less ecological variation than other frameworks, we contend that hydrologic classifications are still useful because they provide a conceptual linkage between hydrologic variation and ecological communities to support flow ecology relationships. Mechanistic associations among fish traits and hydrologic classes support the presumption that environmental flow standards should be developed uniquely for stream classes and ecological communities, therein.« less

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks.

PubMed

Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R; Nguyen, Tuan N; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-01-01

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks

PubMed Central

Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R.; Nguyen, Tuan N.; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T.

2017-01-01

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively. PMID:28326009

Applying Classification Trees to Hospital Administrative Data to Identify Patients with Lower Gastrointestinal Bleeding

PubMed Central

Siddique, Juned; Ruhnke, Gregory W.; Flores, Andrea; Prochaska, Micah T.; Paesch, Elizabeth; Meltzer, David O.; Whelan, Chad T.

2015-01-01

Background Lower gastrointestinal bleeding (LGIB) is a common cause of acute hospitalization. Currently, there is no accepted standard for identifying patients with LGIB in hospital administrative data. The objective of this study was to develop and validate a set of classification algorithms that use hospital administrative data to identify LGIB. Methods Our sample consists of patients admitted between July 1, 2001 and June 30, 2003 (derivation cohort) and July 1, 2003 and June 30, 2005 (validation cohort) to the general medicine inpatient service of the University of Chicago Hospital, a large urban academic medical center. Confirmed cases of LGIB in both cohorts were determined by reviewing the charts of those patients who had at least 1 of 36 principal or secondary International Classification of Diseases, Ninth revision, Clinical Modification (ICD-9-CM) diagnosis codes associated with LGIB. Classification trees were used on the data of the derivation cohort to develop a set of decision rules for identifying patients with LGIB. These rules were then applied to the validation cohort to assess their performance. Results Three classification algorithms were identified and validated: a high specificity rule with 80.1% sensitivity and 95.8% specificity, a rule that balances sensitivity and specificity (87.8% sensitivity, 90.9% specificity), and a high sensitivity rule with 100% sensitivity and 91.0% specificity. Conclusion These classification algorithms can be used in future studies to evaluate resource utilization and assess outcomes associated with LGIB without the use of chart review. PMID:26406318

[The physiological classification of human thermal states under high environmental temperatures].

PubMed

Bobrov, A F; Kuznets, E I

1995-01-01

The paper deals with the physiological classification of human thermal states in a hot environment. A review of the basic systems of classifications of thermal states is given, their main drawbacks are discussed. On the basis of human functional state research in a broad range of environmental temperatures the system of evaluation and classification of human thermal states is proposed. New integral one-dimensional multi-parametric criteria for evaluation are used. For the development of these criteria methods of factor, cluster and canonical correlation analyses are applied. Stochastic nomograms capable of identification of human thermal state for different intensity of influence are given. In this case evaluation of intensity is estimated according to one-dimensional criteria taking into account environmental temperature, physical load and time of man's staying in overheating conditions.

Evaluation of the performance of the reduced local lymph node assay for skin sensitization testing.

PubMed

Ezendam, Janine; Muller, Andre; Hakkert, Betty C; van Loveren, Henk

2013-06-01

The local lymph node assay (LLNA) is the preferred method for classification of sensitizers within REACH. To reduce the number of mice for the identification of sensitizers the reduced LLNA was proposed, which uses only the high dose group of the LLNA. To evaluate the performance of this method for classification, LLNA data from REACH registrations were used and classification based on all dose groups was compared to classification based on the high dose group. We confirmed previous examinations of the reduced LLNA showing that this method is less sensitive compared to the LLNA. The reduced LLNA misclassified 3.3% of the sensitizers identified in the LLNA and misclassification occurred in all potency classes and that there was no clear association with irritant properties. It is therefore not possible to predict beforehand which substances might be misclassified. Another limitation of the reduced LLNA is that skin sensitizing potency cannot be assessed. For these reasons, it is not recommended to use the reduced LLNA as a stand-alone assay for skin sensitization testing within REACH. In the future, the reduced LLNA might be of added value in a weight of evidence approach to confirm negative results obtained with non-animal approaches. Copyright © 2013 Elsevier Inc. All rights reserved.

Integration of environmental and spectral data for sunflower stress determination. [Red River Valley, Minnesota

NASA Technical Reports Server (NTRS)

Lillesand, T.; Seeley, M.

1983-01-01

Stress in sunflowers was assessed in western and northwestern Minnesota. Weekly ground observations (acquired in 1980 and 1981) were analyzed in concert with large scale aerial photography and concurrent LANDSAT data. Using multidate supervised and unsupervised classification procedures, it was found that all crops grown in association with sunflowers in the study area are spectrally separable from one another. Under conditions of extreme drought, severely stressed plants were differentiable from those not severely stressed, but between-crop separation was not possible. Initial regression analyses to estimate sunflower seed yield showed a sensitivity to environmental stress during the flowering and seed development stages. One of the most important biological factors related to sunflower production in the Red River Valley area was found to be the extent and severity of insect infestations.

Observing Changing Ecological Diversity in the Anthropocene

NASA Technical Reports Server (NTRS)

Schimel, David S.; Asner, Gregory P.; Moorcroft, Paul

2012-01-01

As the world enters the Anthropocene, the planet's environment is changing rapidly, putting critical ecosystem services at risk. Understanding and forecasting how ecosystems will change over the coming decades requires understanding the sensitivity of species to environmental change. The extant distribution of species and functional groups contains valuable information about the performance of different species in different environments. However, with high rates of environmental change, information inherent in ranges of many species will disappear, since that information exists only under quasi-equilibrium conditions. The information content of distributional data obtained now is greater than data obtained in the future. New remote sensing technologies can map chemical and structural traits of plant canopies and allow inference of trait and in many cases, species ranges. Current satellite remote sensing data can only produce relatively simple classifications, but new techniques have dramatically higher biological information content.

7 CFR 1794.31 - Classification.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 12 2013-01-01 2013-01-01 false Classification. 1794.31 Section 1794.31 Agriculture... Classification. (a) Electric and telecommunications programs. RUS will normally determine the proper environmental classification of projects based on its evaluation of the project description set forth in the...

7 CFR 1794.31 - Classification.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 12 2012-01-01 2012-01-01 false Classification. 1794.31 Section 1794.31 Agriculture... Classification. (a) Electric and telecommunications programs. RUS will normally determine the proper environmental classification of projects based on its evaluation of the project description set forth in the...

7 CFR 1794.31 - Classification.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 12 2014-01-01 2013-01-01 true Classification. 1794.31 Section 1794.31 Agriculture... Classification. (a) Electric and telecommunications programs. RUS will normally determine the proper environmental classification of projects based on its evaluation of the project description set forth in the...

7 CFR 1794.31 - Classification.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 12 2010-01-01 2010-01-01 false Classification. 1794.31 Section 1794.31 Agriculture... Classification. (a) Electric and telecommunications programs. RUS will normally determine the proper environmental classification of projects based on its evaluation of the project description set forth in the...

7 CFR 1794.31 - Classification.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 12 2011-01-01 2011-01-01 false Classification. 1794.31 Section 1794.31 Agriculture... Classification. (a) Electric and telecommunications programs. RUS will normally determine the proper environmental classification of projects based on its evaluation of the project description set forth in the...

40 CFR 52.1121 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The Massachusetts plan was evaluated on the basis of the following classifications... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.1121 Section 52.1121 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1121 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The Massachusetts plan was evaluated on the basis of the following classifications... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.1121 Section 52.1121 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

Fault Diagnosis for the Heat Exchanger of the Aircraft Environmental Control System Based on the Strong Tracking Filter

PubMed Central

Ma, Jian; Lu, Chen; Liu, Hongmei

2015-01-01

The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system’s efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

PubMed

Ma, Jian; Lu, Chen; Liu, Hongmei

2015-01-01

The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

Debris flow susceptibility assessment based on an empirical approach in the central region of South Korea

NASA Astrophysics Data System (ADS)

Kang, Sinhang; Lee, Seung-Rae

2018-05-01

Many debris flow spreading analyses have been conducted during recent decades to prevent damage from debris flows. An empirical approach that has been used in various studies on debris flow spreading has advantages such as simple data acquisition and good applicability for large areas. In this study, a GIS-based empirical model that was developed at the University of Lausanne (Switzerland) is used to assess the debris flow susceptibility. Study sites are classified based on the types of soil texture or geological conditions, which can indirectly consider geotechnical or rheological properties, to supplement the weaknesses of Flow-R which neglects local controlling factors. The mean travel angle for each classification is calculated from a debris flow inventory map. The debris flow susceptibility is assessed based on changes in the flow-direction algorithm, an inertial function with a 5-m DEM resolution. A simplified friction-limited model was applied to the runout distance analysis by using the appropriate travel angle for the corresponding classification with a velocity limit of 28 m/s. The most appropriate algorithm combinations that derived the highest average of efficiency and sensitivity for each classification are finally determined by applying a confusion matrix with the efficiency and the sensitivity to the results of the susceptibility assessment. The proposed schemes can be useful for debris flow susceptibility assessment in both the study area and the central region of Korea, which has similar environmental factors such as geological conditions, topography and rainfall characteristics to the study area.

Objective classification of ecological status in marine water bodies using ecotoxicological information and multivariate analysis.

PubMed

Beiras, Ricardo; Durán, Iria

2014-12-01

Some relevant shortcomings have been identified in the current approach for the classification of ecological status in marine water bodies, leading to delays in the fulfillment of the Water Framework Directive objectives. Natural variability makes difficult to settle fixed reference values and boundary values for the Ecological Quality Ratios (EQR) for the biological quality elements. Biological responses to environmental degradation are frequently of nonmonotonic nature, hampering the EQR approach. Community structure traits respond only once ecological damage has already been done and do not provide early warning signals. An alternative methodology for the classification of ecological status integrating chemical measurements, ecotoxicological bioassays and community structure traits (species richness and diversity), and using multivariate analyses (multidimensional scaling and cluster analysis), is proposed. This approach does not depend on the arbitrary definition of fixed reference values and EQR boundary values, and it is suitable to integrate nonlinear, sensitive signals of ecological degradation. As a disadvantage, this approach demands the inclusion of sampling sites representing the full range of ecological status in each monitoring campaign. National or international agencies in charge of coastal pollution monitoring have comprehensive data sets available to overcome this limitation.

Application of Sal classification to parotid gland fine-needle aspiration cytology: 10-year retrospective analysis of 312 patients.

PubMed

Kilavuz, Ahmet Erdem; Songu, Murat; İmre, Abdulkadir; Arslanoğlu, Secil; Özkul, Yilmaz; Pinar, Ercan; Ateş, Düzgün

2018-05-01

The accuracy of fine-needle aspiration biopsy (FNAB) is controversial in parotid tumors. We aimed to compare FNAB results with the final histopathological diagnosis and to apply the "Sal classification" to our data and discuss its results and its place in parotid gland cytology. The FNAB cytological findings and final histological diagnosis were assessed retrospectively in 2 different scenarios based on the distribution of nondefinitive cytology, and we applied the Sal classification and determined malignancy rate, sensitivity, and specificity for each category. In 2 different scenarios FNAB sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were found to be 81%, 87%, 54.7%, and 96.1%; and 65.3%, 100%, 100%, and 96.1%, respectively. The malignancy rates and sensitivity and specificity were also calculated and discussed for each Sal category. We believe that the Sal classification has a great potential to be a useful tool in classification of parotid gland cytology. © 2018 Wiley Periodicals, Inc.

40 CFR 52.2821 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The American Samoa plan was evaluated on the basis of the following classifications... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.2821 Section 52.2821 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2021 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The Pennsylvania plan was evaluated on the basis of the following classifications: Air... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.2021 Section 52.2021 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2521 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The West Virginia plan was evaluated on the basis of the following classifications... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.2521 Section 52.2521 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2121 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The South Carolina plan was evaluated on the basis of the following classifications... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.2121 Section 52.2121 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1521 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The New Hampshire plan was evaluated on the basis of the following classifications... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.1521 Section 52.1521 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2121 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The South Carolina plan was evaluated on the basis of the following classifications... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.2121 Section 52.2121 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2521 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The West Virginia plan was evaluated on the basis of the following classifications... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.2521 Section 52.2521 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2171 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The South Dakota plan evaluated on the basis of the following classifications: Air... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.2171 Section 52.2171 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2821 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The American Samoa plan was evaluated on the basis of the following classifications... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.2821 Section 52.2821 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1821 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The North Dakota plan was evaluated on the basis of the following classifications: Air... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.1821 Section 52.1821 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1271 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The Mississippi plan was evaluated on the basis of the following classifications: Air... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.1271 Section 52.1271 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1821 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The North Dakota plan was evaluated on the basis of the following classifications: Air... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.1821 Section 52.1821 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2071 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The Rhode Island plan was evaluated on the basis of the following classifications: Air... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.2071 Section 52.2071 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2071 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The Rhode Island plan was evaluated on the basis of the following classifications: Air... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.2071 Section 52.2071 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2721 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The Puerto Rico plan was evaluated on the basis of the following classifications. Air... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.2721 Section 52.2721 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2721 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The Puerto Rico plan was evaluated on the basis of the following classifications. Air... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.2721 Section 52.2721 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1271 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The Mississippi plan was evaluated on the basis of the following classifications: Air... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.1271 Section 52.1271 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2021 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The Pennsylvania plan was evaluated on the basis of the following classifications: Air... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.2021 Section 52.2021 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.2171 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Classification of regions. The South Dakota plan evaluated on the basis of the following classifications: Air... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.2171 Section 52.2171 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1521 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Classification of regions. The New Hampshire plan was evaluated on the basis of the following classifications... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.1521 Section 52.1521 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

[Accuracy improvement of spectral classification of crop using microwave backscatter data].

PubMed

Jia, Kun; Li, Qiang-Zi; Tian, Yi-Chen; Wu, Bing-Fang; Zhang, Fei-Fei; Meng, Ji-Hua

2011-02-01

In the present study, VV polarization microwave backscatter data used for improving accuracies of spectral classification of crop is investigated. Classification accuracy using different classifiers based on the fusion data of HJ satellite multi-spectral and Envisat ASAR VV backscatter data are compared. The results indicate that fusion data can take full advantage of spectral information of HJ multi-spectral data and the structure sensitivity feature of ASAR VV polarization data. The fusion data enlarges the spectral difference among different classifications and improves crop classification accuracy. The classification accuracy using fusion data can be increased by 5 percent compared to the single HJ data. Furthermore, ASAR VV polarization data is sensitive to non-agrarian area of planted field, and VV polarization data joined classification can effectively distinguish the field border. VV polarization data associating with multi-spectral data used in crop classification enlarges the application of satellite data and has the potential of spread in the domain of agriculture.

Ecological Sensitivity Evaluation of Tourist Region Based on Remote Sensing Image - Taking Chaohu Lake Area as a Case Study

NASA Astrophysics Data System (ADS)

Lin, Y.; Li, W. J.; Yu, J.; Wu, C. Z.

2018-04-01

Remote sensing technology is of significant advantages for monitoring and analysing ecological environment. By using of automatic extraction algorithm, various environmental resources information of tourist region can be obtained from remote sensing imagery. Combining with GIS spatial analysis and landscape pattern analysis, relevant environmental information can be quantitatively analysed and interpreted. In this study, taking the Chaohu Lake Basin as an example, Landsat-8 multi-spectral satellite image of October 2015 was applied. Integrated the automatic ELM (Extreme Learning Machine) classification results with the data of digital elevation model and slope information, human disturbance degree, land use degree, primary productivity, landscape evenness , vegetation coverage, DEM, slope and normalized water body index were used as the evaluation factors to construct the eco-sensitivity evaluation index based on AHP and overlay analysis. According to the value of eco-sensitivity evaluation index, by using of GIS technique of equal interval reclassification, the Chaohu Lake area was divided into four grades: very sensitive area, sensitive area, sub-sensitive areas and insensitive areas. The results of the eco-sensitivity analysis shows: the area of the very sensitive area was 4577.4378 km2, accounting for about 37.12 %, the sensitive area was 5130.0522 km2, accounting for about 37.12 %; the area of sub-sensitive area was 3729.9312 km2, accounting for 26.99 %; the area of insensitive area was 382.4399 km2, accounting for about 2.77 %. At the same time, it has been found that there were spatial differences in ecological sensitivity of the Chaohu Lake basin. The most sensitive areas were mainly located in the areas with high elevation and large terrain gradient. Insensitive areas were mainly distributed in slope of the slow platform area; the sensitive areas and the sub-sensitive areas were mainly agricultural land and woodland. Through the eco-sensitivity analysis of the study area, the automatic recognition and analysis techniques for remote sensing imagery are integrated into the ecological analysis and ecological regional planning, which can provide a reliable scientific basis for rational planning and regional sustainable development of the Chaohu Lake tourist area.

40 CFR 11.5 - Procedures.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY GENERAL SECURITY CLASSIFICATION REGULATIONS..., safekeeping, accountability, transmission, disposition, and destruction of classification information and... shall conform with the National Security Council Directive of May 17, 1972, governing the classification...

40 CFR 11.5 - Procedures.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY GENERAL SECURITY CLASSIFICATION REGULATIONS..., safekeeping, accountability, transmission, disposition, and destruction of classification information and... shall conform with the National Security Council Directive of May 17, 1972, governing the classification...

40 CFR 11.5 - Procedures.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY GENERAL SECURITY CLASSIFICATION REGULATIONS..., safekeeping, accountability, transmission, disposition, and destruction of classification information and... shall conform with the National Security Council Directive of May 17, 1972, governing the classification...

40 CFR 11.5 - Procedures.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY GENERAL SECURITY CLASSIFICATION REGULATIONS..., safekeeping, accountability, transmission, disposition, and destruction of classification information and... shall conform with the National Security Council Directive of May 17, 1972, governing the classification...

49 CFR 1105.6 - Classification of actions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 8 2011-10-01 2011-10-01 false Classification of actions. 1105.6 Section 1105.6... Classification of actions. (a) Environmental Impact Statements will normally be prepared for rail construction... classifications in this section apply without regard to whether the action is proposed by application, petition...

49 CFR 1105.6 - Classification of actions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 8 2010-10-01 2010-10-01 false Classification of actions. 1105.6 Section 1105.6... Classification of actions. (a) Environmental Impact Statements will normally be prepared for rail construction... classifications in this section apply without regard to whether the action is proposed by application, petition...

Boosted classification trees result in minor to modest improvement in the accuracy in classifying cardiovascular outcomes compared to conventional classification trees

PubMed Central

Austin, Peter C; Lee, Douglas S

2011-01-01

Purpose: Classification trees are increasingly being used to classifying patients according to the presence or absence of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final classification. The authors' objective was to examine whether boosting improved the accuracy of classification trees for predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes were less impressive than gains in performance observed in the data mining literature. PMID:22254181

A full computation-relevant topological dynamics classification of elementary cellular automata.

PubMed

Schüle, Martin; Stoop, Ruedi

2012-12-01

Cellular automata are both computational and dynamical systems. We give a complete classification of the dynamic behaviour of elementary cellular automata (ECA) in terms of fundamental dynamic system notions such as sensitivity and chaoticity. The "complex" ECA emerge to be sensitive, but not chaotic and not eventually weakly periodic. Based on this classification, we conjecture that elementary cellular automata capable of carrying out complex computations, such as needed for Turing-universality, are at the "edge of chaos."

Computer-aided Classification of Mammographic Masses Using Visually Sensitive Image Features

PubMed Central

Wang, Yunzhi; Aghaei, Faranak; Zarafshani, Ali; Qiu, Yuchen; Qian, Wei; Zheng, Bin

2017-01-01

Purpose To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. Methods An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Results Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. Conclusion This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a “visual aid” interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features. PMID:27911353

Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

USGS Publications Warehouse

Dieye, A.M.; Roy, David P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

2012-01-01

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

PubMed Central

Alves, Vinicius M.; Capuzzi, Stephen J.; Muratov, Eugene; Braga, Rodolpho C.; Thornton, Thomas; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

2016-01-01

Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential. PMID:28630595

Performance of the 2015 American College of Rheumatology/European League Against Rheumatism gout classification criteria in Thai patients.

PubMed

Louthrenoo, Worawit; Jatuworapruk, Kanon; Lhakum, Panomkorn; Pattamapaspong, Nuttaya

2017-05-01

To evaluate the sensitivity and specificity of the 2015 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) gout classification criteria in Thai patients presenting with acute arthritis in a real-life setting. Data were analyzed on consecutive patients presenting with arthritis of less than 2 weeks duration. Sensitivity and specificity were calculated by using the presence of monosodium urate (MSU) crystals in the synovial fluid or tissue aspirate as gold standard for gout diagnosis. Subgroup analysis was performed in patients with early disease (≤2 years), established disease (>2 years), and those without tophus. Additional analysis also was performed in non-tophaceous gout patients, and patients with acute calcium pyrophosphate dihydrate crystal arthritis were used as controls. One hundred and nine gout and 74 non-gout patients participated in this study. Full ACR/EULAR classification criteria had sensitivity and specificity of 90.2 and 90.0%, respectively; and 90.2 and 85.0%, respectively, when synovial fluid microscopy was excluded. Clinical-only criteria yielded sensitivity and specificity of 79.8 and 87.8%, respectively. The criteria performed well among patients with early and non-tophaceous disease, but had lower specificity in patients with established disease. The variation of serum uric acid level was a major limitation of the classification criteria. The ACR/EULAR classification criteria had high sensitivity and specificity in Thai patients presenting with acute arthritis, even when clinical criteria alone were used.

Bounding species distribution models

USGS Publications Warehouse

Stohlgren, T.J.; Jarnevich, C.S.; Esaias, W.E.; Morisette, J.T.

2011-01-01

Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for "clamping" model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used. ?? 2011 Current Zoology.

Bounding Species Distribution Models

NASA Technical Reports Server (NTRS)

Stohlgren, Thomas J.; Jarnevich, Cahterine S.; Morisette, Jeffrey T.; Esaias, Wayne E.

2011-01-01

Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for "clamping" model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used [Current Zoology 57 (5): 642-647, 2011].

A signal processing framework for simultaneous detection of multiple environmental contaminants

NASA Astrophysics Data System (ADS)

Chakraborty, Subhadeep; Manahan, Michael P.; Mench, Matthew M.

2013-11-01

The possibility of large-scale attacks using chemical warfare agents (CWAs) has exposed the critical need for fundamental research enabling the reliable, unambiguous and early detection of trace CWAs and toxic industrial chemicals. This paper presents a unique approach for the identification and classification of simultaneously present multiple environmental contaminants by perturbing an electrochemical (EC) sensor with an oscillating potential for the extraction of statistically rich information from the current response. The dynamic response, being a function of the degree and mechanism of contamination, is then processed with a symbolic dynamic filter for the extraction of representative patterns, which are then classified using a trained neural network. The approach presented in this paper promises to extend the sensing power and sensitivity of these EC sensors by augmenting and complementing sensor technology with state-of-the-art embedded real-time signal processing capabilities.

Calculating second derivatives of population growth rates for ecology and evolution

PubMed Central

Shyu, Esther; Caswell, Hal

2014-01-01

1. Second derivatives of the population growth rate measure the curvature of its response to demographic, physiological or environmental parameters. The second derivatives quantify the response of sensitivity results to perturbations, provide a classification of types of selection and provide one way to calculate sensitivities of the stochastic growth rate. 2. Using matrix calculus, we derive the second derivatives of three population growth rate measures: the discrete-time growth rate λ, the continuous-time growth rate r = log λ and the net reproductive rate R0, which measures per-generation growth. 3. We present a suite of formulae for the second derivatives of each growth rate and show how to compute these derivatives with respect to projection matrix entries and to lower-level parameters affecting those matrix entries. 4. We also illustrate several ecological and evolutionary applications for these second derivative calculations with a case study for the tropical herb Calathea ovandensis. PMID:25793101

78 FR 53476 - Notice of Realty Action: Classification for Lease and Subsequent Conveyance for Recreation and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-29

...; NMNM-130295] Notice of Realty Action: Classification for Lease and Subsequent Conveyance for Recreation... Management (BLM) has examined and found suitable for classification for lease and subsequent conveyance under... classification of the land for lease and subsequent conveyance of the land, and the environmental assessment...

78 FR 40167 - Notice of Realty Action: Classification for Lease and Subsequent Conveyance for Recreation and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-03

...; 13-08807; MO 4500050340; TAS: 14X5232] Notice of Realty Action: Classification for Lease and... Management (BLM) has examined and found suitable for classification for lease and subsequent conveyance under... proposed classification of the land for lease and subsequent conveyance of the land, and the environmental...

40 CFR 152.160 - Scope.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS PESTICIDE REGISTRATION AND CLASSIFICATION PROCEDURES Classification of Pesticides § 152.160 Scope. (a) Types of classification. A pesticide product may be unclassified, or it may be classified for restricted use or for...

40 CFR 152.160 - Scope.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS PESTICIDE REGISTRATION AND CLASSIFICATION PROCEDURES Classification of Pesticides § 152.160 Scope. (a) Types of classification. A pesticide product may be unclassified, or it may be classified for restricted use or for...

40 CFR 152.160 - Scope.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS PESTICIDE REGISTRATION AND CLASSIFICATION PROCEDURES Classification of Pesticides § 152.160 Scope. (a) Types of classification. A pesticide product may be unclassified, or it may be classified for restricted use or for...

40 CFR 152.160 - Scope.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS PESTICIDE REGISTRATION AND CLASSIFICATION PROCEDURES Classification of Pesticides § 152.160 Scope. (a) Types of classification. A pesticide product may be unclassified, or it may be classified for restricted use or for...

40 CFR 152.160 - Scope.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS PESTICIDE REGISTRATION AND CLASSIFICATION PROCEDURES Classification of Pesticides § 152.160 Scope. (a) Types of classification. A pesticide product may be unclassified, or it may be classified for restricted use or for...

The ITE Land classification: Providing an environmental stratification of Great Britain.

PubMed

Bunce, R G; Barr, C J; Gillespie, M K; Howard, D C

1996-01-01

The surface of Great Britain (GB) varies continuously in land cover from one area to another. The objective of any environmentally based land classification is to produce classes that match the patterns that are present by helping to define clear boundaries. The more appropriate the analysis and data used, the better the classes will fit the natural patterns. The observation of inter-correlations between ecological factors is the basis for interpreting ecological patterns in the field, and the Institute of Terrestrial Ecology (ITE) Land Classification formalises such subjective ideas. The data inevitably comprise a large number of factors in order to describe the environment adequately. Single factors, such as altitude, would only be useful on a national basis if they were the only dominant causative agent of ecological variation.The ITE Land Classification has defined 32 environmental categories called 'land classes', initially based on a sample of 1-km squares in Great Britain but subsequently extended to all 240 000 1-km squares. The original classification was produced using multivariate analysis of 75 environmental variables. The extension to all squares in GB was performed using a combination of logistic discrimination and discriminant functions. The classes have provided a stratification for successive ecological surveys, the results of which have characterised the classes in terms of botanical, zoological and landscape features.The classification has also been applied to integrate diverse datasets including satellite imagery, soils and socio-economic information. A variety of models have used the structure of the classification, for example to show potential land use change under different economic conditions. The principal data sets relevant for planning purposes have been incorporated into a user-friendly computer package, called the 'Countryside Information System'.

Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone

NASA Astrophysics Data System (ADS)

Duffy, James P.; Pratt, Laura; Anderson, Karen; Land, Peter E.; Shutler, Jamie D.

2018-01-01

Seagrass ecosystems are highly sensitive to environmental change. They are also in global decline and under threat from a variety of anthropogenic factors. There is now an urgency to establish robust monitoring methodologies so that changes in seagrass abundance and distribution in these sensitive coastal environments can be understood. Typical monitoring approaches have included remote sensing from satellites and airborne platforms, ground based ecological surveys and snorkel/scuba surveys. These techniques can suffer from temporal and spatial inconsistency, or are very localised making it hard to assess seagrass meadows in a structured manner. Here we present a novel technique using a lightweight (sub 7 kg) drone and consumer grade cameras to produce very high spatial resolution (∼4 mm pixel-1) mosaics of two intertidal sites in Wales, UK. We present a full data collection methodology followed by a selection of classification techniques to produce coverage estimates at each site. We trialled three classification approaches of varying complexity to investigate and illustrate the differing performance and capabilities of each. Our results show that unsupervised classifications perform better than object-based methods in classifying seagrass cover. We also found that the more sparsely vegetated of the two meadows studied was more accurately classified - it had lower root mean squared deviation (RMSD) between observed and classified coverage (9-9.5%) compared to a more densely vegetated meadow (RMSD 16-22%). Furthermore, we examine the potential to detect other biotic features, finding that lugworm mounds can be detected visually at coarser resolutions such as 43 mm pixel-1, whereas smaller features such as cockle shells within seagrass require finer grained data (<17 mm pixel-1).

Criteria for classification of competitive housing projects in terms of their environmental friendliness

NASA Astrophysics Data System (ADS)

Nezhnikova, Ekaterina

2017-10-01

This article deals with social and economic essence of strategy of the housing industry development, both complex system of economic relations in field of production and consumption, which is regulated through the mechanism of prices and implemented through formation and realization of priority directions. Developed criteria for classification of housing construction projects as environmentally friendly and the quality criteria of variables for assessment of the environmental friendliness of residential buildings allowed to determine the ways of development of the industry on the basis of creation of competitive projects in interrelation with quality, environmental friendliness and price of consumption.

Application of sensitivity analysis for assessment of de-desertification alternatives in the central Iran by using Triantaphyllou method.

PubMed

Sadeghi Ravesh, Mohammad Hassan; Ahmadi, Hassan; Zehtabian, Gholamreza

2011-08-01

Desertification, land degradation in arid, semi-arid, and dry sub-humid regions, is a global environmental problem. With respect to increasing importance of desertification and its complexity, the necessity of attention to the optimal de-desertification alternatives is essential. Therefore, this work presents an analytic hierarchy process (AHP) method to objectively select the optimal de-desertification alternatives based on the results of interviews with experts in Khezr Abad region, central Iran as the case study. This model was used in Yazd Khezr Abad region to evaluate the efficiency in presentation of better alternatives related to personal and environmental situations. Obtained results indicate that the criterion "proportion and adaptation to the environment" with the weighted average of 33.6% is the most important criterion from experts viewpoints. While prevention alternatives of land usage unsuitable of reveres and conversion with 22.88% mean weight and vegetation cover development and reclamation with 21.9% mean weight are recognized ordinarily as the most important de-desertification alternatives in region. Finally, sensitivity analysis is performed in detail by varying the objective factor decision weight, the priority weight of subjective factors, and the gain factors. After the fulfillment of sensitivity analysis and determination of the most sensitive criteria and alternatives, the former classification and ranking of alternatives does not change so much, and it was observed that unsuitable land use alternative with the preference degree of 22.7% was still in the first order of priority. The final priority of livestock grazing control alternative was replaced with the alternative of modification of ground water harvesting.

Classifying environmentally significant urban land uses with satellite imagery.

PubMed

Park, Mi-Hyun; Stenstrom, Michael K

2008-01-01

We investigated Bayesian networks to classify urban land use from satellite imagery. Landsat Enhanced Thematic Mapper Plus (ETM(+)) images were used for the classification in two study areas: (1) Marina del Rey and its vicinity in the Santa Monica Bay Watershed, CA and (2) drainage basins adjacent to the Sweetwater Reservoir in San Diego, CA. Bayesian networks provided 80-95% classification accuracy for urban land use using four different classification systems. The classifications were robust with small training data sets with normal and reduced radiometric resolution. The networks needed only 5% of the total data (i.e., 1500 pixels) for sample size and only 5- or 6-bit information for accurate classification. The network explicitly showed the relationship among variables from its structure and was also capable of utilizing information from non-spectral data. The classification can be used to provide timely and inexpensive land use information over large areas for environmental purposes such as estimating stormwater pollutant loads.

HYDROLOGIC REGIME CLASSIFICATION OF LAKE MICHIGAN COASTAL RIVERINE WETLANDS BASED ON WATERSHED CHARACTERISTICS

EPA Science Inventory

Classification of wetlands systems is needed not only to establish reference condition, but also to predict the relative sensitivity of different wetland classes. In the current study, we examined the potential for ecoregion- versus flow-based classification strategies to explain...

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

PubMed Central

Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

2015-01-01

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation. PMID:25560674

GIS coupled Multiple Criteria based Decision Support for Classification of Urban Coastal Areas in India

NASA Astrophysics Data System (ADS)

Dhiman, R.; Kalbar, P.; Inamdar, A. B.

2017-12-01

Coastal area classification in India is a challenge for federal and state government agencies due to fragile institutional framework, unclear directions in implementation of costal regulations and violations happening at private and government level. This work is an attempt to improvise the objectivity of existing classification methods to synergies the ecological systems and socioeconomic development in coastal cities. We developed a Geographic information system coupled Multi-criteria Decision Making (GIS-MCDM) approach to classify urban coastal areas where utility functions are used to transform the costal features into quantitative membership values after assessing the sensitivity of urban coastal ecosystem. Furthermore, these membership values for costal features are applied in different weighting schemes to derive Coastal Area Index (CAI) which classifies the coastal areas in four distinct categories viz. 1) No Development Zone, 2) Highly Sensitive Zone, 3) Moderately Sensitive Zone and 4) Low Sensitive Zone based on the sensitivity of urban coastal ecosystem. Mumbai, a coastal megacity in India is used as case study for demonstration of proposed method. Finally, uncertainty analysis using Monte Carlo approach to validate the sensitivity of CAI under specific multiple scenarios is carried out. Results of CAI method shows the clear demarcation of coastal areas in GIS environment based on the ecological sensitivity. CAI provides better decision support for federal and state level agencies to classify urban coastal areas according to the regional requirement of coastal resources considering resilience and sustainable development. CAI method will strengthen the existing institutional framework for decision making in classification of urban coastal areas where most effective coastal management options can be proposed.

24 CFR 58.36 - Environmental assessments.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Environmental assessments. 58.36... Development ENVIRONMENTAL REVIEW PROCEDURES FOR ENTITIES ASSUMING HUD ENVIRONMENTAL RESPONSIBILITIES Environmental Review Process: Documentation, Range of Activities, Project Aggregation and Classification § 58.36...

24 CFR 58.36 - Environmental assessments.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 1 2011-04-01 2011-04-01 false Environmental assessments. 58.36... Development ENVIRONMENTAL REVIEW PROCEDURES FOR ENTITIES ASSUMING HUD ENVIRONMENTAL RESPONSIBILITIES Environmental Review Process: Documentation, Range of Activities, Project Aggregation and Classification § 58.36...

Application of GIS-based Procedure on Slopeland Use Classification and Identification

NASA Astrophysics Data System (ADS)

KU, L. C.; LI, M. C.

2016-12-01

In Taiwan, the "Slopeland Conservation and Utilization Act" regulates the management of the slopelands. It categorizes the slopeland into land suitable for agricultural or animal husbandry, land suitable for forestry and land for enhanced conservation, according to the environmental factors of average slope, effective soil depth, soil erosion and parental rock. Traditionally, investigations of environmental factors require cost-effective field works. It has been confronted with many practical issues such as non-evaluated cadastral parcels, evaluation results depending on expert's opinion, difficulties in field measurement and judgment, and time consuming. This study aimed to develop a GIS-based procedure involved in the acceleration of slopeland use classification and quality improvement. First, the environmental factors of slopelands were analyzed by GIS and SPSS software. The analysis involved with the digital elevation model (DEM), soil depth map, land use map and satellite images. Second, 5% of the analyzed slopelands were selected to perform the site investigations and correct the results of classification. Finally, a 2nd examination was involved by randomly selected 2% of the analyzed slopelands to perform the accuracy evaluation. It was showed the developed procedure is effective in slopeland use classification and identification. Keywords: Slopeland Use Classification, GIS, Management

Differentiation chronic post traumatic stress disorder patients from healthy subjects using objective and subjective sleep-related parameters.

PubMed

Tahmasian, Masoud; Jamalabadi, Hamidreza; Abedini, Mina; Ghadami, Mohammad R; Sepehry, Amir A; Knight, David C; Khazaie, Habibolah

2017-05-22

Sleep disturbance is common in chronic post-traumatic stress disorder (PTSD). However, prior work has demonstrated that there are inconsistencies between subjective and objective assessments of sleep disturbance in PTSD. Therefore, we investigated whether subjective or objective sleep assessment has greater clinical utility to differentiate PTSD patients from healthy subjects. Further, we evaluated whether the combination of subjective and objective methods improves the accuracy of classification into patient versus healthy groups, which has important diagnostic implications. We recruited 32 chronic war-induced PTSD patients and 32 age- and gender-matched healthy subjects to participate in this study. Subjective (i.e. from three self-reported sleep questionnaires) and objective sleep-related data (i.e. from actigraphy scores) were collected from each participant. Subjective, objective, and combined (subjective and objective) sleep data were then analyzed using support vector machine classification. The classification accuracy, sensitivity, and specificity for subjective variables were 89.2%, 89.3%, and 89%, respectively. The classification accuracy, sensitivity, and specificity for objective variables were 65%, 62.3%, and 67.8%, respectively. The classification accuracy, sensitivity, and specificity for the aggregate variables (combination of subjective and objective variables) were 91.6%, 93.0%, and 90.3%, respectively. Our findings indicate that classification accuracy using subjective measurements is superior to objective measurements and the combination of both assessments appears to improve the classification accuracy for differentiating PTSD patients from healthy individuals. Copyright © 2017 Elsevier B.V. All rights reserved.

Verification and classification bias interactions in diagnostic test accuracy studies for fine-needle aspiration biopsy.

PubMed

Schmidt, Robert L; Walker, Brandon S; Cohen, Michael B

2015-03-01

Reliable estimates of accuracy are important for any diagnostic test. Diagnostic accuracy studies are subject to unique sources of bias. Verification bias and classification bias are 2 sources of bias that commonly occur in diagnostic accuracy studies. Statistical methods are available to estimate the impact of these sources of bias when they occur alone. The impact of interactions when these types of bias occur together has not been investigated. We developed mathematical relationships to show the combined effect of verification bias and classification bias. A wide range of case scenarios were generated to assess the impact of bias components and interactions on total bias. Interactions between verification bias and classification bias caused overestimation of sensitivity and underestimation of specificity. Interactions had more effect on sensitivity than specificity. Sensitivity was overestimated by at least 7% in approximately 6% of the tested scenarios. Specificity was underestimated by at least 7% in less than 0.1% of the scenarios. Interactions between verification bias and classification bias create distortions in accuracy estimates that are greater than would be predicted from each source of bias acting independently. © 2014 American Cancer Society.

Identification and classification of genes required for tolerance to freeze-thaw stress revealed by genome-wide screening of Saccharomyces cerevisiae deletion strains.

PubMed

Ando, Akira; Nakamura, Toshihide; Murata, Yoshinori; Takagi, Hiroshi; Shima, Jun

2007-03-01

Yeasts used in bread making are exposed to freeze-thaw stress during frozen-dough baking. To clarify the genes required for freeze-thaw tolerance, genome-wide screening was performed using the complete deletion strain collection of diploid Saccharomyces cerevisiae. The screening identified 58 gene deletions that conferred freeze-thaw sensitivity. These genes were then classified based on their cellular function and on the localization of their products. The results showed that the genes required for freeze-thaw tolerance were frequently involved in vacuole functions and cell wall biogenesis. The highest numbers of gene products were components of vacuolar H(+)-ATPase. Next, the cross-sensitivity of the freeze-thaw-sensitive mutants to oxidative stress and to cell wall stress was studied; both of these are environmental stresses closely related to freeze-thaw stress. The results showed that defects in the functions of vacuolar H(+)-ATPase conferred sensitivity to oxidative stress and to cell wall stress. In contrast, defects in gene products involved in cell wall assembly conferred sensitivity to cell wall stress but not to oxidative stress. Our results suggest the presence of at least two different mechanisms of freeze-thaw injury: oxidative stress generated during the freeze-thaw process, and defects in cell wall assembly.

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module.

PubMed

Rifai Chai; Naik, Ganesh R; Tran, Yvonne; Sai Ho Ling; Craig, Ashley; Nguyen, Hung T

2015-08-01

An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p <; 0.05).

Application of support vector machine method for the analysis of absorption spectra of exhaled air of patients with broncho-pulmonary diseases

NASA Astrophysics Data System (ADS)

Bukreeva, Ekaterina B.; Bulanova, Anna A.; Kistenev, Yury V.; Kuzmin, Dmitry A.; Tuzikov, Sergei A.; Yumov, Evgeny L.

2014-11-01

The results of the joint use of laser photoacoustic spectroscopy and chemometrics methods in gas analysis of exhaled air of patients with respiratory diseases (chronic obstructive pulmonary disease, pneumonia and lung cancer) are presented. The absorption spectra of exhaled breath of all volunteers were measured, the classification methods of the scans of the absorption spectra were applied, the sensitivity/specificity of the classification results were determined. It were obtained a result of nosological in pairs classification for all investigated volunteers, indices of sensitivity and specificity.

The use of ecological classification in management

Treesearch

Constance A. Carpenter; Wolf-Dieter Busch; David T. Cleland; Juan Gallegos; Rick Harris; ray Holm; Chris Topik; Al Williamson

1999-01-01

Ecological classificafion systems range over a variety of scales and reflect a variety of scientific viewpoints. They incorporate or emphasize varied arrays of environmental factors. Ecological classifications have been developed for marine, wetland, lake, stream, and terrestrial ecosystems. What are the benefits of ecological classification for natural resource...

28 CFR 17.24 - Duration of classification.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Duration of classification. 17.24 Section... ACCESS TO CLASSIFIED INFORMATION Classified Information § 17.24 Duration of classification. (a) At the... based on the duration of the national security sensitivity of the information. If the original...

Pattern recognition and image processing for environmental monitoring

NASA Astrophysics Data System (ADS)

Siddiqui, Khalid J.; Eastwood, DeLyle

1999-12-01

Pattern recognition (PR) and signal/image processing methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications employing analytical techniques for chemometrics such as gas chromatography, fluorescence spectroscopy, etc. An advantage of PR approaches is that they make no a prior assumption regarding the structure of the patterns. However, a majority of these systems rely on human judgment for parameter selection and classification. A PR problem is considered as a composite of four subproblems: pattern acquisition, feature extraction, feature selection, and pattern classification. One of the basic issues in PR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used in the decision rules. The state of the spectral techniques as applied to environmental monitoring is reviewed. A spectral pattern classification system combining the above components and automatic decision-theoretic approaches for classification is developed. It is shown how such a system can be used for analysis of large data sets, warehousing, and interpretation. In a preliminary test, the classifier was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Sensitivity and specificity of criteria for classifying body mass index in adolescents.

PubMed

Farias Júnior, José Cazuza de; Konrad, Lisandra Maria; Rabacow, Fabiana Maluf; Grup, Susane; Araújo, Valbério Candido

2009-02-01

To estimate the prevalence of overweight among adolescents using different body mass index (BMI) classification criteria, and to determine sensitivity and specificity values for these criteria. Weight, height, and tricipital and subscapular skinfolds in 934 adolescents (462 males and 472 females) aged 14-18 years (mean age 16.2; SD=1.0) of the city of Florianópolis, Southern Brazil, in 2001. Percent fat estimated based on skinfold measurements (> or =25% in males and > or =30% in females) was used as a gold-standard for determining specificity and sensitivity of BMI classification criteria among adolescents. The different cutoff points used for classifying BMI in general resulted in similar prevalence of overweight (p>0.05). Sensitivity of the evaluated criteria was high for males (85.4% to 91.7%) and low for females (33.8 to 52.8%). Specificity of all criteria was high for both sexes (83.6% to 98.8%). Estimates of prevalence of obesity among adolescents using different BMI classification criteria were similar and highly specific for both sexes, but sensitivity for females was low.

40 CFR 52.321 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 3 2011-07-01 2011-07-01 false Classification of regions. 52.321 Section 52.321 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) APPROVAL AND PROMULGATION OF IMPLEMENTATION PLANS Colorado § 52.321 Classification of regions. The revised Denver Emergency Episode Plan, adopte...

40 CFR 52.321 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 3 2010-07-01 2010-07-01 false Classification of regions. 52.321 Section 52.321 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) APPROVAL AND PROMULGATION OF IMPLEMENTATION PLANS Colorado § 52.321 Classification of regions. The revised Denver Emergency Episode Plan, adopte...

40 CFR 164.25 - Filing copies of notification of intent to cancel registration or change classification or...

Code of Federal Regulations, 2011 CFR

2011-07-01

... intent to cancel registration or change classification or refusal to register, and statement of issues. 164.25 Section 164.25 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE... ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS...

Habitat typing versus advanced vegetation classification in western forests

Treesearch

Tony Kusbach; John Shaw; James Long; Helga Van Miegroet

2012-01-01

Major habitat and community types in northern Utah were compared with plant alliances and associations that were derived from fidelity- and diagnostic-species classification concepts. Each of these classification approaches was associated with important environmental factors. Within a 20,000-ha watershed, 103 forest ecosystems were described by physiographic features,...

Speech Music Discrimination Using Class-Specific Features

DTIC Science & Technology

2004-08-01

Speech Music Discrimination Using Class-Specific Features Thomas Beierholm...between speech and music . Feature extraction is class-specific and can therefore be tailored to each class meaning that segment size, model orders...interest. Some of the applications of audio signal classification are speech/ music classification [1], acoustical environmental classification [2][3

40 CFR 260.30 - Non-waste determinations and variances from classification as a solid waste.

Code of Federal Regulations, 2010 CFR

2010-07-01

... from classification as a solid waste. 260.30 Section 260.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking Petitions § 260.30 Non-waste determinations and variances from classification as a solid waste. In...

40 CFR 260.30 - Non-waste determinations and variances from classification as a solid waste.

Code of Federal Regulations, 2011 CFR

2011-07-01

... from classification as a solid waste. 260.30 Section 260.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking Petitions § 260.30 Non-waste determinations and variances from classification as a solid waste. In...

32 CFR 2700.32 - Declassification general.

Code of Federal Regulations, 2011 CFR

2011-07-01

... classification. The determination to declassify information shall not be made on the basis of the level of classification assigned, but on the loss of the sensitivity of the information with the passage of time, and with... classification, release of information reasonably could still be expected to cause at least identifiable damage...

32 CFR 2700.32 - Declassification general.

Code of Federal Regulations, 2010 CFR

2010-07-01

... classification. The determination to declassify information shall not be made on the basis of the level of classification assigned, but on the loss of the sensitivity of the information with the passage of time, and with... classification, release of information reasonably could still be expected to cause at least identifiable damage...

Influential input classification in probabilistic multimedia models

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

Maddalena, Randy L.; McKone, Thomas E.; Hsieh, Dennis P.H.

1999-05-01

Monte Carlo analysis is a statistical simulation method that is often used to assess and quantify the outcome variance in complex environmental fate and effects models. Total outcome variance of these models is a function of (1) the uncertainty and/or variability associated with each model input and (2) the sensitivity of the model outcome to changes in the inputs. To propagate variance through a model using Monte Carlo techniques, each variable must be assigned a probability distribution. The validity of these distributions directly influences the accuracy and reliability of the model outcome. To efficiently allocate resources for constructing distributions onemore » should first identify the most influential set of variables in the model. Although existing sensitivity and uncertainty analysis methods can provide a relative ranking of the importance of model inputs, they fail to identify the minimum set of stochastic inputs necessary to sufficiently characterize the outcome variance. In this paper, we describe and demonstrate a novel sensitivity/uncertainty analysis method for assessing the importance of each variable in a multimedia environmental fate model. Our analyses show that for a given scenario, a relatively small number of input variables influence the central tendency of the model and an even smaller set determines the shape of the outcome distribution. For each input, the level of influence depends on the scenario under consideration. This information is useful for developing site specific models and improving our understanding of the processes that have the greatest influence on the variance in outcomes from multimedia models.« less

Remotely sensing the German Wadden Sea-a new approach to address national and international environmental legislation.

PubMed

Müller, Gabriele; Stelzer, Kerstin; Smollich, Susan; Gade, Martin; Adolph, Winny; Melchionna, Sabrina; Kemme, Linnea; Geißler, Jasmin; Millat, Gerald; Reimers, Hans-Christian; Kohlus, Jörn; Eskildsen, Kai

2016-10-01

The Wadden Sea along the North Sea coasts of Denmark, Germany, and the Netherlands is the largest unbroken system of intertidal sand and mud flats in the world. Its habitats are highly productive and harbour high standing stocks and densities of benthic species, well adapted to the demanding environmental conditions. Therefore, the Wadden Sea is one of the most important areas for migratory birds in the world and thus protected by national and international legislation, which amongst others requires extensive monitoring. Due to the inaccessibility of major areas of the Wadden Sea, a classification approach based on optical and radar remote sensing has been developed to support environmental monitoring programmes. In this study, the general classification framework as well as two specific monitoring cases, mussel beds and seagrass meadows, are presented. The classification of mussel beds profits highly from inclusion of radar data due to their rough surface and achieves agreements of up to 79 % with areal data from the regular monitoring programme. Classification of seagrass meadows reaches even higher agreements with monitoring data (up to 100 %) and furthermore captures seagrass densities as low as 10 %. The main classification results are information on area and location of individual habitats. These are needed to fulfil environmental legislation requirements. One of the major advantages of this approach is the large areal coverage with individual satellite images, allowing simultaneous assessment of both accessible and inaccessible areas and thus providing a more complete overall picture.

Escherichia coli's water load affects zebrafish (Danio rerio) behavior.

PubMed

Amorim, João; Fernandes, Miguel; Abreu, Isabel; Tavares, Fernando; Oliva-Teles, Luis

2018-05-01

Traditional physico-chemical sensors are becoming an obsolete tool for environmental quality assessment. Biomonitoring techniques, such as biological early warning systems present the advantage of being sensitivity, fast, non-invasive and ecologically relevant. In this work, we applied a video tracking system, developed with zebrafish (Danio rerio), to detect microbiological contamination in water. Using the fishs' behavior response, the system was able to detect the presence of a non-pathogenic environmental strain of Escherichia coli, at three different levels of contamination: 600, 1800 and 5000 CFU/100 mL (colony forming units/100 mL). Data was collected during 50 min of exposure and analyzed with the artificial neural networks Self-organizing Map and Multi-layer Perceptron. The behavior of exposed fish was more erratic, with pronounced and rapid changes on movement direction and with significant less exploratory activity. The accuracy, sensitivity and specificity values regarding the detection capability (distinction between presence or absence of contamination) ranged from 89 to 100%. Regarding the classification capability (distinction between experimental conditions), the values ranged from 67 to 89%. This research may be a valuable contribution to improve water monitoring and management strategies, by taking as reference the effects on biosensors, without a biased anthropocentric perspective. Copyright © 2018 Elsevier B.V. All rights reserved.

24 CFR 58.37 - Environmental impact statement determinations.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Environmental impact statement... Classification § 58.37 Environmental impact statement determinations. (a) An EIS is required when the project is... and Urban Development ENVIRONMENTAL REVIEW PROCEDURES FOR ENTITIES ASSUMING HUD ENVIRONMENTAL...

Munitions Classification With Portable Advanced Electromagnetic Sensors, Demonstration at the former Camp Beale, CA, Summer 2011

DTIC Science & Technology

2012-07-01

Engineering Service Center, Port Hueneme, CA Robert Kirgan, Army Environmental Command Doug Maddox, US Environmental Protection Agency Doug Murray...FINAL REPORT MUNITIONS CLASSIFICATION WITH PORTABLE ADVANCED ELECTROMAGNETIC SENSORS Demonstration at the former Camp Beale, CA , Summer...if it does not display a currently valid OMB control number. 1. REPORT DATE JUL 2012 2 . REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND

Computer-aided interpretation approach for optical tomographic images

NASA Astrophysics Data System (ADS)

Klose, Christian D.; Klose, Alexander D.; Netz, Uwe J.; Scheel, Alexander K.; Beuthan, Jürgen; Hielscher, Andreas H.

2010-11-01

A computer-aided interpretation approach is proposed to detect rheumatic arthritis (RA) in human finger joints using optical tomographic images. The image interpretation method employs a classification algorithm that makes use of a so-called self-organizing mapping scheme to classify fingers as either affected or unaffected by RA. Unlike in previous studies, this allows for combining multiple image features, such as minimum and maximum values of the absorption coefficient for identifying affected and not affected joints. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index, and mutual information. Different methods (i.e., clinical diagnostics, ultrasound imaging, magnet resonance imaging, and inspection of optical tomographic images), were used to produce ground truth benchmarks to determine the performance of image interpretations. Using data from 100 finger joints, findings suggest that some parameter combinations lead to higher sensitivities, while others to higher specificities when compared to single parameter classifications employed in previous studies. Maximum performances are reached when combining the minimum/maximum ratio of the absorption coefficient and image variance. In this case, sensitivities and specificities over 0.9 can be achieved. These values are much higher than values obtained when only single parameter classifications were used, where sensitivities and specificities remained well below 0.8.

Primary mass discrimination of high energy cosmic rays using PNN and k-NN methods

NASA Astrophysics Data System (ADS)

Rastegarzadeh, G.; Nemati, M.

2018-02-01

Probabilistic neural network (PNN) and k-Nearest Neighbors (k-NN) methods are widely used data classification techniques. In this paper, these two methods have been used to classify the Extensive Air Shower (EAS) data sets which were simulated using the CORSIKA code for three primary cosmic rays. The primaries are proton, oxygen and iron nuclei at energies of 100 TeV-10 PeV. This study is performed in the following of the investigations into the primary cosmic ray mass sensitive observables. We propose a new approach for measuring the mass sensitive observables of EAS in order to improve the primary mass separation. In this work, the EAS observables measurement has performed locally instead of total measurements. Also the relationships between the included number of observables in the classification methods and the prediction accuracy have been investigated. We have shown that the local measurements and inclusion of more mass sensitive observables in the classification processes can improve the classifying quality and also we have shown that muons and electrons energy density can be considered as primary mass sensitive observables in primary mass classification. Also it must be noted that this study is performed for Tehran observation level without considering the details of any certain EAS detection array.

Deep learning for tumor classification in imaging mass spectrometry.

PubMed

Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

2018-04-01

Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network.

PubMed

Chi, Jianning; Walia, Ekta; Babyn, Paul; Wang, Jimmy; Groot, Gary; Eramian, Mark

2017-08-01

With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into "malignant" and "benign" cases. The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.

Performance of the new ACR/EULAR classification criteria for systemic sclerosis in clinical practice.

PubMed

Jordan, Suzana; Maurer, Britta; Toniolo, Martin; Michel, Beat; Distler, Oliver

2015-08-01

The preliminary classification criteria for SSc lack sensitivity for mild/early SSc patients, therefore, the new ACR/EULAR classification criteria for SSc were developed. The objective of this study was to evaluate the performance of the new classification criteria for SSc in clinical practice in a cohort of mild/early patients. Consecutive patients with a clinical diagnosis of SSc, based on expert opinion, were prospectively recruited and assessed according to the EULAR Scleroderma Trials and Research group (EUSTAR) and very early diagnosis of SSc (VEDOSS) recommendations. In some patients, missing values were retrieved retrospectively from the patient's records. Patients were grouped into established SSc (fulfilling the old ACR criteria) and mild/early SSc (not fulfilling the old ACR criteria). The new ACR/EULAR criteria were applied to all patients. Of the 304 patients available for the final analysis, 162/304 (53.3%) had established SSc and 142/304 (46.7%) had mild/early SSc. All 162 established SSc patients fulfilled the new ACR/EULAR classification criteria. The remaining 142 patients had mild/early SSc. Eighty of these 142 patients (56.3%) fulfilled the new ACR/EULAR classification criteria. Patients with mild/early SSc not fulfilling the new classification criteria were most often suffering from RP, had SSc-characteristic autoantibodies and had an SSc pattern on nailfold capillaroscopy. Taken together, the sensitivity of the new ACR/EULAR classification criteria for the overall cohort was 242/304 (79.6%) compared with 162/304 (53.3%) for the ACR criteria. In this cohort with a focus on mild/early SSc, the new ACR/EULAR classification criteria showed higher sensitivity and classified more patients as definite SSc patients than the ACR criteria. © The Author 2015. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Spatial modeling of environmental vulnerability of marine finfish aquaculture using GIS-based neuro-fuzzy techniques.

PubMed

Navas, Juan Moreno; Telfer, Trevor C; Ross, Lindsay G

2011-08-01

Combining GIS with neuro-fuzzy modeling has the advantage that expert scientific knowledge in coastal aquaculture activities can be incorporated into a geospatial model to classify areas particularly vulnerable to pollutants. Data on the physical environment and its suitability for aquaculture in an Irish fjard, which is host to a number of different aquaculture activities, were derived from a three-dimensional hydrodynamic and GIS models. Subsequent incorporation into environmental vulnerability models, based on neuro-fuzzy techniques, highlighted localities particularly vulnerable to aquaculture development. The models produced an overall classification accuracy of 85.71%, with a Kappa coefficient of agreement of 81%, and were sensitive to different input parameters. A statistical comparison between vulnerability scores and nitrogen concentrations in sediment associated with salmon cages showed good correlation. Neuro-fuzzy techniques within GIS modeling classify vulnerability of coastal regions appropriately and have a role in policy decisions for aquaculture site selection. Copyright © 2011 Elsevier Ltd. All rights reserved.

Image-based deep learning for classification of noise transients in gravitational wave detectors

NASA Astrophysics Data System (ADS)

Razzano, Massimiliano; Cuoco, Elena

2018-05-01

The detection of gravitational waves has inaugurated the era of gravitational astronomy and opened new avenues for the multimessenger study of cosmic sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo interferometers will probe a much larger volume of space and expand the capability of discovering new gravitational wave emitters. The characterization of these detectors is a primary task in order to recognize the main sources of noise and optimize the sensitivity of interferometers. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. Deep learning techniques are a promising tool for the recognition and classification of glitches. We present a classification pipeline that exploits convolutional neural networks to classify glitches starting from their time-frequency evolution represented as images. We evaluated the classification accuracy on simulated glitches, showing that the proposed algorithm can automatically classify glitches on very fast timescales and with high accuracy, thus providing a promising tool for online detector characterization.

Cancer Pain: A Critical Review of Mechanism-based Classification and Physical Therapy Management in Palliative Care

PubMed Central

Kumar, Senthil P

2011-01-01

Mechanism-based classification and physical therapy management of pain is essential to effectively manage painful symptoms in patients attending palliative care. The objective of this review is to provide a detailed review of mechanism-based classification and physical therapy management of patients with cancer pain. Cancer pain can be classified based upon pain symptoms, pain mechanisms and pain syndromes. Classification based upon mechanisms not only addresses the underlying pathophysiology but also provides us with an understanding behind patient's symptoms and treatment responses. Existing evidence suggests that the five mechanisms – central sensitization, peripheral sensitization, sympathetically maintained pain, nociceptive and cognitive-affective – operate in patients with cancer pain. Summary of studies showing evidence for physical therapy treatment methods for cancer pain follows with suggested therapeutic implications. Effective palliative physical therapy care using a mechanism-based classification model should be tailored to suit each patient's findings, using a biopsychosocial model of pain. PMID:21976851

Comparison of benthic indices for the evaluation of ecological status of three Slovenian transitional water bodies (northern Adriatic).

PubMed

Pitacco, Valentina; Lipej, Lovrenc; Mavrič, Borut; Mistri, Michele; Munari, Cristina

2018-04-01

Benthic indicators are important tools for the classification of coastal and transitional water bodies. The aim of the work was to assess for the first time the Environmental Status (ES) of Slovenian transitional waters, comparing the following biotic indices: richness, Shannon-Weaver diversity, AMBI, M-AMBI, BENTIX and BITS indices. A total of 13 stations were sampled with a Van Veen grab, in three ecosystems in the northern Adriatic. Samples were sieved and sorted, invertebrates identified and counted. The anthropogenic impact was estimated with professional judgement. Richness and diversity showed a good response to anthropogenic pressure. Conversely, indices based on sensitivity/tolerance groups did not showed a clear distinction between more and less impacted ecosystems. In particular BENTIX underestimated the ES, while with BITS there was a overestimation. The best evaluation was obtained with M-AMBI, because even if based on a sensitivity/tolerance approach, it considered also the structural aspect of the community. Copyright © 2017 Elsevier Ltd. All rights reserved.

Detection, Classification, and Density Estimation of Marine Mammals

DTIC Science & Technology

2012-10-01

Energy and Environmental Readiness Division, Washington, D.C. DETECTION...was prepared for and funded by Chief of Naval Operations, Energy and Environmental Readiness Division, Washington DC. The report was prepared by...and classification, including improvements to the  Energy  Ratio Mapping Algorithm (ERMA) method for use on gliders and  its  extension  to  new

Robust evaluation of time series classification algorithms for structural health monitoring

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Worden, Keith; Todd, Michael D.

2014-03-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

Can-CSC-GBE: Developing Cost-sensitive Classifier with Gentleboost Ensemble for breast cancer classification using protein amino acids and imbalanced data.

PubMed

Ali, Safdar; Majid, Abdul; Javed, Syed Gibran; Sattar, Mohsin

2016-06-01

Early prediction of breast cancer is important for effective treatment and survival. We developed an effective Cost-Sensitive Classifier with GentleBoost Ensemble (Can-CSC-GBE) for the classification of breast cancer using protein amino acid features. In this work, first, discriminant information of the protein sequences related to breast tissue is extracted. Then, the physicochemical properties hydrophobicity and hydrophilicity of amino acids are employed to generate molecule descriptors in different feature spaces. For comparison, we obtained results by combining Cost-Sensitive learning with conventional ensemble of AdaBoostM1 and Bagging. The proposed Can-CSC-GBE system has effectively reduced the misclassification costs and thereby improved the overall classification performance. Our novel approach has highlighted promising results as compared to the state-of-the-art ensemble approaches. Copyright © 2016 Elsevier Ltd. All rights reserved.

Three methods for integration of environmental risk into the benefit-risk assessment of veterinary medicinal products.

PubMed

Chapman, Jennifer L; Porsch, Lucas; Vidaurre, Rodrigo; Backhaus, Thomas; Sinclair, Chris; Jones, Glyn; Boxall, Alistair B A

2017-12-15

Veterinary medicinal products (VMPs) require, as part of the European Union (EU) authorization process, consideration of both risks and benefits. Uses of VMPs have multiple risks (e.g., risks to the animal being treated, to the person administering the VMP) including risks to the environment. Environmental risks are not directly comparable to therapeutic benefits; there is no standardized approach to compare both environmental risks and therapeutic benefits. We have developed three methods for communicating and comparing therapeutic benefits and environmental risks for the benefit-risk assessment that supports the EU authorization process. Two of these methods support independent product evaluation (i.e., a summative classification and a visual scoring matrix classification); the other supports a comparative evaluation between alternative products (i.e., a comparative classification). The methods and the challenges to implementing a benefit-risk assessment including environmental risk are presented herein; how these concepts would work in current policy is discussed. Adaptability to scientific and policy development is considered. This work is an initial step in the development of a standardized methodology for integrated decision-making for VMPs. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification and Clustering Methods for Multiple Environmental Factors in Gene-Environment Interaction: Application to the Multi-Ethnic Study of Atherosclerosis.

PubMed

Ko, Yi-An; Mukherjee, Bhramar; Smith, Jennifer A; Kardia, Sharon L R; Allison, Matthew; Diez Roux, Ana V

2016-11-01

There has been an increased interest in identifying gene-environment interaction (G × E) in the context of multiple environmental exposures. Most G × E studies analyze one exposure at a time, but we are exposed to multiple exposures in reality. Efficient analysis strategies for complex G × E with multiple environmental factors in a single model are still lacking. Using the data from the Multiethnic Study of Atherosclerosis, we illustrate a two-step approach for modeling G × E with multiple environmental factors. First, we utilize common clustering and classification strategies (e.g., k-means, latent class analysis, classification and regression trees, Bayesian clustering using Dirichlet Process) to define subgroups corresponding to distinct environmental exposure profiles. Second, we illustrate the use of an additive main effects and multiplicative interaction model, instead of the conventional saturated interaction model using product terms of factors, to study G × E with the data-driven exposure subgroups defined in the first step. We demonstrate useful analytical approaches to translate multiple environmental exposures into one summary class. These tools not only allow researchers to consider several environmental exposures in G × E analysis but also provide some insight into how genes modify the effect of a comprehensive exposure profile instead of examining effect modification for each exposure in isolation.

Prediction of Chemical Respiratory Sensitizers Using GARD, a Novel In Vitro Assay Based on a Genomic Biomarker Signature

PubMed Central

Albrekt, Ann-Sofie; Borrebaeck, Carl A. K.; Lindstedt, Malin

2015-01-01

Background Repeated exposure to certain low molecular weight (LMW) chemical compounds may result in development of allergic reactions in the skin or in the respiratory tract. In most cases, a certain LMW compound selectively sensitize the skin, giving rise to allergic contact dermatitis (ACD), or the respiratory tract, giving rise to occupational asthma (OA). To limit occurrence of allergic diseases, efforts are currently being made to develop predictive assays that accurately identify chemicals capable of inducing such reactions. However, while a few promising methods for prediction of skin sensitization have been described, to date no validated method, in vitro or in vivo, exists that is able to accurately classify chemicals as respiratory sensitizers. Results Recently, we presented the in vitro based Genomic Allergen Rapid Detection (GARD) assay as a novel testing strategy for classification of skin sensitizing chemicals based on measurement of a genomic biomarker signature. We have expanded the applicability domain of the GARD assay to classify also respiratory sensitizers by identifying a separate biomarker signature containing 389 differentially regulated genes for respiratory sensitizers in comparison to non-respiratory sensitizers. By using an independent data set in combination with supervised machine learning, we validated the assay, showing that the identified genomic biomarker is able to accurately classify respiratory sensitizers. Conclusions We have identified a genomic biomarker signature for classification of respiratory sensitizers. Combining this newly identified biomarker signature with our previously identified biomarker signature for classification of skin sensitizers, we have developed a novel in vitro testing strategy with a potent ability to predict both skin and respiratory sensitization in the same sample. PMID:25760038

29 CFR Appendix A to Subpart P of... - Soil Classification

Code of Federal Regulations, 2013 CFR

2013-07-01

... 29 Labor 8 2013-07-01 2013-07-01 false Soil Classification A Appendix A to Subpart P of Part 1926..., App. A Appendix A to Subpart P of Part 1926—Soil Classification (a) Scope and application—(1) Scope. This appendix describes a method of classifying soil and rock deposits based on site and environmental...

29 CFR Appendix A to Subpart P of... - Soil Classification

Code of Federal Regulations, 2012 CFR

2012-07-01

... 29 Labor 8 2012-07-01 2012-07-01 false Soil Classification A Appendix A to Subpart P of Part 1926..., App. A Appendix A to Subpart P of Part 1926—Soil Classification (a) Scope and application—(1) Scope. This appendix describes a method of classifying soil and rock deposits based on site and environmental...

29 CFR Appendix A to Subpart P of... - Soil Classification

Code of Federal Regulations, 2014 CFR

2014-07-01

... 29 Labor 8 2014-07-01 2014-07-01 false Soil Classification A Appendix A to Subpart P of Part 1926..., App. A Appendix A to Subpart P of Part 1926—Soil Classification (a) Scope and application—(1) Scope. This appendix describes a method of classifying soil and rock deposits based on site and environmental...

29 CFR Appendix A to Subpart P of... - Soil Classification

Code of Federal Regulations, 2011 CFR

2011-07-01

... 29 Labor 8 2011-07-01 2011-07-01 false Soil Classification A Appendix A to Subpart P of Part 1926..., App. A Appendix A to Subpart P of Part 1926—Soil Classification (a) Scope and application—(1) Scope. This appendix describes a method of classifying soil and rock deposits based on site and environmental...

International Classification of Impairments, Disabilities, and Handicaps: A Manual of Classification Relating to the Consequences of Disease.

ERIC Educational Resources Information Center

World Health Organization, Geneva (Switzerland).

This classification system is intended to offer a conceptual framework for information; the framework is relevant to the long-term consequences of disease, injuries or disorders, and applicable both to personal health care, including early identification and prevention, and to the mitigation of environmental and societal barriers. It begins with…

Validating the performance of vehicle classification stations : executive summary report.

DOT National Transportation Integrated Search

2012-05-01

Vehicle classification data are used in many transportation applications, including: pavement design, : environmental impact studies, traffic control, and traffic safety. Typical of most developed countries, every : state in the US maintains a networ...

40 CFR 152.161 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS PESTICIDE REGISTRATION AND CLASSIFICATION PROCEDURES Classification of Pesticides § 152.161 Definitions. In addition to... use means any pesticide application that occurs outside enclosed manmade structures or the...

40 CFR 152.161 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS PESTICIDE REGISTRATION AND CLASSIFICATION PROCEDURES Classification of Pesticides § 152.161 Definitions. In addition to... use means any pesticide application that occurs outside enclosed manmade structures or the...

40 CFR 152.161 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS PESTICIDE REGISTRATION AND CLASSIFICATION PROCEDURES Classification of Pesticides § 152.161 Definitions. In addition to... use means any pesticide application that occurs outside enclosed manmade structures or the...

Mining vehicle classifications from the Columbus Metropolitan Freeway Management System.

DOT National Transportation Integrated Search

2015-01-01

Vehicle classification data are used in many transportation applications, including: pavement design, : environmental impact studies, traffic control, and traffic safety. Ohio has over 200 permanent count stations, : supplemented by many more short-t...

40 CFR 164.120 - Notification.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF PRACTICE... REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF REGISTRATIONS AND... imminent hazard during the time required for cancellation or change in classification proceedings, but that...

40 CFR 164.120 - Notification.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF PRACTICE... REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF REGISTRATIONS AND... imminent hazard during the time required for cancellation or change in classification proceedings, but that...

Understanding the relationship between environmental quality ...

EPA Pesticide Factsheets

In 2014, approximately 17.7 million (7.4%) of United States (U.S.) adults had asthma. In 2009 alone, asthma caused 479,300 hospitalizations and 1.9 million emergency room visits. Asthma has been associated with exposure to air pollution and socioeconomic status, and reductions in atopic sensitization, an asthma precursor, have been associated with green space exposure, suggesting a role of environmental quality. We linked the Environmental Quality Index (EQI), representing 5 environmental domains (air, water, land, built, and sociodemographic) for all US counties (N=3,141) from 2000—2005 to Truven Health’s MarketScan individual claims database to examine associations between county-level EQI and asthma among U.S. adults ages 18-65 from 2003-2010. We defined asthma as having at least 1 claim (International Classification of Disease 9th edition, code 493) during the study period. We used random intercept multi-level Poisson regression clustered by county, adjusted for 10-year age category and sex, to estimate fixed effects of quintiles of the EQI on asthma prevalence. We examined modification by urbanicity through stratification by 4 rural-urban continuum codes (RUCC) ranging from most urban (RUCC1) to rural (RUCC4). Approximately 3% of adults in MarketScan have asthma claims. Comparing the highest EQI quintile (worst quality) to lowest EQI quintile (best quality), we observed increased asthma claims associated with worse environmental quality (prevalence rat

Changes in Crohn's disease phenotype over time in the Chinese population: validation of the Montreal classification system.

PubMed

Chow, Dorothy K L; Leong, Rupert W L; Lai, Larry H; Wong, Grace L H; Leung, Wai-Keung; Chan, Francis K L; Sung, Joseph J Y

2008-04-01

Phenotypic evolution of Crohn's disease occurs in whites but has never been described in other populations. The Montreal classification may describe phenotypes more precisely. The aim of this study was to validate the Montreal classification through a longitudinal sensitivity analysis in detecting phenotypic variation compared to the Vienna classification. This was a retrospective longitudinal study of consecutive Chinese Crohn's disease patients. All cases were classified by the Montreal classification and the Vienna classification for behavior and location. The evolution of these characteristics and the need for surgery were evaluated. A total of 109 patients were recruited (median follow-up: 4 years, range: 6 months-18 years). Crohn's disease behavior changed 3 years after diagnosis (P = 0.025), with an increase in stricturing and penetrating phenotypes, as determined by the Montreal classification, but was only detected by the Vienna classification after 5 years (P = 0.015). Disease location remained stable on follow-up in both classifications. Thirty-four patients (31%) underwent major surgery during the follow-up period with the stricturing [P = 0.002; hazard ratio (HR): 3.3; 95% CI: 1.5-7.0] and penetrating (P = 0.03; HR: 5.8; 95% CI: 1.2-28.2) phenotypes according to the Montreal classification associated with the need for major surgery. In contrast, colonic disease was protective against a major operation (P = 0.02; HR: 0.3; 95% CI: 0.08-0.8). This is the first study demonstrating phenotypic evolution of Crohn's disease in a nonwhite population. The Montreal classification is more sensitive to behavior phenotypic changes than is the Vienna classification after excluding perianal disease from the penetrating disease category and was useful in predicting course and the need for surgery.

40 CFR 164.90 - Initial decision.

Code of Federal Regulations, 2010 CFR

2010-07-01

....90 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF... REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF... Environmental Appeals Board. The initial decision shall become the decision of the Environmental Appeals Board...

40 CFR 164.90 - Initial decision.

Code of Federal Regulations, 2011 CFR

2011-07-01

....90 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF... REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF... Environmental Appeals Board. The initial decision shall become the decision of the Environmental Appeals Board...

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

PubMed Central

2014-01-01

Background The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB). Methods Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated. Results Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast. Conclusions A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice. PMID:24981916

Environmental clustering of lakes to evaluate performance of a macrophyte index of biotic integrity

USGS Publications Warehouse

Vondracek, Bruce C.; Vondracek, Bruce; Hatch, Lorin K.

2013-01-01

Proper classification of sites is critical for the use of biological indices that can distinguish between natural and human-induced variation in biological response. The macrophyte-based index of biotic integrity was developed to assess the condition of Minnesota lakes in relation to anthropogenic stressors, but macrophyte community composition varies naturally across the state. The goal of the study was to identify environmental characteristics that naturally influence macrophyte index response and establish a preliminary lake classification scheme for biological assessment (bioassessment). Using a comprehensive set of environmental variables, we identified similar groups of lakes by clustering using flexible beta classification. Variance partitioning analysis of IBI response indicated that evaluating similar lake clusters could improve the ability of the macrophyte index to identify community change to anthropogenic stressors, although lake groups did not fully account for the natural variation in macrophyte composition. Diagnostic capabilities of the index could be improved when evaluating lakes with similar environmental characteristics, suggesting the index has potential for accurate bioassessment provided comparable groups of lakes are evaluated.

Learning about the internal structure of categories through classification and feature inference.

PubMed

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition.

PubMed

Janousova, Eva; Schwarz, Daniel; Kasparek, Tomas

2015-06-30

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

40 CFR 257.3-7 - Air.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES CRITERIA FOR CLASSIFICATION OF SOLID WASTE DISPOSAL FACILITIES AND PRACTICES Classification of Solid Waste Disposal Facilities... residential, commercial, institutional or industrial solid waste. This requirement does not apply to...

40 CFR 257.3-7 - Air.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES CRITERIA FOR CLASSIFICATION OF SOLID WASTE DISPOSAL FACILITIES AND PRACTICES Classification of Solid Waste Disposal Facilities... residential, commercial, institutional or industrial solid waste. This requirement does not apply to...

Mining vehicle classifications from the Columbus Metropolitan Freeway Management System : [summary].

DOT National Transportation Integrated Search

2015-01-01

Vehicle classification data are used in many transportation applications, including: pavement design, : environmental impact studies, traffic control, and traffic safety. Ohio has over 200 permanent count : stations, supplemented by many more short-t...

A fit-for-purpose approach to analytical sensitivity applied to a cardiac troponin assay: time to escape the 'highly-sensitive' trap.

PubMed

Ungerer, Jacobus P J; Pretorius, Carel J

2014-04-01

Highly-sensitive cardiac troponin (cTn) assays are being introduced into the market. In this study we argue that the classification of cTn assays into sensitive and highly-sensitive is flawed and recommend a more appropriate way to characterize analytical sensitivity of cTn assays. The raw data of 2252 cardiac troponin I (cTnI) tests done in duplicate with a 'sensitive' assay was extracted and used to calculate the cTnI levels in all, including those below the 'limit of detection' (LoD) that were censored. Duplicate results were used to determine analytical imprecision. We show that cTnI can be quantified in all samples including those with levels below the LoD and that the actual margins of error decrease as concentrations approach zero. The dichotomous classification of cTn assays into sensitive and highly-sensitive is theoretically flawed and characterizing analytical sensitivity as a continuous variable based on imprecision at 0 and the 99th percentile cut-off would be more appropriate.

Global Stress Classification System for Materials Used in Solar Energy Applications

NASA Astrophysics Data System (ADS)

Slamova, Karolina; Schill, Christian; Herrmann, Jan; Datta, Pawan; Chih Wang, Chien

2016-08-01

Depending on the geographical location, the individual or combined impact of environmental stress factors and corresponding performance losses for solar applications varies significantly. Therefore, as a strategy to reduce investment risks and operating and maintenance costs, it is necessary to adapt the materials and components of solar energy systems specifically to regional environmental conditions. The project «GloBe Solar» supports this strategy by focusing on the development of a global stress classification system for materials in solar energy applications. The aim of this classification system is to assist in the identification of the individual stress conditions for every location on the earth's surface. The stress classification system could serve as a decision support tool for the industry (manufacturers, investors, lenders and project developers) and help to improve knowledge and services that can provide higher confidence to solar power systems.

A proposal of criteria for the classification of systemic sclerosis.

PubMed

Nadashkevich, Oleg; Davis, Paul; Fritzler, Marvin J

2004-11-01

Sensitive and specific criteria for the classification of systemic sclerosis are required by clinicians and investigators to achieve higher quality clinical studies and approaches to therapy. A clinical study of systemic sclerosis patients in Europe and Canada led to a set of criteria that achieve high sensitivity and specificity. Both clinical and laboratory investigations of patients with systemic sclerosis, related conditions and diseases with clinical features that can be mistaken as part of the systemic sclerosis spectrum were undertaken. Laboratory investigations included the detection of autoantibodies to centromere proteins, Scl-70 (topoisomerase I), and fibrillarin (U3-RNP). Based on the investigation of 269 systemic sclerosis patients and 720 patients presenting with related and confounding conditions, the following set of criteria for the classification of systemic sclerosis was proposed: 1) autoantibodies to: centromere proteins, Scl-70 (topo I), fibrillarin; 2) bibasilar pulmonary fibrosis; 3) contractures of the digital joints or prayer sign; 4) dermal thickening proximal to the wrists; 5) calcinosis cutis; 6) Raynaud's phenomenon; 7) esophageal distal hypomotility or reflux-esophagitis; 8) sclerodactyly or non-pitting digital edema; 9) teleangiectasias. The classification of definite SSc requires at least three of the above criteria. Criteria for the classification of systemic sclerosis have been proposed. Preliminary testing has defined the sensitivity and specificity of these criteria as high as 99% and 100%, respectively. Testing and validation of the proposed criteria by other clinical centers is required.

[Research progress on remote sensing of ecological and environmental changes in the Three Gorges Reservoir area, China].

PubMed

Teng, Ming-jun; Zeng, Li-xiong; Xiao, Wen-fa; Zhou, Zhi-xiang; Huang, Zhi-lin; Wang, Peng-cheng; Dian, Yuan-yong

2014-12-01

The Three Gorges Reservoir area (TGR area) , one of the most sensitive ecological zones in China, has dramatically changes in ecosystem configurations and services driven by the Three Gorges Engineering Project and its related human activities. Thus, understanding the dynamics of ecosystem configurations, ecological processes and ecosystem services is an attractive and critical issue to promote regional ecological security of the TGR area. The remote sensing of environment is a promising approach to the target and is thus increasingly applied to and ecosystem dynamics of the TGR area on mid- and macro-scales. However, current researches often showed controversial results in ecological and environmental changes in the TGR area due to the differences in remote sensing data, scale, and land-use/cover classification. Due to the complexity of ecological configurations and human activities, challenges still exist in the remote-sensing based research of ecological and environmental changes in the TGR area. The purpose of this review was to summarize the research advances in remote sensing of ecological and environmental changes in the TGR area. The status, challenges and trends of ecological and environmental remote-sensing in the TGR area were further discussed and concluded in the aspect of land-use/land-cover, vegetation dynamics, soil and water security, ecosystem services, ecosystem health and its management. The further researches on the remote sensing of ecological and environmental changes were proposed to improve the ecosystem management of the TGR area.

40 CFR 260.33 - Procedures for variances from classification as a solid waste, for variances to be classified as...

Code of Federal Regulations, 2011 CFR

2011-07-01

... classification as a solid waste, for variances to be classified as a boiler, or for non-waste determinations. 260.33 Section 260.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES... from classification as a solid waste, for variances to be classified as a boiler, or for non-waste...

40 CFR 260.33 - Procedures for variances from classification as a solid waste, for variances to be classified as...

Code of Federal Regulations, 2010 CFR

2010-07-01

... classification as a solid waste, for variances to be classified as a boiler, or for non-waste determinations. 260.33 Section 260.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES... from classification as a solid waste, for variances to be classified as a boiler, or for non-waste...

Efficacy measures associated to a plantar pressure based classification system in diabetic foot medicine.

PubMed

Deschamps, Kevin; Matricali, Giovanni Arnoldo; Desmet, Dirk; Roosen, Philip; Keijsers, Noel; Nobels, Frank; Bruyninckx, Herman; Staes, Filip

2016-09-01

The concept of 'classification' has, similar to many other diseases, been found to be fundamental in the field of diabetic medicine. In the current study, we aimed at determining efficacy measures of a recently published plantar pressure based classification system. Technical efficacy of the classification system was investigated by applying a high resolution, pixel-level analysis on the normalized plantar pressure pedobarographic fields of the original experimental dataset consisting of 97 patients with diabetes and 33 persons without diabetes. Clinical efficacy was assessed by considering the occurence of foot ulcers at the plantar aspect of the forefoot in this dataset. Classification efficacy was assessed by determining the classification recognition rate as well as its sensitivity and specificity using cross-validation subsets of the experimental dataset together with a novel cohort of 12 patients with diabetes. Pixel-level comparison of the four groups associated to the classification system highlighted distinct regional differences. Retrospective analysis showed the occurence of eleven foot ulcers in the experimental dataset since their gait analysis. Eight out of the eleven ulcers developed in a region of the foot which had the highest forces. Overall classification recognition rate exceeded 90% for all cross-validation subsets. Sensitivity and specificity of the four groups associated to the classification system exceeded respectively the 0.7 and 0.8 level in all cross-validation subsets. The results of the current study support the use of the novel plantar pressure based classification system in diabetic foot medicine. It may particularly serve in communication, diagnosis and clinical decision making. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction of Skin Sensitization Potency Using Machine Learning Approaches

EPA Science Inventory

Replacing animal tests currently used for regulatory hazard classification of skin sensitizers is one of ICCVAM’s top priorities. Accordingly, U.S. federal agency scientists are developing and evaluating computational approaches to classify substances as sensitizers or nons...

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System.

PubMed

Hu, Maogui; Li, Wei; Li, Lianfa; Houston, Douglas; Wu, Jun

2016-01-01

Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations.

In the (sub)tropics allergic rhinitis and its impact on asthma classification of allergic rhinitis is more useful than perennial-seasonal classification.

PubMed

Larenas-Linnemann, Désirée; Michels, Alexandra; Dinger, Hanna; Arias-Cruz, Alfredo; Ambriz Moreno, Marichuy; Bedolla Barajas, Martin; Javier, Ruth Cerino; Cid Del Prado, Maria de la Luz; Cruz Moreno, Manuel Alejandro; Vergara, Laura Diego; García Almaráz, Roberto; García-Cobas, Cecilia Y; Garcia Imperial, Daniel Alberto; Muñoz, Rosa Garcia; Hernandez Colín, Dante; Linares Zapien, Francisco Javier; Luna Pech, Jorge Agustín; Matta Campos, Juan Jose; Martinez Jimenez, Norma; Avalos, Miguel Medina; Medina Hernandez, Alejandra; Maldonado, Albero Monteverde; López, Doris Nereida; Pizano Nazara, Luis Julian; Sanchez, Emanuel Ramirez; Ramos López, José Domingo; Rodriguez-Pérez, Noel; Rodriguez Ortiz, Pablo G; Shah-Hosseini, Kijawasch; Mösges, Ralph

2014-01-01

Two different allergic rhinitis (AR) symptom phenotype classifications exist. Treatment recommendations are based on intermittent-persistent (INT-PER) cataloging, but clinical trials still use the former seasonal AR-perennial AR (SAR-PAR) classification. This study was designed to describe how INT-PER, mild-moderate/severe and SAR-PAR of patients seen by allergists are distributed over the different climate zones in a (sub)tropical country and how these phenotypes relate to allergen sensitization patterns. Six climate zones throughout Mexico were determined, based on National Geographic Institute (Instituto Nacional de Estadística y Geografía) data. Subsequent AR patients (2-68 years old) underwent a blinded, standardized skin-prick test and filled out a validated questionnaire phenotyping AR. Five hundred twenty-nine subjects participated in this study. In the tropical zone with 87% house-dust mite sensitization, INT (80.9%; p < 0.001) and PAR (91%; p = 0.04) were more frequent than in the subtropics. In the central high-pollen areas, there was less moderate/severe AR (65.5%; p < 0.005). Frequency of comorbid asthma showed a clear north-south gradient, from 25% in the dry north to 59% in the tropics (p < 0.005). No differences exist in AR cataloging among patients with different sensitization patterns, with two minor exceptions (more PER in tree sensitized and more PAR in mold positives; p < 0.05). In a (sub)tropical country the SAR-PAR classification seems of limited value and bears poor relation with the INT-PER classification. INT is more frequent in the tropical zone. Because PER has been shown to relate to AR severity, clinical trials should select patients based on INT-PER combined with the severity cataloging because these make for a better treatment guide than SAR-PAR.

40 CFR 164.20 - Commencement of proceeding.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Section 164.20 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS..., ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS... registration or to change the classification of a pesticide. A proceeding shall likewise be commenced whenever...

40 CFR 164.20 - Commencement of proceeding.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Section 164.20 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS..., ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS... registration or to change the classification of a pesticide. A proceeding shall likewise be commenced whenever...

40 CFR 11.1 - Purpose.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 1 2010-07-01 2010-07-01 false Purpose. 11.1 Section 11.1 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY GENERAL SECURITY CLASSIFICATION REGULATIONS PURSUANT... the classification and declassification of national security information. They apply also to...

Parental Sensitivity, Infant Affect, and Affect Regulation: Predictors of Later Attachment.

ERIC Educational Resources Information Center

Braungart-Rieker, Julia M.; Garwood, Molly M.; Powers, Bruce P.; Wang, Xiaoyu

2001-01-01

Examined extent to which parent sensitivity, infant affect, and affect regulation at 4 months predicted mother- and father-infant attachment classifications at 1 year. Found that affect regulation and maternal sensitivity discriminated infant-mother attachment groups. The association between maternal sensitivity and infant-mother attachment was…

Orientation selectivity based structure for texture classification

NASA Astrophysics Data System (ADS)

Wu, Jinjian; Lin, Weisi; Shi, Guangming; Zhang, Yazhong; Lu, Liu

2014-10-01

Local structure, e.g., local binary pattern (LBP), is widely used in texture classification. However, LBP is too sensitive to disturbance. In this paper, we introduce a novel structure for texture classification. Researches on cognitive neuroscience indicate that the primary visual cortex presents remarkable orientation selectivity for visual information extraction. Inspired by this, we investigate the orientation similarities among neighbor pixels, and propose an orientation selectivity based pattern for local structure description. Experimental results on texture classification demonstrate that the proposed structure descriptor is quite robust to disturbance.

Self-estimation or phototest measurement of skin UV sensitivity and its association with people's attitudes towards sun exposure.

PubMed

Falk, Magnus

2014-02-01

Fitzpatrick's classification is the most common way of assessing skin UV sensitivity. The study aim was to investigate how self-estimated and actual UV sensitivity, as measured by phototest, are associated with attitudes towards sunbathing and the propensity to increase sun protection, as well as the correlation between self-estimated and actual UV sensitivity. A total of 166 primary healthcare patients filled-out a questionnaire investigating attitudes towards sunbathing and the propensity to increase sun protection. They reported their skin type according to Fitzpatrick, and a UV sensitivity phototest was performed. Self-rated low UV sensitivity (skin type III-VI) was associated with a more positive attitude towards sunbathing and a lower propensity to increase sun protection, compared to high UV sensitivity. The correlation between the two methods was weak. The findings might indicate that individuals with a perceived low but in reality high UV sensitivity do not seek adequate sun protection with regard to skin cancer risk. Furthermore, the poor correlation between self-reported and actual UV sensitivity, measured by phototest, makes the clinical use of Fitzpatrick's classification questionable.

International regulatory requirements for skin sensitization testing.

PubMed

Daniel, Amber B; Strickland, Judy; Allen, David; Casati, Silvia; Zuang, Valérie; Barroso, João; Whelan, Maurice; Régimbald-Krnel, M J; Kojima, Hajime; Nishikawa, Akiyoshi; Park, Hye-Kyung; Lee, Jong Kwon; Kim, Tae Sung; Delgado, Isabella; Rios, Ludmila; Yang, Ying; Wang, Gangli; Kleinstreuer, Nicole

2018-06-01

Skin sensitization test data are required or considered by chemical regulation authorities around the world. These data are used to develop product hazard labeling for the protection of consumers or workers and to assess risks from exposure to skin-sensitizing chemicals. To identify opportunities for regulatory uses of non-animal replacements for skin sensitization tests, the needs and uses for skin sensitization test data must first be clarified. Thus, we reviewed skin sensitization testing requirements for seven countries or regions that are represented in the International Cooperation on Alternative Test Methods (ICATM). We noted the type of skin sensitization data required for each chemical sector and whether these data were used in a hazard classification, potency classification, or risk assessment context; the preferred tests; and whether alternative non-animal tests were acceptable. An understanding of national and regional regulatory requirements for skin sensitization testing will inform the development of ICATM's international strategy for the acceptance and implementation of non-animal alternatives to assess the health hazards and risks associated with potential skin sensitizers. Copyright © 2018 Elsevier Inc. All rights reserved.

Performance of the 2012 Systemic Lupus International Collaborating Clinics classification criteria versus the 1997 American College of Rheumatology classification criteria in adult and juvenile systemic lupus erythematosus. A systematic review and meta-analysis.

PubMed

Hartman, Esther A R; van Royen-Kerkhof, Annet; Jacobs, Johannes W G; Welsing, Paco M J; Fritsch-Stork, Ruth D E

2018-03-01

To evaluate the performance in classifying systemic lupus erythematosus by the 2012 Systemic Lupus International Collaborating Clinics criteria (SLICC'12), versus the revised American College of Rheumatology criteria from 1997 (ACR'97) in adult and juvenile SLE patients. A systematic literature search was conducted in PubMed and Embase for studies comparing SLICC'12 and ACR'97 with clinical diagnosis. A meta-analysis was performed to estimate the sensitivity and specificity of SLICC'12 and ACR'97. To assess classification earlier in the disease by either set, sensitivity and specificity were compared for patients with disease duration <5years. Sensitivity and specificity of individual criteria items were also assessed. In adult SLE (nine studies: 5236 patients, 1313 controls), SLICC'12 has higher sensitivity (94.6% vs. 89.6%) and similar specificity (95.5% vs. 98.1%) compared to ACR'97. For juvenile SLE (four studies: 568 patients, 339 controls), SLICC'12 demonstrates higher sensitivity (99.9% vs. 84.3%) than ACR'97, but much lower specificity (82.0% vs. 94.1%). SLICC'12 classifies juvenile SLE patients earlier in disease course. Individual items contributing to diagnostic accuracy are low complement, anti-ds DNA and acute cutaneous lupus in SLICC'12, and the immunologic and hematologic disorder in ACR'97. Based on sensitivity and specificity SLICC'12 is best for adult SLE. Following the view that higher specificity, i.e. avoidance of false positives, is preferable, ACR'97 is best for juvenile SLE even if associated with lower sensitivity. Our results on the contribution of the individual items of SLICC'12 and ACR´97 may be of value in future efforts to update classification criteria. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Analysis and comparison of sleeping posture classification methods using pressure sensitive bed system.

PubMed

Hsia, C C; Liou, K J; Aung, A P W; Foo, V; Huang, W; Biswas, J

2009-01-01

Pressure ulcers are common problems for bedridden patients. Caregivers need to reposition the sleeping posture of a patient every two hours in order to reduce the risk of getting ulcers. This study presents the use of Kurtosis and skewness estimation, principal component analysis (PCA) and support vector machines (SVMs) for sleeping posture classification using cost-effective pressure sensitive mattress that can help caregivers to make correct sleeping posture changes for the prevention of pressure ulcers.

Reduction from cost-sensitive ordinal ranking to weighted binary classification.

PubMed

Lin, Hsuan-Tien; Li, Ling

2012-05-01

We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only the design of good ordinal ranking algorithms based on well-tuned binary classification approaches, but also the derivation of new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance, as well as improved listwise ranking performance.

Comparison of patterns of allergen sensitization among inner-city Hispanic and African American children with asthma.

PubMed

Rastogi, Deepa; Reddy, Mamta; Neugebauer, Richard

2006-11-01

Among Hispanics, the largest minority ethnic group in the United States, asthma prevalence is increasing, particularly in inner-city neighborhoods. Although allergen sensitization among asthmatic African Americans has been extensively studied, similar details are not available for Hispanic children. To examine patterns of allergen sensitization, including the association with illness severity, in asthmatic children overall and in Hispanic and African American children living in a socioeconomically disadvantaged area of New York City. A retrospective medical record review of asthmatic children attending a community hospital in the South Bronx area of New York City was performed. Information abstracted included demographics, asthma severity classification, reported exposures to indoor allergens, and results of allergy testing. Among 384 children in the analysis, 270 (70.3%) were Hispanic and 114 (29.7%) were African American. Sensitization to indoor and outdoor allergens, respectively, did not differ between Hispanic (58.5% and 27.0%) and African American (58.8% and 32.6%) children. Allergen sensitization exhibited a direct, significant association with asthma severity for indoor allergens for the 2 ethnic groups combined and for Hispanics separately but not between asthma severity and outdoor allergens (P < .01). No correlation was found between self-reported allergen exposure and sensitization. Patterns of allergen sensitization among inner-city Hispanic asthmatic children resemble those among African American children, a finding that is likely explained by the similarity in levels of environmental exposures. With the increasing prevalence of asthma among inner-city Hispanic children, skin testing should be used frequently for objective evaluation of asthma in this ethnic group.

Algorithms for Hyperspectral Endmember Extraction and Signature Classification with Morphological Dendritic Networks

NASA Astrophysics Data System (ADS)

Schmalz, M.; Ritter, G.

Accurate multispectral or hyperspectral signature classification is key to the nonimaging detection and recognition of space objects. Additionally, signature classification accuracy depends on accurate spectral endmember determination [1]. Previous approaches to endmember computation and signature classification were based on linear operators or neural networks (NNs) expressed in terms of the algebra (R, +, x) [1,2]. Unfortunately, class separation in these methods tends to be suboptimal, and the number of signatures that can be accurately classified often depends linearly on the number of NN inputs. This can lead to poor endmember distinction, as well as potentially significant classification errors in the presence of noise or densely interleaved signatures. In contrast to traditional CNNs, autoassociative morphological memories (AMM) are a construct similar to Hopfield autoassociatived memories defined on the (R, +, ?,?) lattice algebra [3]. Unlimited storage and perfect recall of noiseless real valued patterns has been proven for AMMs [4]. However, AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, the prior definition of a set of endmembers corresponds to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. It has further been shown that AMMs can be based on dendritic computation [3,6]. These techniques yield improved accuracy and class segmentation/separation ability in the presence of highly interleaved signature data. In this paper, we present a procedure for endmember determination based on AMM noise sensitivity, which employs morphological dendritic computation. We show that detected endmembers can be exploited by AMM based classification techniques, to achieve accurate signature classification in the presence of noise, closely spaced or interleaved signatures, and simulated camera optical distortions. In particular, we examine two critical cases: (1) classification of multiple closely spaced signatures that are difficult to separate using distance measures, and (2) classification of materials in simulated hyperspectral images of spaceborne satellites. In each case, test data are derived from a NASA database of space material signatures. Additional analysis pertains to computational complexity and noise sensitivity, which are superior to classical NN based techniques.

A proposed ecosystem services classification system to support green accounting

EPA Science Inventory

There are a multitude of actual or envisioned, complete or incomplete, ecosystem service classification systems being proposed to support Green Accounting. Green Accounting is generally thought to be the formal accounting attempt to factor environmental production into National ...

40 CFR 257.2 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES CRITERIA FOR CLASSIFICATION OF SOLID WASTE DISPOSAL FACILITIES AND PRACTICES Classification of Solid Waste Disposal Facilities... demolition (C&D) landfill means a solid waste disposal facility subject to the requirements of subparts A or...

Breastfeeding and its relation to maternal sensitivity and infant attachment.

PubMed

Tharner, Anne; Luijk, Maartje P C M; Raat, Hein; Ijzendoorn, Marinus H; Bakermans-Kranenburg, Marian J; Moll, Henriette A; Jaddoe, Vincent W V; Hofman, Albert; Verhulst, Frank C; Tiemeier, Henning

2012-06-01

To examine the association of breastfeeding with maternal sensitive responsiveness and infant-mother attachment security and disorganization. We included 675 participants of a prospective cohort study. Questionnaires about breastfeeding practices were administered at 2 and 6 months postpartum. At 14 months, maternal sensitive responsiveness was assessed in a 13-minute laboratory procedure using Ainsworth's sensitivity scales, and attachment quality was assessed with the Strange Situation Procedure. Mothers were genotyped for oxytocin receptor genes OXTR rs53576 and OXTR rs2254298. Linear regressions and analyses of covariance adjusted for various background variables were conducted. We tested for mediation and moderation by maternal sensitive responsiveness and maternal oxytocin receptor genotype. Continuous analyses showed that longer duration of breastfeeding was associated with more maternal sensitive responsiveness (B = 0.11, 95% confidence interval [CI] 0.02; 0.20, p < .05), more attachment security (B = 0.24, 95% CI = 0.02; 0.46, p < .05), and less attachment disorganization (B = -0.20, 95% CI -0.36; -0.03, p < .05). Duration of breastfeeding was not related to the risk of insecure-avoidant or insecure-resistant versus secure attachment classification, but longer duration of breastfeeding predicted a lower risk of disorganized versus secure attachment classification (n = 151; odds ratio [OR] = 0.81, 95% CI 0.66 to 0.99, p = .04). Maternal sensitive responsiveness did not mediate the associations, and maternal oxytocin receptor genotype was not a significant moderator. Although duration of breastfeeding was not associated with differences in infant-mother attachment classifications, we found subtle positive associations between duration of breastfeeding and sensitive responsiveness, attachment security, and disorganization.

2013 American College of Rheumatology/European League against rheumatism classification criteria for systemic sclerosis outperform the 1980 criteria: data from the Canadian Scleroderma Research Group.

PubMed

Alhajeri, Hebah; Hudson, Marie; Fritzler, Marvin; Pope, Janet; Tatibouet, Solène; Markland, Janet; Robinson, David; Jones, Niall; Khalidi, Nader; Docherty, Peter; Kaminska, Elzbieta; Masetto, Ariel; Sutton, Evelyn; Mathieu, Jean-Pierre; Ligier, Sophie; Grodzicky, Tamara; LeClercq, Sharon; Thorne, Carter; Gyger, Geneviève; Smith, Douglas; Fortin, Paul R; Larché, Maggie; Baron, Murray

2015-04-01

The goal of this study was to determine the sensitivity of the new 2013 classification criteria for systemic sclerosis (SSc; scleroderma) in an independent cohort of SSc subjects and to assess the contribution of individual items of the criteria to the overall sensitivity. SSc subjects from the Canadian Scleroderma Research Group cohort were assessed. Sensitivity was determined in several subgroups of patients. In patients without the criterion of skin thickening proximal to the metacarpophalangeal (MCP) joints, we recalculated sensitivity after removing the individual criterion. A total of 724 SSc patients were included. Most were women (86%), mean age was 55.8 years, mean disease duration was 10.9 years, and 59% had limited cutaneous SSc (lcSSc). Overall, the sensitivity of the 2013 criteria was 98.3% compared to 88.3% for the 1980 criteria. This pattern was consistent among those with lcSSc (98.8% versus 85.6%), anticentromere antibodies (98.9% versus 79.8%), disease duration ≤3 years (98.7% versus 84.7%), and no skin involvement proximal to the MCP joints (97% versus 60%). In the latter subgroup, removing Raynaud's phenomenon and sclerodactyly from the criteria reduced the sensitivity to 77% and 79%, respectively. Removing both sclerodactyly and puffy fingers reduced the sensitivity to 62%. The 2013 SSc classification criteria classify more SSc patients than the 1980 criteria. The improvement in sensitivity is most striking in those with lcSSc, especially those without skin involvement proximal to the MCP joints. The addition of Raynaud's phenomenon and puffy fingers to the 2013 criteria accounts for important gains in sensitivity. Copyright © 2015 by the American College of Rheumatology.

Combined use of SAR and optical data for environmental assessments around refugee camps in semiarid landscapes

NASA Astrophysics Data System (ADS)

Braun, A.; Hochschild, V.

2015-04-01

Over 15 million people were officially considered as refugees in the year 2012 and another 28 million as internally displaced people (IDPs). Natural disasters, climatic and environmental changes, violent regional conflicts and population growth force people to migrate in all parts of this world. This trend is likely to continue in the near future, as political instabilities increase and land degradation progresses. EO4HumEn aims at developing operational services to support humanitarian operations during crisis situations by means of dedicated geo-spatial information products derived from Earth observation and GIS data. The goal is to develop robust, automated methods of image analysis routines for population estimation, identification of potential groundwater extraction sites and monitoring the environmental impact of refugee/IDP camps. This study investigates the combination of satellite SAR data with optical sensors and elevation information for the assessment of the environmental conditions around refugee camps. In order to estimate their impact on land degradation, land cover classifications are required which target dynamic landscapes. We performed a land use / land cover classification based on a random forest algorithm and 39 input prediction rasters based on Landsat 8 data and additional layers generated from radar texture and elevation information. The overall accuracy was 92.9 %, while optical data had the highest impact on the final classification. By analysing all combinations of the three input datasets we additionally estimated their impact on single classification outcomes and land cover classes.

The CERAD Neuropsychological Assessment Battery Is Sensitive to Alcohol-Related Cognitive Deficiencies in Elderly Patients: A Retrospective Matched Case-Control Study.

PubMed

Kaufmann, Liane; Huber, Stefan; Mayer, Daniel; Moeller, Korbinian; Marksteiner, Josef

2018-04-01

Adverse effects of heavy drinking on cognition have frequently been reported. In the present study, we systematically examined for the first time whether clinical neuropsychological assessments may be sensitive to alcohol abuse in elderly patients with suspected minor neurocognitive disorder. A total of 144 elderly with and without alcohol abuse (each group n=72; mean age 66.7 years) were selected from a patient pool of n=738 by applying propensity score matching (a statistical method allowing to match participants in experimental and control group by balancing various covariates to reduce selection bias). Accordingly, study groups were almost perfectly matched regarding age, education, gender, and Mini Mental State Examination score. Neuropsychological performance was measured using the CERAD (Consortium to Establish a Registry for Alzheimer's Disease). Classification analyses (i.e., decision tree and boosted trees models) were conducted to examine whether CERAD variables or total score contributed to group classification. Decision tree models disclosed that groups could be reliably classified based on the CERAD variables "Word List Discriminability" (tapping verbal recognition memory, 64% classification accuracy) and "Trail Making Test A" (measuring visuo-motor speed, 59% classification accuracy). Boosted tree analyses further indicated the sensitivity of "Word List Recall" (measuring free verbal recall) for discriminating elderly with versus without a history of alcohol abuse. This indicates that specific CERAD variables seem to be sensitive to alcohol-related cognitive dysfunctions in elderly patients with suspected minor neurocognitive disorder. (JINS, 2018, 24, 360-371).

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

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

Alves, Vinicius M.; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599; Muratov, Eugene

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putativemore » sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. • Putative chemical hazards in the Scorecard database were found using our models.« less

40 CFR 164.3 - Scope and applicability of this part.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... 164.3 Section 164.3 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE... ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... registration, changes of classifications or hearings called by the Administrator; the provisions of subpart C...

40 CFR 164.22 - Contents of document setting forth objections.

Code of Federal Regulations, 2011 CFR

2011-07-01

... objections. 164.22 Section 164.22 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... registration, or change the classification of a pesticide, shall clearly and concisely set forth such...

40 CFR 164.3 - Scope and applicability of this part.

Code of Federal Regulations, 2011 CFR

2011-07-01

.... 164.3 Section 164.3 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE... ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... registration, changes of classifications or hearings called by the Administrator; the provisions of subpart C...

40 CFR 164.22 - Contents of document setting forth objections.

Code of Federal Regulations, 2010 CFR

2010-07-01

... objections. 164.22 Section 164.22 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... registration, or change the classification of a pesticide, shall clearly and concisely set forth such...

40 CFR 257.4 - Effective date.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 25 2011-07-01 2011-07-01 false Effective date. 257.4 Section 257.4 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES CRITERIA FOR CLASSIFICATION OF SOLID WASTE DISPOSAL FACILITIES AND PRACTICES Classification of Solid Waste Disposal Facilities...

40 CFR 257.4 - Effective date.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Effective date. 257.4 Section 257.4 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES CRITERIA FOR CLASSIFICATION OF SOLID WASTE DISPOSAL FACILITIES AND PRACTICES Classification of Solid Waste Disposal Facilities...

Can Preoperative Magnetic Resonance Imaging Predict the Reparability of Massive Rotator Cuff Tears?

PubMed

Kim, Jung Youn; Park, Ji Seon; Rhee, Yong Girl

2017-06-01

Numerous studies have shown preoperative fatty infiltration of rotator cuff muscles to be strongly negatively correlated with the successful repair of massive rotator cuff tears (RCTs). To assess the association between factors identified on preoperative magnetic resonance imaging (MRI), especially infraspinatus fatty infiltration, and the reparability of massive RCTs. Case-control study; Level of evidence, 3. We analyzed a total of 105 patients with massive RCTs for whom MRI was performed ≤6 months before arthroscopic procedures. The mean age of the patients was 62.7 years (range, 46-83 years), and 46 were men. Among them, complete repair was possible in 50 patients (48%) and not possible in 55 patients (52%). The tangent sign, fatty infiltration of the rotator cuff, and Patte classification were evaluated as predictors of reparability. Using the receiver operating characteristic curve and the area under the curve (AUC), the prediction accuracy of each variable and combinations of variables were measured. Reparability was associated with fatty infiltration of the supraspinatus ( P = .0045) and infraspinatus ( P < .001) muscles, the tangent sign ( P = .0033), and the Patte classification ( P < .001) but not with fatty infiltration of the subscapularis and teres minor ( P = .425 and .132, respectively). The cut-off values for supraspinatus and infraspinatus fatty infiltration were grade >3 and grade >2, respectively. The examination of single variables revealed that infraspinatus fatty infiltration showed the highest AUC value (0.812; sensitivity: 0.86; specificity: 0.76), while the tangent sign showed the lowest AUC value (0.626; sensitivity: 0.38; specificity: 0.87). Among 2-variable combinations, the combination of infraspinatus fatty infiltration and the Patte classification showed the highest AUC value (0.874; sensitivity: 0.54; specificity: 0.96). The combination of 4 variables, that is, infraspinatus and supraspinatus fatty infiltration, the tangent sign, and the Patte classification, had an AUC of 0.866 (sensitivity: 0.28; specificity: 0.98), which was lower than the highest AUC value (0.874; sensitivity: 0.54; specificity: 0.96) among the 2-variable combinations. The tangent sign or Patte classification alone was not a predictive indicator of the reparability of massive RCTs. Among single variables, infraspinatus fatty infiltration was the most effective in predicting reparability, while the combination of Goutallier classification <3 of the infraspinatus and Patte classification ≤2 of the rotator cuff muscles was the most predictive among the combinations of variables. This information may help predict the reparability of massive RCTs.

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

PubMed

Mumtaz, Wajid; Ali, Syed Saad Azhar; Yasin, Mohd Azhar Mohd; Malik, Aamir Saeed

2018-02-01

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95% and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6% and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9% and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.

Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography.

PubMed

Ruiz Hidalgo, Irene; Rodriguez, Pablo; Rozema, Jos J; Ní Dhubhghaill, Sorcha; Zakaria, Nadia; Tassignon, Marie-José; Koppen, Carina

2016-06-01

To evaluate the performance of a support vector machine algorithm that automatically and objectively identifies corneal patterns based on a combination of 22 parameters obtained from Pentacam measurements and to compare this method with other known keratoconus (KC) classification methods. Pentacam data from 860 eyes were included in the study and divided into 5 groups: 454 KC, 67 forme fruste (FF), 28 astigmatic, 117 after refractive surgery (PR), and 194 normal eyes (N). Twenty-two parameters were used for classification using a support vector machine algorithm developed in Weka, a machine-learning computer software. The cross-validation accuracy for 3 different classification tasks (KC vs. N, FF vs. N and all 5 groups) was calculated and compared with other known classification methods. The accuracy achieved in the KC versus N discrimination task was 98.9%, with 99.1% sensitivity and 98.5% specificity for KC detection. The accuracy in the FF versus N task was 93.1%, with 79.1% sensitivity and 97.9% specificity for the FF discrimination. Finally, for the 5-groups classification, the accuracy was 88.8%, with a weighted average sensitivity of 89.0% and specificity of 95.2%. Despite using the strictest definition for FF KC, the present study obtained comparable or better results than the single-parameter methods and indices reported in the literature. In some cases, direct comparisons with the literature were not possible because of differences in the compositions and definitions of the study groups, especially the FF KC.

Sensitivity and specificity of radiographic methods for predicting insertion torque of dental implants.

PubMed

Cortes, Arthur Rodriguez Gonzalez; Eimar, Hazem; Barbosa, Jorge de Sá; Costa, Claudio; Arita, Emiko Saito; Tamimi, Faleh

2015-05-01

Subjective radiographic classifications of alveolar bone have been proposed and correlated with implant insertion torque (IT). The present diagnostic study aims to identify quantitative bone features influencing IT and to use these findings to develop an objective radiographic classification for predicting IT. Demographics, panoramic radiographs (taken at the beginning of dental treatment), and cone-beam computed tomographic scans (taken for implant surgical planning) of 25 patients receiving 31 implants were analyzed. Bone samples retrieved from implant sites were assessed with dual x-ray absorptiometry, microcomputed tomography, and histology. Odds ratio, sensitivity, and specificity of all variables to predict high peak IT were assessed. A ridge cortical thickness >0.75 mm and a normal appearance of the inferior mandibular cortex were the most sensitive variables for predicting high peak IT (87.5% and 75%, respectively). A classification based on the combination of both variables presented high sensitivity (90.9%) and specificity (100%) for predicting IT. Within the limitations of this study, the results suggest that it is possible to predict IT accurately based on radiographic findings of the patient. This could be useful in the treatment plan of immediate loading cases.

Application of PCA and SIMCA statistical analysis of FT-IR spectra for the classification and identification of different slag types with environmental origin.

PubMed

Stumpe, B; Engel, T; Steinweg, B; Marschner, B

2012-04-03

In the past, different slag materials were often used for landscaping and construction purposes or simply dumped. Nowadays German environmental laws strictly control the use of slags, but there is still a remaining part of 35% which is uncontrolled dumped in landfills. Since some slags have high heavy metal contents and different slag types have typical chemical and physical properties that will influence the risk potential and other characteristics of the deposits, an identification of the slag types is needed. We developed a FT-IR-based statistical method to identify different slags classes. Slags samples were collected at different sites throughout various cities within the industrial Ruhr area. Then, spectra of 35 samples from four different slags classes, ladle furnace (LF), blast furnace (BF), oxygen furnace steel (OF), and zinc furnace slags (ZF), were determined in the mid-infrared region (4000-400 cm(-1)). The spectra data sets were subject to statistical classification methods for the separation of separate spectral data of different slag classes. Principal component analysis (PCA) models for each slag class were developed and further used for soft independent modeling of class analogy (SIMCA). Precise classification of slag samples into four different slag classes were achieved using two different SIMCA models stepwise. At first, SIMCA 1 was used for classification of ZF as well as OF slags over the total spectral range. If no correct classification was found, then the spectrum was analyzed with SIMCA 2 at reduced wavenumbers for the classification of LF as well as BF spectra. As a result, we provide a time- and cost-efficient method based on FT-IR spectroscopy for processing and identifying large numbers of environmental slag samples.

Validation of Case Finding Algorithms for Hepatocellular Cancer From Administrative Data and Electronic Health Records Using Natural Language Processing.

PubMed

Sada, Yvonne; Hou, Jason; Richardson, Peter; El-Serag, Hashem; Davila, Jessica

2016-02-01

Accurate identification of hepatocellular cancer (HCC) cases from automated data is needed for efficient and valid quality improvement initiatives and research. We validated HCC International Classification of Diseases, 9th Revision (ICD-9) codes, and evaluated whether natural language processing by the Automated Retrieval Console (ARC) for document classification improves HCC identification. We identified a cohort of patients with ICD-9 codes for HCC during 2005-2010 from Veterans Affairs administrative data. Pathology and radiology reports were reviewed to confirm HCC. The positive predictive value (PPV), sensitivity, and specificity of ICD-9 codes were calculated. A split validation study of pathology and radiology reports was performed to develop and validate ARC algorithms. Reports were manually classified as diagnostic of HCC or not. ARC generated document classification algorithms using the Clinical Text Analysis and Knowledge Extraction System. ARC performance was compared with manual classification. PPV, sensitivity, and specificity of ARC were calculated. A total of 1138 patients with HCC were identified by ICD-9 codes. On the basis of manual review, 773 had HCC. The HCC ICD-9 code algorithm had a PPV of 0.67, sensitivity of 0.95, and specificity of 0.93. For a random subset of 619 patients, we identified 471 pathology reports for 323 patients and 943 radiology reports for 557 patients. The pathology ARC algorithm had PPV of 0.96, sensitivity of 0.96, and specificity of 0.97. The radiology ARC algorithm had PPV of 0.75, sensitivity of 0.94, and specificity of 0.68. A combined approach of ICD-9 codes and natural language processing of pathology and radiology reports improves HCC case identification in automated data.

Prospective identification of adolescent suicide ideation using classification tree analysis: Models for community-based screening.

PubMed

Hill, Ryan M; Oosterhoff, Benjamin; Kaplow, Julie B

2017-07-01

Although a large number of risk markers for suicide ideation have been identified, little guidance has been provided to prospectively identify adolescents at risk for suicide ideation within community settings. The current study addressed this gap in the literature by utilizing classification tree analysis (CTA) to provide a decision-making model for screening adolescents at risk for suicide ideation. Participants were N = 4,799 youth (Mage = 16.15 years, SD = 1.63) who completed both Waves 1 and 2 of the National Longitudinal Study of Adolescent to Adult Health. CTA was used to generate a series of decision rules for identifying adolescents at risk for reporting suicide ideation at Wave 2. Findings revealed 3 distinct solutions with varying sensitivity and specificity for identifying adolescents who reported suicide ideation. Sensitivity of the classification trees ranged from 44.6% to 77.6%. The tree with greatest specificity and lowest sensitivity was based on a history of suicide ideation. The tree with moderate sensitivity and high specificity was based on depressive symptoms, suicide attempts or suicide among family and friends, and social support. The most sensitive but least specific tree utilized these factors and gender, ethnicity, hours of sleep, school-related factors, and future orientation. These classification trees offer community organizations options for instituting large-scale screenings for suicide ideation risk depending on the available resources and modality of services to be provided. This study provides a theoretically and empirically driven model for prospectively identifying adolescents at risk for suicide ideation and has implications for preventive interventions among at-risk youth. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Articular cartilage degeneration classification by means of high-frequency ultrasound.

PubMed

Männicke, N; Schöne, M; Oelze, M; Raum, K

2014-10-01

To date only single ultrasound parameters were regarded in statistical analyses to characterize osteoarthritic changes in articular cartilage and the potential benefit of using parameter combinations for characterization remains unclear. Therefore, the aim of this work was to utilize feature selection and classification of a Mankin subset score (i.e., cartilage surface and cell sub-scores) using ultrasound-based parameter pairs and investigate both classification accuracy and the sensitivity towards different degeneration stages. 40 punch biopsies of human cartilage were previously scanned ex vivo with a 40-MHz transducer. Ultrasound-based surface parameters, as well as backscatter and envelope statistics parameters were available. Logistic regression was performed with each unique US parameter pair as predictor and different degeneration stages as response variables. The best ultrasound-based parameter pair for each Mankin subset score value was assessed by highest classification accuracy and utilized in receiver operating characteristics (ROC) analysis. The classifications discriminating between early degenerations yielded area under the ROC curve (AUC) values of 0.94-0.99 (mean ± SD: 0.97 ± 0.03). In contrast, classifications among higher Mankin subset scores resulted in lower AUC values: 0.75-0.91 (mean ± SD: 0.84 ± 0.08). Variable sensitivities of the different ultrasound features were observed with respect to different degeneration stages. Our results strongly suggest that combinations of high-frequency ultrasound-based parameters exhibit potential to characterize different, particularly very early, degeneration stages of hyaline cartilage. Variable sensitivities towards different degeneration stages suggest that a concurrent estimation of multiple ultrasound-based parameters is diagnostically valuable. In-vivo application of the present findings is conceivable in both minimally invasive arthroscopic ultrasound and high-frequency transcutaneous ultrasound. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).

Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage under clinical imaging conditions.

PubMed

Lukas, Vanessa A; Fishbein, Kenneth W; Reiter, David A; Lin, Ping-Chang; Schneider, Erika; Spencer, Richard G

2015-07-01

To evaluate the sensitivity and specificity of classification of pathomimetically degraded bovine nasal cartilage at 3 Tesla and 37°C using univariate MRI measurements of both pure parameter values and intensities of parameter-weighted images. Pre- and posttrypsin degradation values of T1 , T2 , T2 *, magnetization transfer ratio (MTR), and apparent diffusion coefficient (ADC), and corresponding weighted images, were analyzed. Classification based on the Euclidean distance was performed and the quality of classification was assessed through sensitivity, specificity and accuracy (ACC). The classifiers with the highest accuracy values were ADC (ACC = 0.82 ± 0.06), MTR (ACC = 0.78 ± 0.06), T1 (ACC = 0.99 ± 0.01), T2 derived from a three-dimensional (3D) spin-echo sequence (ACC = 0.74 ± 0.05), and T2 derived from a 2D spin-echo sequence (ACC = 0.77 ± 0.06), along with two of the diffusion-weighted signal intensities (b = 333 s/mm(2) : ACC = 0.80 ± 0.05; b = 666 s/mm(2) : ACC = 0.85 ± 0.04). In particular, T1 values differed substantially between the groups, resulting in atypically high classification accuracy. The second-best classifier, diffusion weighting with b = 666 s/mm(2) , as well as all other parameters evaluated, exhibited substantial overlap between pre- and postdegradation groups, resulting in decreased accuracies. Classification according to T1 values showed excellent test characteristics (ACC = 0.99), with several other parameters also showing reasonable performance (ACC > 0.70). Of these, diffusion weighting is particularly promising as a potentially practical clinical modality. As in previous work, we again find that highly statistically significant group mean differences do not necessarily translate into accurate clinical classification rules. © 2014 Wiley Periodicals, Inc.

Can the Ni classification of vessels predict neoplasia? A systematic review and meta-analysis.

PubMed

Mehlum, Camilla S; Rosenberg, Tine; Dyrvig, Anne-Kirstine; Groentved, Aagot Moeller; Kjaergaard, Thomas; Godballe, Christian

2018-01-01

The Ni classification of vascular change from 2011 is well documented for evaluating pharyngeal and laryngeal lesions, primarily focusing on cancer. In the planning of surgery it may be more relevant to differentiate neoplasia from non-neoplasia. We aimed to evaluate the ability of the Ni classification to predict laryngeal or hypopharyngeal neoplasia and to investigate if a changed cutoff value would support the recent European Laryngological Society (ELS) proposal of perpendicular vascular changes as indicative of neoplasia. PubMed, Embase, Cochrane, and Scopus databases. A systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. We systematically searched for publications from 2011 until 2016. All retrieved studies were reviewed and qualitatively assessed. The pooled sensitivity and specificity of the Ni classification with two different cutoffs were calculated, and bubble and summary receiver operating characteristics plots were created. The combined sensitivity of five studies (n = 687) with Ni type IV-V defined as test-positive was 0.89 (95% confidence interval [CI]: 0.76-0.95), and specificity was 0.82 (95% CI: 0.72-0.89). The equivalent combined sensitivity of four studies (n = 624) with Ni type V defined as test-positive was 0.82 (95% CI: 0.75-0.87), and specificity was 0.93 (95% CI: 0.82-0.97). The diagnostic accuracy of the Ni classification in predicting neoplasia was high, without significant difference between the two analyzed cutoff values. Implementation of the proposed ELS classification of vascular changes seems reasonable from a clinical perspective, with comparable accuracy. Attention must be drawn to the accompanying risk of exposing patients to unnecessary surgery. Laryngoscope, 128:168-176, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Natural personal care products-analysis of ingredient lists and legal situation.

PubMed

Klaschka, Ursula

2016-01-01

Many natural substances are classified as dangerous substances according to the European regulation on classification and labelling. Are they used in natural personal care products today? One hundred ingredient lists were analyzed to find this out. All products with natural substances contained dangerous natural substances or they contained natural substances, for which the information about their classification as dangerous substances is not available. 54 natural substances quoted in the ingredient lists were found to be classified, with 37 substances being classified due to hazardous effects for skin and eyes. However, the most frequently used natural substances are not classified as dangerous. Natural substances are multi-constituent compounds, leading to two main problems in personal care products: the potential interactions of a multitude of substances and the fact that dangerous constituents are not disclosed in the ingredient lists. For example, the fragrance allergens citral, farnesol, limonene, and linalool are frequent components of the natural substances employed. In addition, 82 products listed allergenic fragrance ingredients as single substances in their ingredient lists. Recommendations for sensitive skin in a product's name do not imply that the '26 fragrance allergens' are omitted. Furthermore, 80 products listed 'parfum'/'aroma', and 50 products listed ethanol. The data show that the loopholes for natural substances and for personal care products in the present European chemical legislation (e.g. the exception for classification and labelling of cosmetic products and the exception for information transfer in the supply chain) are not in line with an adequate consumer and environmental protection.

A vegetational and ecological resource analysis from space and high flight photography

NASA Technical Reports Server (NTRS)

Poulton, C. E.; Faulkner, D. P.; Schrumpf, B. J.

1970-01-01

A hierarchial classification of vegetation and related resources is considered that is applicable to convert remote sensing data in space and aerial synoptic photography. The numerical symbolization provides for three levels of vegetational classification and three levels of classification of environmental features associated with each vegetational class. It is shown that synoptic space photography accurately projects how urban sprawl affects agricultural land use areas and ecological resources.

Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis.

PubMed

Lee, Ga-Young; Kim, Jeonghun; Kim, Ju Han; Kim, Kiwoong; Seong, Joon-Kyung

2014-01-01

Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.

Automating document classification for the Immune Epitope Database

PubMed Central

Wang, Peng; Morgan, Alexander A; Zhang, Qing; Sette, Alessandro; Peters, Bjoern

2007-01-01

Background The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose. Results We here report our experience in automating this process using Naïve Bayes classifiers trained on 20,910 abstracts classified by domain experts. Improvements on the basic classifier performance were made by a) utilizing information stored in PubMed beyond the abstract itself b) applying standard feature selection criteria and c) extracting domain specific feature patterns that e.g. identify peptides sequences. We have implemented the classifier into the curation process determining if abstracts are clearly relevant, clearly irrelevant, or if no certain classification can be made, in which case the abstracts are manually classified. Testing this classification scheme on an independent dataset, we achieve 95% sensitivity and specificity in the 51.1% of abstracts that were automatically classified. Conclusion By implementing text classification, we have sped up the reference selection process without sacrificing sensitivity or specificity of the human expert classification. This study provides both practical recommendations for users of text classification tools, as well as a large dataset which can serve as a benchmark for tool developers. PMID:17655769

A machine learning approach to galaxy-LSS classification - I. Imprints on halo merger trees

NASA Astrophysics Data System (ADS)

Hui, Jianan; Aragon, Miguel; Cui, Xinping; Flegal, James M.

2018-04-01

The cosmic web plays a major role in the formation and evolution of galaxies and defines, to a large extent, their properties. However, the relation between galaxies and environment is still not well understood. Here, we present a machine learning approach to study imprints of environmental effects on the mass assembly of haloes. We present a galaxy-LSS machine learning classifier based on galaxy properties sensitive to the environment. We then use the classifier to assess the relevance of each property. Correlations between galaxy properties and their cosmic environment can be used to predict galaxy membership to void/wall or filament/cluster with an accuracy of 93 per cent. Our study unveils environmental information encoded in properties of haloes not normally considered directly dependent on the cosmic environment such as merger history and complexity. Understanding the physical mechanism by which the cosmic web is imprinted in a halo can lead to significant improvements in galaxy formation models. This is accomplished by extracting features from galaxy properties and merger trees, computing feature scores for each feature and then applying support vector machine (SVM) to different feature sets. To this end, we have discovered that the shape and depth of the merger tree, formation time, and density of the galaxy are strongly associated with the cosmic environment. We describe a significant improvement in the original classification algorithm by performing LU decomposition of the distance matrix computed by the feature vectors and then using the output of the decomposition as input vectors for SVM.

Development of Tier 1 screening tool for soil and groundwater vulnerability assessment in Korea using classification algorithm in a neural network

NASA Astrophysics Data System (ADS)

Shin, K. H.; Kim, K. H.; Ki, S. J.; Lee, H. G.

2017-12-01

The vulnerability assessment tool at a Tier 1 level, although not often used for regulatory purposes, helps establish pollution prevention and management strategies in the areas of potential environmental concern such as soil and ground water. In this study, the Neural Network Pattern Recognition Tool embedded in MATLAB was used to allow the initial screening of soil and groundwater pollution based on data compiled across about 1000 previously contaminated sites in Korea. The input variables included a series of parameters which were tightly related to downward movement of water and contaminants through soil and ground water, whereas multiple classes were assigned to the sum of concentrations of major pollutants detected. Results showed that in accordance with diverse pollution indices for soil and ground water, pollution levels in both media were strongly modulated by site-specific characteristics such as intrinsic soil and other geologic properties, in addition to pollution sources and rainfall. However, classification accuracy was very sensitive to the number of classes defined as well as the types of the variables incorporated, requiring careful selection of input variables and output categories. Therefore, we believe that the proposed methodology is used not only to modify existing pollution indices so that they are more suitable for addressing local vulnerability, but also to develop a unique assessment tool to support decision making based on locally or nationally available data. This study was funded by a grant from the GAIA project(2016000560002), Korea Environmental Industry & Technology Institute, Republic of Korea.

40 CFR 51.150 - Classification of regions for episode plans.

Code of Federal Regulations, 2010 CFR

2010-07-01

... plans. 51.150 Section 51.150 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF IMPLEMENTATION PLANS Prevention of Air Pollution Emergency Episodes § 51.150 Classification of regions for episode plans. (a) This section...

Strong influence of variable treatment on the performance of numerically defined ecological regions.

PubMed

Snelder, Ton; Lehmann, Anthony; Lamouroux, Nicolas; Leathwick, John; Allenbach, Karin

2009-10-01

Numerical clustering has frequently been used to define hierarchically organized ecological regionalizations, but there has been little robust evaluation of their performance (i.e., the degree to which regions discriminate areas with similar ecological character). In this study we investigated the effect of the weighting and treatment of input variables on the performance of regionalizations defined by agglomerative clustering across a range of hierarchical levels. For this purpose, we developed three ecological regionalizations of Switzerland of increasing complexity using agglomerative clustering. Environmental data for our analysis were drawn from a 400 m grid and consisted of estimates of 11 environmental variables for each grid cell describing climate, topography and lithology. Regionalization 1 was defined from the environmental variables which were given equal weights. We used the same variables in Regionalization 2 but weighted and transformed them on the basis of a dissimilarity model that was fitted to land cover composition data derived for a random sample of cells from interpretation of aerial photographs. Regionalization 3 was a further two-stage development of Regionalization 2 where specific classifications, also weighted and transformed using dissimilarity models, were applied to 25 small scale "sub-domains" defined by Regionalization 2. Performance was assessed in terms of the discrimination of land cover composition for an independent set of sites using classification strength (CS), which measured the similarity of land cover composition within classes and the dissimilarity between classes. Regionalization 2 performed significantly better than Regionalization 1, but the largest gains in performance, compared to Regionalization 1, occurred at coarse hierarchical levels (i.e., CS did not increase significantly beyond the 25-region level). Regionalization 3 performed better than Regionalization 2 beyond the 25-region level and CS values continued to increase to the 95-region level. The results show that the performance of regionalizations defined by agglomerative clustering are sensitive to variable weighting and transformation. We conclude that large gains in performance can be achieved by training classifications using dissimilarity models. However, these gains are restricted to a narrow range of hierarchical levels because agglomerative clustering is unable to represent the variation in importance of variables at different spatial scales. We suggest that further advances in the numerical definition of hierarchically organized ecological regionalizations will be possible with techniques developed in the field of statistical modeling of the distribution of community composition.

An EEG-based functional connectivity measure for automatic detection of alcohol use disorder.

PubMed

Mumtaz, Wajid; Saad, Mohamad Naufal B Mohamad; Kamel, Nidal; Ali, Syed Saad Azhar; Malik, Aamir Saeed

2018-01-01

The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics. In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used. The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95. The SL features could be utilized as objective markers to screen the AUD patients and healthy controls. Copyright © 2017 Elsevier B.V. All rights reserved.

Crowdsourcing as a novel technique for retinal fundus photography classification: analysis of images in the EPIC Norfolk cohort on behalf of the UK Biobank Eye and Vision Consortium.

PubMed

Mitry, Danny; Peto, Tunde; Hayat, Shabina; Morgan, James E; Khaw, Kay-Tee; Foster, Paul J

2013-01-01

Crowdsourcing is the process of outsourcing numerous tasks to many untrained individuals. Our aim was to assess the performance and repeatability of crowdsourcing for the classification of retinal fundus photography. One hundred retinal fundus photograph images with pre-determined disease criteria were selected by experts from a large cohort study. After reading brief instructions and an example classification, we requested that knowledge workers (KWs) from a crowdsourcing platform classified each image as normal or abnormal with grades of severity. Each image was classified 20 times by different KWs. Four study designs were examined to assess the effect of varying incentive and KW experience in classification accuracy. All study designs were conducted twice to examine repeatability. Performance was assessed by comparing the sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Without restriction on eligible participants, two thousand classifications of 100 images were received in under 24 hours at minimal cost. In trial 1 all study designs had an AUC (95%CI) of 0.701(0.680-0.721) or greater for classification of normal/abnormal. In trial 1, the highest AUC (95%CI) for normal/abnormal classification was 0.757 (0.738-0.776) for KWs with moderate experience. Comparable results were observed in trial 2. In trial 1, between 64-86% of any abnormal image was correctly classified by over half of all KWs. In trial 2, this ranged between 74-97%. Sensitivity was ≥ 96% for normal versus severely abnormal detections across all trials. Sensitivity for normal versus mildly abnormal varied between 61-79% across trials. With minimal training, crowdsourcing represents an accurate, rapid and cost-effective method of retinal image analysis which demonstrates good repeatability. Larger studies with more comprehensive participant training are needed to explore the utility of this compelling technique in large scale medical image analysis.

Vector analysis of postcardiotomy behavioral phenomena.

PubMed

Caston, J C; Miller, W C; Felber, W J

1975-04-01

The classification of postcardiotomy behavioral phenomena in Figure 1 is proposed for use as a clinical instrument to analyze etiological determinants. The utilization of a vector analysis analogy inherently denies absolutism. Classifications A-P are presented as prototypes of certain ratio imbalances of the metabolic, hemodynamic, environmental, and psychic vectors. Such a system allows for change from one type to another according to the individuality of the patient and the highly specific changes in his clinical presentation. A vector analysis also allows for infinite intermediary ratio imbalances between classification types as a function of time. Thus, postcardiotomy behavioral phenomena could be viewed as the vector summation of hemodynamic, metabolic, environmental, and psychic processes at a given point in time. Elaboration of unknown determinants in this complex syndrome appears to be task for the future.

FACTORS AFFECTING SENSITIVITY OF CHEMICAL AND ECOLOGICAL RESPONSES OF MARINE EMBAYMEMTS TO NITROGEN LOADING

EPA Science Inventory

This paper summarizes an ongoing examination of the primary factors that affect sensitivity of marine embayment responses to nitrogen loading. Included is a discussion of two methods for using these factors: classification of embayments into discrete sensitivity classes and norma...

Fines classification based on sensitivity to pore-fluid chemistry

USGS Publications Warehouse

Jang, Junbong; Santamarina, J. Carlos

2016-01-01

The 75-μm particle size is used to discriminate between fine and coarse grains. Further analysis of fine grains is typically based on the plasticity chart. Whereas pore-fluid-chemistry-dependent soil response is a salient and distinguishing characteristic of fine grains, pore-fluid chemistry is not addressed in current classification systems. Liquid limits obtained with electrically contrasting pore fluids (deionized water, 2-M NaCl brine, and kerosene) are combined to define the soil “electrical sensitivity.” Liquid limit and electrical sensitivity can be effectively used to classify fine grains according to their fluid-soil response into no-, low-, intermediate-, or high-plasticity fine grains of low, intermediate, or high electrical sensitivity. The proposed methodology benefits from the accumulated experience with liquid limit in the field and addresses the needs of a broader range of geotechnical engineering problems.

Improving Environmental Model Calibration and Prediction

DTIC Science & Technology

2011-01-18

REPORT Final Report - Improving Environmental Model Calibration and Prediction 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: First, we have continued to...develop tools for efficient global optimization of environmental models. Our algorithms are hybrid algorithms that combine evolutionary strategies...toward practical hybrid optimization tools for environmental models. 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 18-01-2011 13

SELDI-TOF-MS proteomic profiling of serum, urine, and amniotic fluid in neural tube defects.

PubMed

Liu, Zhenjiang; Yuan, Zhengwei; Zhao, Qun

2014-01-01

Neural tube defects (NTDs) are common birth defects, whose specific biomarkers are needed. The purpose of this pilot study is to determine whether protein profiling in NTD-mothers differ from normal controls using SELDI-TOF-MS. ProteinChip Biomarker System was used to evaluate 82 maternal serum samples, 78 urine samples and 76 amniotic fluid samples. The validity of classification tree was then challenged with a blind test set including another 20 NTD-mothers and 18 controls in serum samples, and another 19 NTD-mothers and 17 controls in urine samples, and another 20 NTD-mothers and 17 controls in amniotic fluid samples. Eight proteins detected in serum samples were up-regulated and four proteins were down-regulated in the NTD group. Four proteins detected in urine samples were up-regulated and one protein was down-regulated in the NTD group. Six proteins detected in amniotic fluid samples were up-regulated and one protein was down-regulated in the NTD group. The classification tree for serum samples separated NTDs from healthy individuals, achieving a sensitivity of 91% and a specificity of 97% in the training set, and achieving a sensitivity of 90% and a specificity of 97% and a positive predictive value of 95% in the test set. The classification tree for urine samples separated NTDs from controls, achieving a sensitivity of 95% and a specificity of 94% in the training set, and achieving a sensitivity of 89% and a specificity of 82% and a positive predictive value of 85% in the test set. The classification tree for amniotic fluid samples separated NTDs from controls, achieving a sensitivity of 93% and a specificity of 89% in the training set, and achieving a sensitivity of 90% and a specificity of 88% and a positive predictive value of 90% in the test set. These suggest that SELDI-TOF-MS is an additional method for NTDs pregnancies detection.

A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification.

PubMed

Krawczyk, Bartosz; Schaefer, Gerald; Woźniak, Michał

2015-11-01

Early recognition of breast cancer, the most commonly diagnosed form of cancer in women, is of crucial importance, given that it leads to significantly improved chances of survival. Medical thermography, which uses an infrared camera for thermal imaging, has been demonstrated as a particularly useful technique for early diagnosis, because it detects smaller tumors than the standard modality of mammography. In this paper, we analyse breast thermograms by extracting features describing bilateral symmetries between the two breast areas, and present a classification system for decision making. Clearly, the costs associated with missing a cancer case are much higher than those for mislabelling a benign case. At the same time, datasets contain significantly fewer malignant cases than benign ones. Standard classification approaches fail to consider either of these aspects. In this paper, we introduce a hybrid cost-sensitive classifier ensemble to address this challenging problem. Our approach entails a pool of cost-sensitive decision trees which assign a higher misclassification cost to the malignant class, thereby boosting its recognition rate. A genetic algorithm is employed for simultaneous feature selection and classifier fusion. As an optimisation criterion, we use a combination of misclassification cost and diversity to achieve both a high sensitivity and a heterogeneous ensemble. Furthermore, we prune our ensemble by discarding classifiers that contribute minimally to the decision making. For a challenging dataset of about 150 thermograms, our approach achieves an excellent sensitivity of 83.10%, while maintaining a high specificity of 89.44%. This not only signifies improved recognition of malignant cases, it also statistically outperforms other state-of-the-art algorithms designed for imbalanced classification, and hence provides an effective approach for analysing breast thermograms. Our proposed hybrid cost-sensitive ensemble can facilitate a highly accurate early diagnostic of breast cancer based on thermogram features. It overcomes the difficulties posed by the imbalanced distribution of patients in the two analysed groups. Copyright © 2015 Elsevier B.V. All rights reserved.

On the classification of mixed floating pollutants on the Yellow Sea of China by using a quad-polarized SAR image

NASA Astrophysics Data System (ADS)

Wang, Xiaochen; Shao, Yun; Tian, Wei; Li, Kun

2018-06-01

This study explored different methodologies using a C-band RADARSAT-2 quad-polarized Synthetic Aperture Radar (SAR) image located over China's Yellow Sea to investigate polarization decomposition parameters for identifying mixed floating pollutants from a complex ocean background. It was found that solitary polarization decomposition did not meet the demand for detecting and classifying multiple floating pollutants, even after applying a polarized SAR image. Furthermore, considering that Yamaguchi decomposition is sensitive to vegetation and the algal variety Enteromorpha prolifera, while H/A/alpha decomposition is sensitive to oil spills, a combination of parameters which was deduced from these two decompositions was proposed for marine environmental monitoring of mixed floating sea surface pollutants. A combination of volume scattering, surface scattering, and scattering entropy was the best indicator for classifying mixed floating pollutants from a complex ocean background. The Kappa coefficients for Enteromorpha prolifera and oil spills were 0.7514 and 0.8470, respectively, evidence that the composite polarized parameters based on quad-polarized SAR imagery proposed in this research is an effective monitoring method for complex marine pollution.

78 FR 17227 - Notice of Intent To Amend the Snake River Resource Management Plan for the Pinedale Field Office...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-20

... and Prepare an Associated Environmental Assessment; and Notice of Realty Action: Classification and..., classification, or sale must be received by the BLM at the address below no later than May 6, 2013. The date(s...

40 CFR 52.1771 - Classification of regions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 4 2013-07-01 2013-07-01 false Classification of regions. 52.1771 Section 52.1771 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS... III III III III Eastern Piedmont Intrastate I III III III III Northern Coastal Intrastate I III III...

40 CFR 52.1771 - Classification of regions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 4 2014-07-01 2014-07-01 false Classification of regions. 52.1771 Section 52.1771 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS... III III III III Eastern Piedmont Intrastate I III III III III Northern Coastal Intrastate I III III...

40 CFR 52.1771 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 4 2011-07-01 2011-07-01 false Classification of regions. 52.1771 Section 52.1771 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS... III III III III Eastern Piedmont Intrastate I III III III III Northern Coastal Intrastate I III III...

40 CFR 52.1771 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 4 2010-07-01 2010-07-01 false Classification of regions. 52.1771 Section 52.1771 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS... III III III III Eastern Piedmont Intrastate I III III III III Northern Coastal Intrastate I III III...

40 CFR 52.1771 - Classification of regions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 4 2012-07-01 2012-07-01 false Classification of regions. 52.1771 Section 52.1771 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS... III III III III Eastern Piedmont Intrastate I III III III III Northern Coastal Intrastate I III III...

40 CFR 164.80 - Order of proceeding and burden of proof.

Code of Federal Regulations, 2011 CFR

2011-07-01

... proof. 164.80 Section 164.80 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... and burden of proof. (a) At the hearing, the proponent of cancellation or change in classification has...

40 CFR 51.1102 - Classification and nonattainment area planning provisions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 2 2014-07-01 2014-07-01 false Classification and nonattainment area planning provisions. 51.1102 Section 51.1102 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Provisions for Implementation of the 2008 Ozone National Ambient Air Quality Standards § 51.1102...

40 CFR 51.1102 - Classification and nonattainment area planning provisions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 2 2013-07-01 2013-07-01 false Classification and nonattainment area planning provisions. 51.1102 Section 51.1102 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Provisions for Implementation of the 2008 Ozone National Ambient Air Quality Standards § 51.1102...

40 CFR 51.1102 - Classification and nonattainment area planning provisions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 2 2012-07-01 2012-07-01 false Classification and nonattainment area planning provisions. 51.1102 Section 51.1102 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Provisions for Implementation of the 2008 Ozone National Ambient Air Quality Standards § 51.1102...

Stream Classification Tool User Manual: For Use in Applications in Hydropower-Related Evironmental Mitigation

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

McManamay, Ryan A.; Troia, Matthew J.; DeRolph, Christopher R.

Stream classifications are an inventory of different types of streams. Classifications help us explore similarities and differences among different types of streams, make inferences regarding stream ecosystem behavior, and communicate the complexities of ecosystems. We developed a nested, layered, and spatially contiguous stream classification to characterize the biophysical settings of stream reaches within the Eastern United States (~ 900,000 reaches). The classification is composed of five natural characteristics (hydrology, temperature, size, confinement, and substrate) along with several disturbance regime layers, and each was selected because of their relevance to hydropower mitigation. We developed the classification at the stream reach levelmore » using the National Hydrography Dataset Plus Version 1 (1:100k scale). The stream classification is useful to environmental mitigation for hydropower dams in multiple ways. First, it creates efficiency in the regulatory process by creating an objective and data-rich means to address meaningful mitigation actions. Secondly, the SCT addresses data gaps as it quickly provides an inventory of hydrology, temperature, morphology, and ecological communities for the immediate project area, but also surrounding streams. This includes identifying potential reference streams as those that are proximate to the hydropower facility and fall within the same class. These streams can potentially be used to identify ideal environmental conditions or identify desired ecological communities. In doing so, the stream provides some context for how streams may function, respond to dam regulation, and an overview of specific mitigation needs. Herein, we describe the methodology in developing each stream classification layer and provide a tutorial to guide applications of the classification (and associated data) in regulatory settings, such as hydropower (re)licensing.« less

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

PubMed

Cho, Youngsang; Seong, Joon-Kyung; Jeong, Yong; Shin, Sung Yong

2012-02-01

Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

NASA Astrophysics Data System (ADS)

Omenzetter, Piotr; de Lautour, Oliver R.

2010-04-01

Developed for studying long, periodic records of various measured quantities, time series analysis methods are inherently suited and offer interesting possibilities for Structural Health Monitoring (SHM) applications. However, their use in SHM can still be regarded as an emerging application and deserves more studies. In this research, Autoregressive (AR) models were used to fit experimental acceleration time histories from two experimental structural systems, a 3- storey bookshelf-type laboratory structure and the ASCE Phase II SHM Benchmark Structure, in healthy and several damaged states. The coefficients of the AR models were chosen as damage sensitive features. Preliminary visual inspection of the large, multidimensional sets of AR coefficients to check the presence of clusters corresponding to different damage severities was achieved using Sammon mapping - an efficient nonlinear data compression technique. Systematic classification of damage into states based on the analysis of the AR coefficients was achieved using two supervised classification techniques: Nearest Neighbor Classification (NNC) and Learning Vector Quantization (LVQ), and one unsupervised technique: Self-organizing Maps (SOM). This paper discusses the performance of AR coefficients as damage sensitive features and compares the efficiency of the three classification techniques using experimental data.

Comprehensive 4-stage categorization of bicuspid aortic valve leaflet morphology by cardiac MRI in 386 patients.

PubMed

Murphy, I G; Collins, J; Powell, A; Markl, M; McCarthy, P; Malaisrie, S C; Carr, J C; Barker, A J

2017-08-01

Bicuspid aortic valve (BAV) disease is heterogeneous and related to valve dysfunction and aortopathy. Appropriate follow up and surveillance of patients with BAV may depend on correct phenotypic categorization. There are multiple classification schemes, however a need exists to comprehensively capture commissure fusion, leaflet asymmetry, and valve orifice orientation. Our aim was to develop a BAV classification scheme for use at MRI to ascertain the frequency of different phenotypes and the consistency of BAV classification. The BAV classification scheme builds on the Sievers surgical BAV classification, adding valve orifice orientation, partial leaflet fusion and leaflet asymmetry. A single observer successfully applied this classification to 386 of 398 Cardiac MRI studies. Repeatability of categorization was ascertained with intraobserver and interobserver kappa scores. Sensitivity and specificity of MRI findings was determined from operative reports, where available. Fusion of the right and left leaflets accounted for over half of all cases. Partial leaflet fusion was seen in 46% of patients. Good interobserver agreement was seen for orientation of the valve opening (κ = 0.90), type (κ = 0.72) and presence of partial fusion (κ = 0.83, p < 0.0001). Retrospective review of operative notes showed sensitivity and specificity for orientation (90, 93%) and for Sievers type (73, 87%). The proposed BAV classification schema was assessed by MRI for its reliability to classify valve morphology in addition to illustrating the wide heterogeneity of leaflet size, orifice orientation, and commissural fusion. The classification may be helpful in further understanding the relationship between valve morphology, flow derangement and aortopathy.

Scenario Analysis: An Integrative Study and Guide to Implementation in the United States Air Force

DTIC Science & Technology

1994-09-01

Environmental Analysis ................................ 3-3 Classifications of Environments ......................... 3-5 Characteristics of... Environments ........................ 3-8 iii Page Components of the Environmental Analysis Process ........... 3-12 Forecasting... Environmental Analysis ...................... 3-4 3-2 Model of the Industry Environment ......................... 3-6 3-3 Model of Macroenvironment

77 FR 27534 - Department of Transportation Updated Environmental Justice Order 5610.2(a)

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-10

... component of the Department's strategy to promote the principles of environmental justice in all... environmental justice principles in all (DOT) programs, policies, and activities. It describes how the... the Office of Management and Budget's (OMB) Revisions to the Standards for the Classification of...

21 CFR 864.9575 - Environmental chamber for storage of platelet concentrate.

Code of Federal Regulations, 2012 CFR

2012-04-01

... to hold platelet-rich plasma within a preselected temperature range. (b) Classification. Class II... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Environmental chamber for storage of platelet... Establishments That Manufacture Blood and Blood Products § 864.9575 Environmental chamber for storage of platelet...

21 CFR 864.9575 - Environmental chamber for storage of platelet concentrate.

Code of Federal Regulations, 2010 CFR

2010-04-01

... to hold platelet-rich plasma within a preselected temperature range. (b) Classification. Class II... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Environmental chamber for storage of platelet... Establishments That Manufacture Blood and Blood Products § 864.9575 Environmental chamber for storage of platelet...

21 CFR 864.9575 - Environmental chamber for storage of platelet concentrate.

Code of Federal Regulations, 2013 CFR

2013-04-01

... to hold platelet-rich plasma within a preselected temperature range. (b) Classification. Class II... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Environmental chamber for storage of platelet... Establishments That Manufacture Blood and Blood Products § 864.9575 Environmental chamber for storage of platelet...

21 CFR 864.9575 - Environmental chamber for storage of platelet concentrate.

Code of Federal Regulations, 2011 CFR

2011-04-01

... to hold platelet-rich plasma within a preselected temperature range. (b) Classification. Class II... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Environmental chamber for storage of platelet... Establishments That Manufacture Blood and Blood Products § 864.9575 Environmental chamber for storage of platelet...

21 CFR 864.9575 - Environmental chamber for storage of platelet concentrate.

Code of Federal Regulations, 2014 CFR

2014-04-01

... to hold platelet-rich plasma within a preselected temperature range. (b) Classification. Class II... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Environmental chamber for storage of platelet... Establishments That Manufacture Blood and Blood Products § 864.9575 Environmental chamber for storage of platelet...

Notes on an Environmental Pollution Vocabulary.

ERIC Educational Resources Information Center

Smithsonian Institution, Washington, DC. Science Information Exchange.

This vocabulary covering the field of environmental pollution was compiled by the staff of the Science Information Exchange, Smithsonian Institution. The view of the approach is to include an outline-classification all physical, life, and social science aspects of environmental pollution, trying to achieve a balance in the representation of each…

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System

PubMed Central

Hu, Maogui; Li, Wei; Li, Lianfa; Houston, Douglas; Wu, Jun

2016-01-01

Background Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Methods Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Results Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. Conclusions The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations. PMID:26919723

Experimental measurement of cooling tower emissions using image processing of sensitive papers

NASA Astrophysics Data System (ADS)

Ruiz, J.; Kaiser, A. S.; Ballesta, M.; Gil, A.; Lucas, M.

2013-04-01

Cooling tower emissions are harmful for several reasons such as air polluting, wetting, icing and solid particle deposition, but mainly due to human health hazards (i.e. Legionella). There are several methods for measuring drift drops. This paper is focussed on the sensitive paper technique, which is suitable in low drift scenarios and real conditions. The lack of an automatic classification method motivated the development of a digital image process algorithm for the Sensitive Paper method. This paper presents a detailed description of this method, in which, drop-like elements are identified by means of the Canny edge detector combined with some morphological operations. Afterwards, the application of a J48 decision tree is proposed as one of the most relevant contributions. This classification method allows us to discern between stains whose origin is a drop and stains whose origin is not a drop. The method is applied to a real case and results are presented in terms of drift and PM10 emissions. This involves the calculation of the main features of the droplet distribution at the cooling tower exit surface in terms of drop size distribution data, cumulative mass distribution curve and characteristic drop diameters. The Log-normal and the Rosin-Rammler distribution functions have been fitted to the experimental data collected in the tests and it can been concluded that the first one is the most suitable for experimental data among the functions tested (whereas the second one is less suitable). Realistic PM10 calculations include the measurement of drift emissions and Total Dissolved Solids as well as the size and number of drops. Results are compared to the method proposed by the U.S. Environmental Protection Agency assessing its overestimation. Drift emissions have found to be 0.0517% of the recirculating water, which is over the Spanish standards limit (0.05%).

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.

Disability and Profiles of Functioning of Patients with Parkinson's Disease Described with ICF Classification

ERIC Educational Resources Information Center

Raggi, Alberto; Leonardi, Matilde; Ajovalasit, Daniela; Carella, Francesco; Soliveri, Paola; Albanese, Alberto; Romito, Luigi

2011-01-01

The objective of this study was to describe the functional profiles of patients with Parkinson's disease (PD), and the relationships between impairment in body functions, limitations in activities, and environmental factors, using the World Health Organization's International Classification of Functioning, Disability, and Health (ICF). Patients…

Disability and Functional Profiles of Patients with Migraine Measured with ICF Classification

ERIC Educational Resources Information Center

Raggi, Alberto

2010-01-01

To describe the functional profiles of patients with migraine, and the relationships between symptoms, activities and environmental factors, using WHO's International Classification of Functioning (ICF). Patients were consecutively enrolled at the Besta Institute of Milan. The ICF checklist was administered and two count-based indexes developed:…

40 CFR 260.30 - Non-waste determinations and variances from classification as a solid waste.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 27 2012-07-01 2012-07-01 false Non-waste determinations and variances from classification as a solid waste. 260.30 Section 260.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking...

40 CFR 260.30 - Non-waste determinations and variances from classification as a solid waste.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 27 2013-07-01 2013-07-01 false Non-waste determinations and variances from classification as a solid waste. 260.30 Section 260.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking...

40 CFR 260.30 - Non-waste determinations and variances from classification as a solid waste.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 26 2014-07-01 2014-07-01 false Non-waste determinations and variances from classification as a solid waste. 260.30 Section 260.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking...

Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques

PubMed Central

Cheng, Wei; Ji, Xiaoxi; Zhang, Jie; Feng, Jianfeng

2012-01-01

Accurate classification or prediction of the brain state across individual subject, i.e., healthy, or with brain disorders, is generally a more difficult task than merely finding group differences. The former must be approached with highly informative and sensitive biomarkers as well as effective pattern classification/feature selection approaches. In this paper, we propose a systematic methodology to discriminate attention deficit hyperactivity disorder (ADHD) patients from healthy controls on the individual level. Multiple neuroimaging markers that are proved to be sensitive features are identified, which include multiscale characteristics extracted from blood oxygenation level dependent (BOLD) signals, such as regional homogeneity (ReHo) and amplitude of low-frequency fluctuations. Functional connectivity derived from Pearson, partial, and spatial correlation is also utilized to reflect the abnormal patterns of functional integration, or, dysconnectivity syndromes in the brain. These neuroimaging markers are calculated on either voxel or regional level. Advanced feature selection approach is then designed, including a brain-wise association study (BWAS). Using identified features and proper feature integration, a support vector machine (SVM) classifier can achieve a cross-validated classification accuracy of 76.15% across individuals from a large dataset consisting of 141 healthy controls and 98 ADHD patients, with the sensitivity being 63.27% and the specificity being 85.11%. Our results show that the most discriminative features for classification are primarily associated with the frontal and cerebellar regions. The proposed methodology is expected to improve clinical diagnosis and evaluation of treatment for ADHD patient, and to have wider applications in diagnosis of general neuropsychiatric disorders. PMID:22888314

Low back related leg pain: an investigation of construct validity of a new classification system.

PubMed

Schäfer, Axel G M; Hall, Toby M; Rolke, Roman; Treede, Rolf-Detlef; Lüdtke, Kerstin; Mallwitz, Joachim; Briffa, Kathryn N

2014-01-01

Leg pain is associated with back pain in 25-65% of all cases and classified as somatic referred pain or radicular pain. However, distinction between the two may be difficult as different pathomechanisms may cause similar patterns of pain. Therefore a pathomechanism based classification system was proposed, with four distinct hierarchical and mutually exclusive categories: Neuropathic Sensitization (NS) comprising major features of neuropathic pain with sensory sensitization; Denervation (D) arising from significant axonal compromise; Peripheral Nerve Sensitization (PNS) with marked nerve trunk mechanosensitivity; and Musculoskeletal (M) with pain referred from musculoskeletal structures. To investigate construct validity of the classification system. Construct validity was investigated by determining the relationship of nerve functioning with subgroups of patients and asymptomatic controls. Thus somatosensory profiles of subgroups of patients with low back related leg pain (LBRLP) and healthy controls were determined by a comprehensive quantitative sensory test (QST) protocol. It was hypothesized that subgroups of patients and healthy controls would show differences in QST profiles relating to underlying pathomechanisms. 77 subjects with LBRLP were recruited and classified in one of the four groups. Additionally, 18 age and gender matched asymptomatic controls were measured. QST revealed signs of pain hypersensitivity in group NS and sensory deficits in group D whereas Groups PNS and M showed no significant differences when compared to the asymptomatic group. These findings support construct validity for two of the categories of the new classification system, however further research is warranted to achieve construct validation of the classification system as a whole.

Challenges in congenital syphilis surveillance: how are congenital syphilis investigations classified?

PubMed

Introcaso, Camille E; Gruber, DeAnn; Bradley, Heather; Peterman, Thomas A; Ewell, Joy; Wendell, Debbie; Foxhood, Joseph; Su, John R; Weinstock, Hillard S; Markowitz, Lauri E

2013-09-01

Congenital syphilis is a serious, preventable, and nationally notifiable disease. Despite the existence of a surveillance case definition, congenital syphilis is sometimes classified differently using an algorithm on the Centers for Disease Control and Prevention's case reporting form. We reviewed Louisiana's congenital syphilis electronic reporting system for investigations of infants born from January 2010 to October 2011, abstracted data required for classification, and applied the surveillance definition and the algorithm. We calculated the sensitivities and specificities of the algorithm and Louisiana's classification using the surveillance definition as the surveillance gold standard. Among 349 congenital syphilis investigations, the surveillance definition identified 62 cases. The algorithm had a sensitivity of 91.9% and a specificity of 64.1%. Louisiana's classification had a sensitivity of 50% and a specificity of 91.3% compared with the surveillance definition. The differences between the algorithm and the surveillance definition led to misclassification of congenital syphilis cases. The algorithm should match the surveillance definition. Other state and local health departments should assure that their reported cases meet the surveillance definition.

Epoch-based Entropy for Early Screening of Alzheimer's Disease.

PubMed

Houmani, N; Dreyfus, G; Vialatte, F B

2015-12-01

In this paper, we introduce a novel entropy measure, termed epoch-based entropy. This measure quantifies disorder of EEG signals both at the time level and spatial level, using local density estimation by a Hidden Markov Model on inter-channel stationary epochs. The investigation is led on a multi-centric EEG database recorded from patients at an early stage of Alzheimer's disease (AD) and age-matched healthy subjects. We investigate the classification performances of this method, its robustness to noise, and its sensitivity to sampling frequency and to variations of hyperparameters. The measure is compared to two alternative complexity measures, Shannon's entropy and correlation dimension. The classification accuracies for the discrimination of AD patients from healthy subjects were estimated using a linear classifier designed on a development dataset, and subsequently tested on an independent test set. Epoch-based entropy reached a classification accuracy of 83% on the test dataset (specificity = 83.3%, sensitivity = 82.3%), outperforming the two other complexity measures. Furthermore, it was shown to be more stable to hyperparameter variations, and less sensitive to noise and sampling frequency disturbances than the other two complexity measures.

A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers.

PubMed

Tartar, A; Akan, A; Kilic, N

2014-01-01

Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this paper, a novel Computer-Aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. The proposed CAD system using ensemble learning classifiers, provides an important support to radiologists at the diagnosis process of the disease, achieves high classification performance. The proposed approach with bagging classifier results in 94.7 %, 90.0 % and 77.8 % classification sensitivities for benign, malignant and undetermined classes (89.5 % accuracy), respectively.

Classification of skin cancer images using local binary pattern and SVM classifier

NASA Astrophysics Data System (ADS)

Adjed, Faouzi; Faye, Ibrahima; Ababsa, Fakhreddine; Gardezi, Syed Jamal; Dass, Sarat Chandra

2016-11-01

In this paper, a classification method for melanoma and non-melanoma skin cancer images has been presented using the local binary patterns (LBP). The LBP computes the local texture information from the skin cancer images, which is later used to compute some statistical features that have capability to discriminate the melanoma and non-melanoma skin tissues. Support vector machine (SVM) is applied on the feature matrix for classification into two skin image classes (malignant and benign). The method achieves good classification accuracy of 76.1% with sensitivity of 75.6% and specificity of 76.7%.

Design of neural networks for classification of remotely sensed imagery

NASA Technical Reports Server (NTRS)

Chettri, Samir R.; Cromp, Robert F.; Birmingham, Mark

1992-01-01

Classification accuracies of a backpropagation neural network are discussed and compared with a maximum likelihood classifier (MLC) with multivariate normal class models. We have found that, because of its nonparametric nature, the neural network outperforms the MLC in this area. In addition, we discuss techniques for constructing optimal neural nets on parallel hardware like the MasPar MP-1 currently at GSFC. Other important discussions are centered around training and classification times of the two methods, and sensitivity to the training data. Finally, we discuss future work in the area of classification and neural nets.

The impact of missing trauma data on predicting massive transfusion

PubMed Central

Trickey, Amber W.; Fox, Erin E.; del Junco, Deborah J.; Ning, Jing; Holcomb, John B.; Brasel, Karen J.; Cohen, Mitchell J.; Schreiber, Martin A.; Bulger, Eileen M.; Phelan, Herb A.; Alarcon, Louis H.; Myers, John G.; Muskat, Peter; Cotton, Bryan A.; Wade, Charles E.; Rahbar, Mohammad H.

2013-01-01

INTRODUCTION Missing data are inherent in clinical research and may be especially problematic for trauma studies. This study describes a sensitivity analysis to evaluate the impact of missing data on clinical risk prediction algorithms. Three blood transfusion prediction models were evaluated utilizing an observational trauma dataset with valid missing data. METHODS The PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study included patients requiring ≥ 1 unit of red blood cells (RBC) at 10 participating U.S. Level I trauma centers from July 2009 – October 2010. Physiologic, laboratory, and treatment data were collected prospectively up to 24h after hospital admission. Subjects who received ≥ 10 RBC units within 24h of admission were classified as massive transfusion (MT) patients. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation. A sensitivity analysis for missing data was conducted to determine the upper and lower bounds for correct classification percentages. RESULTS PROMMTT enrolled 1,245 subjects. MT was received by 297 patients (24%). Missing percentage ranged from 2.2% (heart rate) to 45% (respiratory rate). Proportions of complete cases utilized in the MT prediction models ranged from 41% to 88%. All models demonstrated similar correct classification percentages using complete case analysis and multiple imputation. In the sensitivity analysis, correct classification upper-lower bound ranges per model were 4%, 10%, and 12%. Predictive accuracy for all models using PROMMTT data was lower than reported in the original datasets. CONCLUSIONS Evaluating the accuracy clinical prediction models with missing data can be misleading, especially with many predictor variables and moderate levels of missingness per variable. The proposed sensitivity analysis describes the influence of missing data on risk prediction algorithms. Reporting upper/lower bounds for percent correct classification may be more informative than multiple imputation, which provided similar results to complete case analysis in this study. PMID:23778514

40 CFR 164.132 - Procedures governing hearing.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Section 164.132 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS..., ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS... and environmental risks found by the Administrator in his cancellation or suspension determination and...

40 CFR 164.123 - Emergency order.

Code of Federal Regulations, 2010 CFR

2010-07-01

....123 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF... REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF... Hearings § 164.123 Emergency order. (a) Whenever the Environmental Appeals Board determines that an...

40 CFR 164.121 - Expedited hearing.

Code of Federal Regulations, 2010 CFR

2010-07-01

....121 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF... REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF... the presentation of evidence the Presiding Officer shall submit to the Environmental Appeals Board his...

40 CFR 164.121 - Expedited hearing.

Code of Federal Regulations, 2011 CFR

2011-07-01

....121 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF... REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF... the presentation of evidence the Presiding Officer shall submit to the Environmental Appeals Board his...

40 CFR 164.123 - Emergency order.

Code of Federal Regulations, 2011 CFR

2011-07-01

....123 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF... REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF... Hearings § 164.123 Emergency order. (a) Whenever the Environmental Appeals Board determines that an...

Environmental Sensitivity in Children: Development of the Highly Sensitive Child Scale and Identification of Sensitivity Groups

ERIC Educational Resources Information Center

Pluess, Michael; Assary, Elham; Lionetti, Francesca; Lester, Kathryn J.; Krapohl, Eva; Aron, Elaine N.; Aron, Arthur

2018-01-01

A large number of studies document that children differ in the degree they are shaped by their developmental context with some being more sensitive to environmental influences than others. Multiple theories suggest that "Environmental Sensitivity" is a common trait predicting the response to negative as well as positive exposures.…

[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

PubMed

Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

2006-08-01

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

The Molecular Epidemiology of Breast Cancer: Risk from Environmental Exposures and Genetic Susceptibility.

DTIC Science & Technology

1996-10-01

Diet 16. PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT OF REPORT OF THIS PAGE...approach, Frank et al. (1993) compared DDE and PCB residues in the general diet with blood levels of Ontario residents. Blood samples were obtained from...sources of PCBs and HCB in this geographical region. In a similar study, Kashyap et al. (1994) monitored DDT levels in duplicate diet samples and

Information analysis of a spatial database for ecological land classification

NASA Technical Reports Server (NTRS)

Davis, Frank W.; Dozier, Jeff

1990-01-01

An ecological land classification was developed for a complex region in southern California using geographic information system techniques of map overlay and contingency table analysis. Land classes were identified by mutual information analysis of vegetation pattern in relation to other mapped environmental variables. The analysis was weakened by map errors, especially errors in the digital elevation data. Nevertheless, the resulting land classification was ecologically reasonable and performed well when tested with higher quality data from the region.

Classification of permafrost active layer depth from remotely sensed and topographic evidence

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

Peddle, D.R.; Franklin, S.E.

1993-04-01

The remote detection of permafrost (perennially frozen ground) has important implications to environmental resource development, engineering studies, natural hazard prediction, and climate change research. In this study, the authors present results from two experiments into the classification of permafrost active layer depth within the zone of discontinuous permafrost in northern Canada. A new software system based on evidential reasoning was implemented to permit the integrated classification of multisource data consisting of landcover, terrain aspect, and equivalent latitude, each of which possessed different formats, data types, or statistical properties that could not be handled by conventional classification algorithms available to thismore » study. In the first experiment, four active layer depth classes were classified using ground based measurements of the three variables with an accuracy of 83% compared to in situ soil probe determination of permafrost active layer depth at over 500 field sites. This confirmed the environmental significance of the variables selected, and provided a baseline result to which a remote sensing classification could be compared. In the second experiment, evidence for each input variable was obtained from image processing of digital SPOT imagery and a photogrammetric digital elevation model, and used to classify active layer depth with an accuracy of 79%. These results suggest the classification of evidence from remotely sensed measures of spectral response and topography may provide suitable indicators of permafrost active layer depth.« less

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.

PubMed

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

2018-01-17

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

Divorcing Strain Classification from Species Names.

PubMed

Baltrus, David A

2016-06-01

Confusion about strain classification and nomenclature permeates modern microbiology. Although taxonomists have traditionally acted as gatekeepers of order, the numbers of, and speed at which, new strains are identified has outpaced the opportunity for professional classification for many lineages. Furthermore, the growth of bioinformatics and database-fueled investigations have placed metadata curation in the hands of researchers with little taxonomic experience. Here I describe practical challenges facing modern microbial taxonomy, provide an overview of complexities of classification for environmentally ubiquitous taxa like Pseudomonas syringae, and emphasize that classification can be independent of nomenclature. A move toward implementation of relational classification schemes based on inherent properties of whole genomes could provide sorely needed continuity in how strains are referenced across manuscripts and data sets. Copyright © 2016 Elsevier Ltd. All rights reserved.

Land development in Massachusetts: Its effect on the environment within Essex and Middlesex counties from 1990 to 2007

NASA Astrophysics Data System (ADS)

Tardie, Peter Sean

Since the 1970's urban centers in and surrounding Essex and Middlesex Counties in Massachusetts have expanded and proliferated into adjacent communities. This expansion has led to the conversion of land for housing, businesses, schools, recreation, and parks, placing significant strain on existing land cover, land use, and available natural resources. Mounting growth pressures and a reduction of undeveloped land have raised serious concerns as cropland and forest fragmentation, wetland destruction, protected open-space infringement, pollution, and systematic losses of rural conditions have become obvious. To monitor development, the post-classification change detection method was applied to Landsat Thematic Mapper (TM) satellite data and GIS was used to detect, quantity, and document the extent of development and its effect on the environment and to assess and quantify the demographic changes that occurred within the counties from 1990 to 2007. Classification of the 1990 image resulted in 217 clusters and 214 clusters for the 2007 image The overall accuracy achieved for the 1990 image classification was 87.3% with a KHAT value of 0.848, and the overall accuracy for the 2007 classification was 86.27% with a KHAT value of 0.840. From 1990 to 2007 land cover change occurred primarily along major transportation corridors. The post-classification change detection results indicate that Essex and Middlesex County combined gained 23,435.66 "new" acres of land development from 1990 to 2007 through a loss and change in acreage from the Bareland, Forest, Grassland, Water, and Wetland land cover class categories. Results indicate that there was an approximate 0.56% overall (net) increase of newly developed land areas within the 1990 and 2007 image classifications from 415.46 acres or 0.64 square miles. In addition, there was a substantial decrease (-40.0%) within the grassland category. Land development was responsible for a portion of the decrease of grasslands (-13.63%), which occurred mostly within Middlesex County. Results also indicate that "new" land development occurred within several Commonwealth of Massachusetts designated environmentally-sensitive areas: 722 acres in areas of critical environmental concern, 670 acres in priority habitats of rare species, 1,092 acres in living waters core habitats and critical supporting watersheds, 1,318 acres in protected and recreational open spaces, and within 0-1000 feet of 600 certified vernal pools. In addition, several rare or imperiled species inhabiting these areas may have been adversely affected by land development through habitat loss, change, or fragmentation, and/or passage corridor disruptions. A GIS comparison of the "new" land development acreages and census demographic statistics within Essex and Middlesex County cities and towns during this period indicate that communities with more families with children exhibited more land development, and communities with higher median household income exhibited less land development. Land change detection over the 17-year period indicated encroachment of development in areas of environmental concern, but level of development varied by socio-demographic factors. This study also illustrated that the combined use of remotely sensed data, Geographic Information Systems (GIS) technology, and demographic data are effective for use as a diagnostic tool and/or base to be built upon to explore associations, indicators, or drivers which may influence land cover change and its effects on existing environmental conditions in areas exhibiting change. In addition, this study provided awareness to ancillary research where scientific guidelines were derived for the protection of specific wildlife habitats and resident species. Lastly, this study presented several land cover modeling and web deployed data dissemination tools for the dissertation results as well as provided a conceptual framework for the successful adoption and implementation of these tools for organizations engaged in natural resource planning and management.

Differential Memory Test Sensitivity for Diagnosing Amnestic Mild Cognitive Impairment and Predicting Conversion to Alzheimer's Disease

PubMed Central

Rabin, Laura A.; Paré, Nadia; Saykin, Andrew J.; Brown, Michael J.; Wishart, Heather A.; Flashman, Laura A.; Santulli, Robert B.

2011-01-01

Episodic memory is the first and most severely affected cognitive domain in Alzheimer's disease (AD), and it is also the key early marker in prodromal stages including amnestic mild cognitive impairment (MCI). The relative ability of memory tests to discriminate between MCI and normal aging has not been well characterized. We compared the classification value of widely used verbal memory tests in distinguishing healthy older adults (n = 51) from those with MCI (n = 38). Univariate logistic regression indicated that the total learning score from the California Verbal Learning Test-II (CVLT-II) ranked highest in terms of distinguishing MCI from normal aging (sensitivity = 90.2; specificity = 84.2). Inclusion of the delayed recall condition of a story memory task (i.e., WMS-III Logical Memory, Story A) enhanced the overall accuracy of classification (sensitivity = 92.2; specificity = 94.7). Combining Logical Memory recognition and CVLT-II long delay best predicted progression from MCI to AD over a 4-year period (accurate classification = 87.5%). Learning across multiple trials may provide the most sensitive index for initial diagnosis of MCI, but inclusion of additional variables may enhance overall accuracy and may represent the optimal strategy for identifying individuals most likely to progress to dementia. PMID:19353345

Nearest Neighbor Classification Using a Density Sensitive Distance Measurement

DTIC Science & Technology

2009-09-01

both the proposed density sensitive distance measurement and Euclidean distance are compared on the Wisconsin Diagnostic Breast Cancer dataset and...proposed density sensitive distance measurement and Euclidean distance are compared on the Wisconsin Diagnostic Breast Cancer dataset and the MNIST...35 1. The Wisconsin Diagnostic Breast Cancer (WDBC) Dataset..........35 2. The

Microstructure-Sensitive Modeling of High Cycle Fatigue (Preprint)

DTIC Science & Technology

2009-03-01

SUBJECT TERMS microplasticity , microstructure-sensitive modeling, high cycle fatigue, fatigue variability 16. SECURITY CLASSIFICATION OF: 17...3Air Force Research Laboratory Wright Patterson Air Force Base, Ohio 45433 Keywords: Microplasticity , microstructure-sensitive modeling, high cycle...cyclic microplasticity ) plays a key role in modeling fatigue resistance. Unlike effective properties such as elastic stiffness, fatigue is

Adding an alcohol-related risk score to an existing categorical risk classification for older adults: sensitivity to group differences.

PubMed

Wilson, Sandra R; Fink, Arlene; Verghese, Shinu; Beck, John C; Nguyen, Khue; Lavori, Philip

2007-03-01

To evaluate a new alcohol-related risk score for research use. Using data from a previously reported trial of a screening and education system for older adults (Computerized Alcohol-Related Problems Survey), secondary analyses were conducted comparing the ability of two different measures of risk to detect post-intervention group differences: the original categorical outcome measure and a new, finely grained quantitative risk score based on the same research-based risk factors. Three primary care group practices in southern California. Six hundred sixty-five patients aged 65 and older. A previously calculated, three-level categorical classification of alcohol-related risk and a newly developed quantitative risk score. Mean post-intervention risk scores differed between the three experimental conditions: usual care, patient report, and combined report (P<.001). The difference between the combined report and usual care was significant (P<.001) and directly proportional to baseline risk. The three-level risk classification did not reveal approximately 57.3% of the intervention effect detected by the risk score. The risk score also was sufficiently sensitive to detect the intervention effect within the subset of hypertensive patients (n=112; P=.001). As an outcome measure in intervention trials, the finely grained risk score is more sensitive than the trinary risk classification. The additional clinical value of the risk score relative to the categorical measure needs to be determined.

Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction.

PubMed

Cardoso, F F; Tempelman, R J

2012-07-01

The objectives of this work were to assess alternative linear reaction norm (RN) models for genetic evaluation of Angus cattle in Brazil. That is, we investigated the interaction between genotypes and continuous descriptors of the environmental variation to examine evidence of genotype by environment interaction (G×E) in post-weaning BW gain (PWG) and to compare the environmental sensitivity of national and imported Angus sires. Data were collected by the Brazilian Angus Improvement Program from 1974 to 2005 and consisted of 63,098 records and a pedigree file with 95,896 animals. Six models were implemented using Bayesian inference and compared using the Deviance Information Criterion (DIC). The simplest model was M(1), a traditional animal model, which showed the largest DIC and hence the poorest fit when compared with the 4 alternative RN specifications accounting for G×E. In M(2), a 2-step procedure was implemented using the contemporary group posterior means of M(1) as the environmental gradient, ranging from -92.6 to +265.5 kg. Moreover, the benefits of jointly estimating all parameters in a 1-step approach were demonstrated by M(3). Additionally, we extended M(3) to allow for residual heteroskedasticity using an exponential function (M(4)) and the best fitting (smallest DIC) environmental classification model (M(5)) specification. Finally, M(6) added just heteroskedastic residual variance to M(1). Heritabilities were less at harsh environments and increased with the improvement of production conditions for all RN models. Rank correlations among genetic merit predictions obtained by M(1) and by the best fitting RN models M(3) (homoskedastic) and M(5) (heteroskedastic) at different environmental levels ranged from 0.79 and 0.81, suggesting biological importance of G×E in Brazilian Angus PWG. These results suggest that selection progress could be optimized by adopting environment-specific genetic merit predictions. The PWG environmental sensitivity of imported North American origin bulls (0.046 ± 0.009) was significantly larger (P < 0.05) than that of local sires (0.012 ± 0.013). Moreover, PWG of progeny of imported sires exceeded that of native sires in medium and superior production levels. On the other hand, Angus cattle locally selected in Brazil tended to be more robust to environmental changes and hence be more suitable when production environments for potential progeny is uncertain.

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

PubMed Central

Groenendyk, Derek G.; Ferré, Ty P.A.; Thorp, Kelly R.; Rice, Amy K.

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function. PMID:26121466

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

PubMed

Groenendyk, Derek G; Ferré, Ty P A; Thorp, Kelly R; Rice, Amy K

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.

Sensitivity and Specificity of Cardiac Tissue Discrimination Using Fiber-Optics Confocal Microscopy.

PubMed

Huang, Chao; Sachse, Frank B; Hitchcock, Robert W; Kaza, Aditya K

2016-01-01

Disturbances of the cardiac conduction system constitute a major risk after surgical repair of complex cases of congenital heart disease. Intraoperative identification of the conduction system may reduce the incidence of these disturbances. We previously developed an approach to identify cardiac tissue types using fiber-optics confocal microscopy and extracellular fluorophores. Here, we applied this approach to investigate sensitivity and specificity of human and automated classification in discriminating images of atrial working myocardium and specialized tissue of the conduction system. Two-dimensional image sequences from atrial working myocardium and nodal tissue of isolated perfused rodent hearts were acquired using a fiber-optics confocal microscope (Leica FCM1000). We compared two methods for local application of extracellular fluorophores: topical via pipette and with a dye carrier. Eight blinded examiners evaluated 162 randomly selected images of atrial working myocardium (n = 81) and nodal tissue (n = 81). In addition, we evaluated the images using automated classification. Blinded examiners achieved a sensitivity and specificity of 99.2 ± 0.3% and 98.0 ± 0.7%, respectively, with the dye carrier method of dye application. Sensitivity and specificity was similar for dye application via a pipette (99.2 ± 0.3% and 94.0 ± 2.4%, respectively). Sensitivity and specificity for automated methods of tissue discrimination were similarly high. Human and automated classification achieved high sensitivity and specificity in discriminating atrial working myocardium and nodal tissue. We suggest that our findings facilitate clinical translation of fiber-optics confocal microscopy as an intraoperative imaging modality to reduce the incidence of conduction disturbances during surgical correction of congenital heart disease.

Internal complexity and environmental sensitivity in hospitals.

PubMed

Ashmos, D P; Duchon, D; Hauge, F E; McDaniel, R R

1996-01-01

Theory suggests that organizations should respond to external complexity with internal complexity. We examine whether "environmentally sensitive" hospitals are more internally complex than "environmentally insensitive" hospitals. Results show that environmentally sensitive and insensitive hospitals differed on three of the measures of internal complexity: goal complexity, strategic complexity, and relational complexity.

40 CFR 11.5 - Procedures.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 1 2010-07-01 2010-07-01 false Procedures. 11.5 Section 11.5 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY GENERAL SECURITY CLASSIFICATION REGULATIONS...” shall be directed to: Director, Security and Inspection Division, Environmental Protection Agency...

2015 Gout classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative

PubMed Central

Neogi, Tuhina; Jansen, Tim L Th A; Dalbeth, Nicola; Fransen, Jaap; Schumacher, H Ralph; Berendsen, Dianne; Brown, Melanie; Choi, Hyon; Edwards, N Lawrence; Janssens, Hein J E M; Lioté, Frédéric; Naden, Raymond P; Nuki, George; Ogdie, Alexis; Perez-Ruiz, Fernando; Saag, Kenneth; Singh, Jasvinder A; Sundy, John S; Tausche, Anne-Kathrin; Vaquez-Mellado, Janitzia; Yarows, Steven A; Taylor, William J

2015-01-01

Objective Existing criteria for the classification of gout have suboptimal sensitivity and/or specificity, and were developed at a time when advanced imaging was not available. The current effort was undertaken to develop new classification criteria for gout. Methods An international group of investigators, supported by the American College of Rheumatology and the European League Against Rheumatism, conducted a systematic review of the literature on advanced imaging of gout, a diagnostic study in which the presence of monosodium urate monohydrate (MSU) crystals in synovial fluid or tophus was the gold standard, a ranking exercise of paper patient cases, and a multi-criterion decision analysis exercise. These data formed the basis for developing the classification criteria, which were tested in an independent data set. Results The entry criterion for the new classification criteria requires the occurrence of at least one episode of peripheral joint or bursal swelling, pain, or tenderness. The presence of MSU crystals in a symptomatic joint/bursa (ie, synovial fluid) or in a tophus is a sufficient criterion for classification of the subject as having gout, and does not require further scoring. The domains of the new classification criteria include clinical (pattern of joint/bursa involvement, characteristics and time course of symptomatic episodes), laboratory (serum urate, MSU-negative synovial fluid aspirate), and imaging (double-contour sign on ultrasound or urate on dual-energy CT, radiographic gout-related erosion). The sensitivity and specificity of the criteria are high (92% and 89%, respectively). Conclusions The new classification criteria, developed using a data-driven and decision-analytic approach, have excellent performance characteristics and incorporate current state-of-the-art evidence regarding gout. PMID:26359487

2015 Gout Classification Criteria: An American College of Rheumatology/European League Against Rheumatism Collaborative Initiative

PubMed Central

Jansen, Tim L. Th. A.; Dalbeth, Nicola; Fransen, Jaap; Schumacher, H. Ralph; Berendsen, Dianne; Brown, Melanie; Choi, Hyon; Edwards, N. Lawrence; Janssens, Hein J. E. M.; Lioté, Frédéric; Naden, Raymond P.; Nuki, George; Ogdie, Alexis; Perez‐Ruiz, Fernando; Saag, Kenneth; Singh, Jasvinder A.; Sundy, John S.; Tausche, Anne‐Kathrin; Vaquez‐Mellado, Janitzia; Yarows, Steven A.; Taylor, William J.

2015-01-01

Objective Existing criteria for the classification of gout have suboptimal sensitivity and/or specificity, and were developed at a time when advanced imaging was not available. The current effort was undertaken to develop new classification criteria for gout. Methods An international group of investigators, supported by the American College of Rheumatology and the European League Against Rheumatism, conducted a systematic review of the literature on advanced imaging of gout, a diagnostic study in which the presence of monosodium urate monohydrate (MSU) crystals in synovial fluid or tophus was the gold standard, a ranking exercise of paper patient cases, and a multicriterion decision analysis exercise. These data formed the basis for developing the classification criteria, which were tested in an independent data set. Results The entry criterion for the new classification criteria requires the occurrence of at least 1 episode of peripheral joint or bursal swelling, pain, or tenderness. The presence of MSU crystals in a symptomatic joint/bursa (i.e., synovial fluid) or in a tophus is a sufficient criterion for classification of the subject as having gout, and does not require further scoring. The domains of the new classification criteria include clinical (pattern of joint/bursa involvement, characteristics and time course of symptomatic episodes), laboratory (serum urate, MSU‐negative synovial fluid aspirate), and imaging (double‐contour sign on ultrasound or urate on dual‐energy computed tomography, radiographic gout‐related erosion). The sensitivity and specificity of the criteria are high (92% and 89%, respectively). Conclusion The new classification criteria, developed using a data‐driven and decision analytic approach, have excellent performance characteristics and incorporate current state‐of‐the‐art evidence regarding gout. PMID:26352873

A Study of MX Environmental Management Information System (MXEMIS) Needs.

DTIC Science & Technology

1983-12-01

ENVIRONMENTAL MANAGEMENT INFORMATION SYSTEM (MXEMIS) NEEDS by Ronald Webster Ralph Mitchell Valorie Young -J : 2 34 LA--. Approved for public release...System (SAIFS) The MX Management Information System (MX MIS) The Mobilization Early Warning System (MEWS) The Computer-Aided Environmental Baseline...26 REFERENCES DISTRIBUTION I5 S’ t A STUDY OF MX ENVIRONMENTAL 2 EXISTING SYSTEMS CLASSIFICATION MANAGEMENT INFORMATION SYSTEM (MXEMIS

Disability and Functional Profiles of Patients with Myasthenia Gravis Measured with ICF Classification

ERIC Educational Resources Information Center

Leonardi, Matilde; Raggi, Alberto; Antozzi, Carlo; Confalonieri, Paolo; Maggi, Lorenzo; Cornelio, Ferdinando; Mantegazza, Renato

2009-01-01

The objective of this study is to describe functional profiles of patients with myasthenia gravis (MG), and the relationships among symptoms, activities and environmental factors (EF), by using WHO's International Classification of Functioning Disability and Health (ICF). Patients were consecutively enrolled at the Besta Institute of Milan, Italy.…

78 FR 34915 - Approval and Promulgation of Air Quality Implementation Plans; Virginia; Revision to the...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-11

... ENVIRONMENTAL PROTECTION AGENCY 40 CFR Part 52 [EPA-R03-OAR-2013-0289; FRL-9822-3] Approval and Promulgation of Air Quality Implementation Plans; Virginia; Revision to the Classification and Implementation... approving these revisions to include the classification of Northern Virginia as ``marginal'' for the 2008...

Fuel Characteristic Classification System version 3.0: technical documentation

Treesearch

Susan J. Prichard; David V. Sandberg; Roger D. Ottmar; Ellen Eberhardt; Anne Andreu; Paige Eagle; Kjell Swedin

2013-01-01

The Fuel Characteristic Classification System (FCCS) is a software module that records wildland fuel characteristics and calculates potential fire behavior and hazard potentials based on input environmental variables. The FCCS 3.0 is housed within the Integrated Fuels Treatment Decision Support System (Joint Fire Science Program 2012). It can also be run from command...

Ecosystem classification, Chapter 2

Treesearch

M.J. Robin-Abbott; L.H. Pardo

2011-01-01

The ecosystem classification in this report is based on the ecoregions developed through the Commission for Environmental Cooperation (CEC) for North America (CEC 1997). Only ecosystems that occur in the United States are included. CEC ecoregions are described, with slight modifications, below (CEC 1997) and shown in Figures 2.1 and 2.2. We chose this ecosystem...

40 CFR 164.21 - Contents of a denial of registration, notice of intent to cancel a registration, or notice of...

Code of Federal Regulations, 2011 CFR

2011-07-01

..., notice of intent to cancel a registration, or notice of intent to change a classification. 164.21 Section 164.21 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES..., ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS...

Predicting fire severity using surface fuels and moisture

Treesearch

Pamela G. Sikkink; Robert E. Keane

2012-01-01

Fire severity classifications have been used extensively in fire management over the last 30 years to describe specific environmental or ecological impacts of fire on fuels, vegetation, wildlife, and soils in recently burned areas. New fire severity classifications need to be more objective, predictive, and ultimately more useful to fire management and planning. Our...

Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

NASA Technical Reports Server (NTRS)

Logan, T. L.; Huning, J. R.; Glackin, D. L.

1983-01-01

The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

An alternative respiratory sounds classification system utilizing artificial neural networks.

PubMed

Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen

2015-01-01

Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

Using two classification schemes to develop vegetation indices of biological integrity for wetlands in West Virginia, USA.

PubMed

Veselka, Walter; Rentch, James S; Grafton, William N; Kordek, Walter S; Anderson, James T

2010-11-01

Bioassessment methods for wetlands, and other bodies of water, have been developed worldwide to measure and quantify changes in "biological integrity." These assessments are based on a classification system, meant to ensure appropriate comparisons between wetland types. Using a local site-specific disturbance gradient, we built vegetation indices of biological integrity (Veg-IBIs) based on two commonly used wetland classification systems in the USA: One based on vegetative structure and the other based on a wetland's position in a landscape and sources of water. The resulting class-specific Veg-IBIs were comprised of 1-5 metrics that varied in their sensitivity to the disturbance gradient (R2=0.14-0.65). Moreover, the sensitivity to the disturbance gradient increased as metrics from each of the two classification schemes were combined (added). Using this information to monitor natural and created wetlands will help natural resource managers track changes in biological integrity of wetlands in response to anthropogenic disturbance and allows the use of vegetative communities to set ecological performance standards for mitigation banks.

Application of NEPA to nuclear weapons production, storage, and testing Weinberger v. Catholic Action of Hawaii/Peace Education Project

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

Sauber, A.J.

The National Environmental Policy Act (NEPA) requirement of environmental impact statements for the testing of military equipment, specifically nuclear weapons, conflicts with national security objectives. The author examines NEPA and the Freedom of Information Act (FOIA) in terms of the environmental effects of weapons testing and the relevant case law. The Supreme Court's decision in Catholic Action of Hawaii/Peace Education Project sought to resolve the conflict by distinguishing between a project which is contemplated and one which is proposed. The classification scheme embodied in the FOIA exemption for national security may cause unwarranted frustration of NEPA's goals. The author outlinesmore » a new classification system and review mechanism that could curb military abuse in this area.« less

Environmental/chemical thesaurus

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

Shriner, C.R.; Dailey, N.S.; Jordan, A.C.

The Environmental/Chemical Thesaurus approaches scientific language control problems from a multidisciplinary view. The Environmental/Biomedical Terminology Index (EBTI) was used as a base for the present thesaurus. The Environmental/Chemical Thesaurus, funded by the Environmental Protection Agency, used as its source of new terms those major terms found in 13 Environmental Protection Agency data bases. The scope of this thesaurus includes not only environmental and biomedical sciences, but also the physical sciences with emphasis placed on chemistry. Specific chemical compounds are not included; only classes of chemicals are given. To adhere to this level of classification, drugs and pesticides are identified bymore » class rather than by specific chemical name. An attempt was also made to expand the areas of sociology and economics. Terminology dealing with law, demography, and geography was expanded. Proper names of languages and races were excluded. Geographic terms were expanded to include proper names for oceans, continents, major lakes, rivers, and islands. Political divisions were added to allow for proper names of countries and states. With such a broad scope, terminology for specific sciences does not provide for indexing to the lowest levels in plant, animal, or chemical classifications.« less

Regional estimates of reef carbonate dynamics and productivity Using Landsat 7 ETM+, and potential impacts from ocean acidification

USGS Publications Warehouse

Moses, C.S.; Andrefouet, S.; Kranenburg, C.; Muller-Karger, F. E.

2009-01-01

Using imagery at 30 m spatial resolution from the most recent Landsat satellite, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), we scale up reef metabolic productivity and calcification from local habitat-scale (10 -1 to 100 km2) measurements to regional scales (103 to 104 km2). Distribution and spatial extent of the North Florida Reef Tract (NFRT) habitats come from supervised classification of the Landsat imagery within independent Landsat-derived Millennium Coral Reef Map geomorphologic classes. This system minimizes the depth range and variability of benthic habitat characteristics found in the area of supervised classification and limits misclassification. Classification of Landsat imagery into 5 biotopes (sand, dense live cover, sparse live cover, seagrass, and sparse seagrass) by geomorphologic class is >73% accurate at regional scales. Based on recently published habitat-scale in situ metabolic measurements, gross production (P = 3.01 ?? 109 kg C yr -1), excess production (E = -5.70 ?? 108 kg C yr -1), and calcification (G = -1.68 ?? 106 kg CaCO 3 yr-1) are estimated over 2711 km2 of the NFRT. Simple models suggest sensitivity of these values to ocean acidification, which will increase local dissolution of carbonate sediments. Similar approaches could be applied over large areas with poorly constrained bathymetry or water column properties and minimal metabolic sampling. This tool has potential applications for modeling and monitoring large-scale environmental impacts on reef productivity, such as the influence of ocean acidification on coral reef environments. ?? Inter-Research 2009.

Classification of highly unbalanced CYP450 data of drugs using cost sensitive machine learning techniques.

PubMed

Eitrich, T; Kless, A; Druska, C; Meyer, W; Grotendorst, J

2007-01-01

In this paper, we study the classifications of unbalanced data sets of drugs. As an example we chose a data set of 2D6 inhibitors of cytochrome P450. The human cytochrome P450 2D6 isoform plays a key role in the metabolism of many drugs in the preclinical drug discovery process. We have collected a data set from annotated public data and calculated physicochemical properties with chemoinformatics methods. On top of this data, we have built classifiers based on machine learning methods. Data sets with different class distributions lead to the effect that conventional machine learning methods are biased toward the larger class. To overcome this problem and to obtain sensitive but also accurate classifiers we combine machine learning and feature selection methods with techniques addressing the problem of unbalanced classification, such as oversampling and threshold moving. We have used our own implementation of a support vector machine algorithm as well as the maximum entropy method. Our feature selection is based on the unsupervised McCabe method. The classification results from our test set are compared structurally with compounds from the training set. We show that the applied algorithms enable the effective high throughput in silico classification of potential drug candidates.

Intermittent Turbulence in the Stable Boundary Layer over Land. Part III: A Classification for Observations during CASES-99.

NASA Astrophysics Data System (ADS)

van de Wiel, B. J. H.; Moene, A. F.; Hartogensis, O. K.; de Bruin, H. A. R.; Holtslag, A. A. M.

2003-10-01

In this paper a classification of stable boundary layer regimes is presented based on observations of near-surface turbulence during the Cooperative Atmosphere-Surface Exchange Study-1999 (CASES-99). It is found that the different nights can be divided into three subclasses: a turbulent regime, an intermittent regime, and a radiative regime, which confirms the findings of two companion papers that use a simplified theoretical model (it is noted that its simpliflied structure limits the model generality to near-surface flows). The papers predict the occurrence of stable boundary layer regimes in terms of external forcing parameters such as the (effective) pressure gradient and radiative forcing. The classification in the present work supports these predictions and shows that the predictions are robust in a qualitative sense. As such, it is, for example, shown that intermittent turbulence is most likely to occur in clear-sky conditions with a moderately weak effective pressure gradient. The quantitative features of the theoretical classification are, however, rather sensitive to (often uncertain) local parameter estimations, such as the bulk heat conductance of the vegetation layer. This sensitivity limits the current applicability of the theoretical classification in a strict quantitative sense, apart from its conceptual value.

Detection of mastitis in dairy cattle by use of mixture models for repeated somatic cell scores: a Bayesian approach via Gibbs sampling.

PubMed

Odegård, J; Jensen, J; Madsen, P; Gianola, D; Klemetsdal, G; Heringstad, B

2003-11-01

The distribution of somatic cell scores could be regarded as a mixture of at least two components depending on a cow's udder health status. A heteroscedastic two-component Bayesian normal mixture model with random effects was developed and implemented via Gibbs sampling. The model was evaluated using datasets consisting of simulated somatic cell score records. Somatic cell score was simulated as a mixture representing two alternative udder health statuses ("healthy" or "diseased"). Animals were assigned randomly to the two components according to the probability of group membership (Pm). Random effects (additive genetic and permanent environment), when included, had identical distributions across mixture components. Posterior probabilities of putative mastitis were estimated for all observations, and model adequacy was evaluated using measures of sensitivity, specificity, and posterior probability of misclassification. Fitting different residual variances in the two mixture components caused some bias in estimation of parameters. When the components were difficult to disentangle, so were their residual variances, causing bias in estimation of Pm and of location parameters of the two underlying distributions. When all variance components were identical across mixture components, the mixture model analyses returned parameter estimates essentially without bias and with a high degree of precision. Including random effects in the model increased the probability of correct classification substantially. No sizable differences in probability of correct classification were found between models in which a single cow effect (ignoring relationships) was fitted and models where this effect was split into genetic and permanent environmental components, utilizing relationship information. When genetic and permanent environmental effects were fitted, the between-replicate variance of estimates of posterior means was smaller because the model accounted for random genetic drift.

The power of timing: Adding a time-to-completion cutoff to the Word Choice Test and Recognition Memory Test improves classification accuracy.

PubMed

Erdodi, Laszlo A; Tyson, Bradley T; Shahein, Ayman G; Lichtenstein, Jonathan D; Abeare, Christopher A; Pelletier, Chantalle L; Zuccato, Brandon G; Kucharski, Brittany; Roth, Robert M

2017-05-01

The Recognition Memory Test (RMT) and Word Choice Test (WCT) are structurally similar, but psychometrically different. Previous research demonstrated that adding a time-to-completion cutoff improved the classification accuracy of the RMT. However, the contribution of WCT time-cutoffs to improve the detection of invalid responding has not been investigated. The present study was designed to evaluate the classification accuracy of time-to-completion on the WCT compared to the accuracy score and the RMT. Both tests were administered to 202 adults (M age  = 45.3 years, SD = 16.8; 54.5% female) clinically referred for neuropsychological assessment in counterbalanced order as part of a larger battery of cognitive tests. Participants obtained lower and more variable scores on the RMT (M = 44.1, SD = 7.6) than on the WCT (M = 46.9, SD = 5.7). Similarly, they took longer to complete the recognition trial on the RMT (M = 157.2 s,SD = 71.8) than the WCT (M = 137.2 s, SD = 75.7). The optimal cutoff on the RMT (≤43) produced .60 sensitivity at .87 specificity. The optimal cutoff on the WCT (≤47) produced .57 sensitivity at .87 specificity. Time-cutoffs produced comparable classification accuracies for both RMT (≥192 s; .48 sensitivity at .88 specificity) and WCT (≥171 s; .49 sensitivity at .91 specificity). They also identified an additional 6-10% of the invalid profiles missed by accuracy score cutoffs, while maintaining good specificity (.93-.95). Functional equivalence was reached at accuracy scores ≤43 (RMT) and ≤47 (WCT) or time-to-completion ≥192 s (RMT) and ≥171 s (WCT). Time-to-completion cutoffs are valuable additions to both tests. They can function as independent validity indicators or enhance the sensitivity of accuracy scores without requiring additional measures or extending standard administration time.

Integrative analysis of environmental sequences using MEGAN4.

PubMed

Huson, Daniel H; Mitra, Suparna; Ruscheweyh, Hans-Joachim; Weber, Nico; Schuster, Stephan C

2011-09-01

A major challenge in the analysis of environmental sequences is data integration. The question is how to analyze different types of data in a unified approach, addressing both the taxonomic and functional aspects. To facilitate such analyses, we have substantially extended MEGAN, a widely used taxonomic analysis program. The new program, MEGAN4, provides an integrated approach to the taxonomic and functional analysis of metagenomic, metatranscriptomic, metaproteomic, and rRNA data. While taxonomic analysis is performed based on the NCBI taxonomy, functional analysis is performed using the SEED classification of subsystems and functional roles or the KEGG classification of pathways and enzymes. A number of examples illustrate how such analyses can be performed, and show that one can also import and compare classification results obtained using others' tools. MEGAN4 is freely available for academic purposes, and installers for all three major operating systems can be downloaded from www-ab.informatik.uni-tuebingen.de/software/megan.

40 CFR 164.7 - Ex parte discussion of proceeding.

Code of Federal Regulations, 2011 CFR

2011-07-01

....7 Section 164.7 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE... ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... Environmental Appeals Board, the Presiding Officer, or the Administrative Law Judge discuss ex parte the merits...

40 CFR 164.102 - Appeals from accelerated decisions.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... 164.102 Section 164.102 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... of the submission of briefs, but the Environmental Appeals Board may allow additional briefs and oral...

40 CFR 164.7 - Ex parte discussion of proceeding.

Code of Federal Regulations, 2010 CFR

2010-07-01

....7 Section 164.7 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE... ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... Environmental Appeals Board, the Presiding Officer, or the Administrative Law Judge discuss ex parte the merits...

40 CFR 164.102 - Appeals from accelerated decisions.

Code of Federal Regulations, 2011 CFR

2011-07-01

.... 164.102 Section 164.102 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... of the submission of briefs, but the Environmental Appeals Board may allow additional briefs and oral...

40 CFR 11.2 - Background.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 1 2010-07-01 2010-07-01 false Background. 11.2 Section 11.2 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY GENERAL SECURITY CLASSIFICATION REGULATIONS PURSUANT TO EXECUTIVE ORDER 11652 § 11.2 Background. While the Environmental Protection Agency does not...

Application of the Coastal and Marine Ecological Classification Standard to ROV Video Data for Enhanced Analysis of Deep-Sea Habitats in the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Ruby, C.; Skarke, A. D.; Mesick, S.

2016-02-01

The Coastal and Marine Ecological Classification Standard (CMECS) is a network of common nomenclature that provides a comprehensive framework for organizing physical, biological, and chemical information about marine ecosystems. It was developed by the National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center, in collaboration with other feral agencies and academic institutions, as a means for scientists to more easily access, compare, and integrate marine environmental data from a wide range of sources and time frames. CMECS has been endorsed by the Federal Geographic Data Committee (FGDC) as a national metadata standard. The research presented here is focused on the application of CMECS to deep-sea video and environmental data collected by the NOAA ROV Deep Discoverer and the NOAA Ship Okeanos Explorer in the Gulf of Mexico in 2011-2014. Specifically, a spatiotemporal index of the physical, chemical, biological, and geological features observed in ROV video records was developed in order to allow scientist, otherwise unfamiliar with the specific content of existing video data, to rapidly determine the abundance and distribution of features of interest, and thus evaluate the applicability of those video data to their research. CMECS units (setting, component, or modifier) for seafloor images extracted from high-definition ROV video data were established based upon visual assessment as well as analysis of coincident environmental sensor (temperature, conductivity), navigation (ROV position, depth, attitude), and log (narrative dive summary) data. The resulting classification units were integrated into easily searchable textual and geo-databases as well as an interactive web map. The spatial distribution and associations of deep-sea habitats as indicated by CMECS classifications are described and optimized methodological approaches for application of CMECS to deep-sea video and environmental data are presented.

Centrifuge: rapid and sensitive classification of metagenomic sequences

PubMed Central

Song, Li; Breitwieser, Florian P.

2016-01-01

Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space. PMID:27852649

Sensitivity of Raman spectroscopy to normal patient variability

NASA Astrophysics Data System (ADS)

Vargis, Elizabeth; Byrd, Teresa; Logan, Quinisha; Khabele, Dineo; Mahadevan-Jansen, Anita

2011-11-01

Many groups have used Raman spectroscopy for diagnosing cervical dysplasia; however, there have been few studies looking at the effect of normal physiological variations on Raman spectra. We assess four patient variables that may affect normal Raman spectra: Race/ethnicity, body mass index (BMI), parity, and socioeconomic status. Raman spectra were acquired from a diverse population of 75 patients undergoing routine screening for cervical dysplasia. Classification of Raman spectra from patients with a normal cervix is performed using sparse multinomial logistic regression (SMLR) to determine if any of these variables has a significant effect. Results suggest that BMI and parity have the greatest impact, whereas race/ethnicity and socioeconomic status have a limited effect. Incorporating BMI and obstetric history into classification algorithms may increase sensitivity and specificity rates of disease classification using Raman spectroscopy. Studies are underway to assess the effect of these variables on disease.

Evaluation Methodology between Globalization and Localization Features Approaches for Skin Cancer Lesions Classification

NASA Astrophysics Data System (ADS)

Ahmed, H. M.; Al-azawi, R. J.; Abdulhameed, A. A.

2018-05-01

Huge efforts have been put in the developing of diagnostic methods to skin cancer disease. In this paper, two different approaches have been addressed for detection the skin cancer in dermoscopy images. The first approach uses a global method that uses global features for classifying skin lesions, whereas the second approach uses a local method that uses local features for classifying skin lesions. The aim of this paper is selecting the best approach for skin lesion classification. The dataset has been used in this paper consist of 200 dermoscopy images from Pedro Hispano Hospital (PH2). The achieved results are; sensitivity about 96%, specificity about 100%, precision about 100%, and accuracy about 97% for globalization approach while, sensitivity about 100%, specificity about 100%, precision about 100%, and accuracy about 100% for Localization Approach, these results showed that the localization approach achieved acceptable accuracy and better than globalization approach for skin cancer lesions classification.

REGIONAL ASSESSMENT OF AQUIFER VULNERABILITY AND SENSITIVITY IN THE CONTERMINOUS UNITED STATES

EPA Science Inventory

This report provides, in a generalized, largely graphic format, are presentation of ground-water vulnerability, precipitation distribution, population density, potential well yield, and aquifer sensitivity for each of the 48 conterminous states. Classification scheme is developed...

Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies.

PubMed

Zheng, Wenjing; Balzer, Laura; van der Laan, Mark; Petersen, Maya

2018-01-30

Binary classification problems are ubiquitous in health and social sciences. In many cases, one wishes to balance two competing optimality considerations for a binary classifier. For instance, in resource-limited settings, an human immunodeficiency virus prevention program based on offering pre-exposure prophylaxis (PrEP) to select high-risk individuals must balance the sensitivity of the binary classifier in detecting future seroconverters (and hence offering them PrEP regimens) with the total number of PrEP regimens that is financially and logistically feasible for the program. In this article, we consider a general class of constrained binary classification problems wherein the objective function and the constraint are both monotonic with respect to a threshold. These include the minimization of the rate of positive predictions subject to a minimum sensitivity, the maximization of sensitivity subject to a maximum rate of positive predictions, and the Neyman-Pearson paradigm, which minimizes the type II error subject to an upper bound on the type I error. We propose an ensemble approach to these binary classification problems based on the Super Learner methodology. This approach linearly combines a user-supplied library of scoring algorithms, with combination weights and a discriminating threshold chosen to minimize the constrained optimality criterion. We then illustrate the application of the proposed classifier to develop an individualized PrEP targeting strategy in a resource-limited setting, with the goal of minimizing the number of PrEP offerings while achieving a minimum required sensitivity. This proof of concept data analysis uses baseline data from the ongoing Sustainable East Africa Research in Community Health study. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data.

PubMed

Kroenke, Candyce H; Chubak, Jessica; Johnson, Lisa; Castillo, Adrienne; Weltzien, Erin; Caan, Bette J

2016-03-01

The utility of data-based algorithms in research has been questioned because of errors in identification of cancer recurrences. We adapted previously published breast cancer recurrence algorithms, selectively using medical record (MR) data to improve classification. We evaluated second breast cancer event (SBCE) and recurrence-specific algorithms previously published by Chubak and colleagues in 1535 women from the Life After Cancer Epidemiology (LACE) and 225 women from the Women's Health Initiative cohorts and compared classification statistics to published values. We also sought to improve classification with minimal MR examination. We selected pairs of algorithms-one with high sensitivity/high positive predictive value (PPV) and another with high specificity/high PPV-using MR information to resolve discrepancies between algorithms, properly classifying events based on review; we called this "triangulation." Finally, in LACE, we compared associations between breast cancer survival risk factors and recurrence using MR data, single Chubak algorithms, and triangulation. The SBCE algorithms performed well in identifying SBCE and recurrences. Recurrence-specific algorithms performed more poorly than published except for the high-specificity/high-PPV algorithm, which performed well. The triangulation method (sensitivity = 81.3%, specificity = 99.7%, PPV = 98.1%, NPV = 96.5%) improved recurrence classification over two single algorithms (sensitivity = 57.1%, specificity = 95.5%, PPV = 71.3%, NPV = 91.9%; and sensitivity = 74.6%, specificity = 97.3%, PPV = 84.7%, NPV = 95.1%), with 10.6% MR review. Triangulation performed well in survival risk factor analyses vs analyses using MR-identified recurrences. Use of multiple recurrence algorithms in administrative data, in combination with selective examination of MR data, may improve recurrence data quality and reduce research costs. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data

PubMed Central

Chubak, Jessica; Johnson, Lisa; Castillo, Adrienne; Weltzien, Erin; Caan, Bette J.

2016-01-01

Abstract Background: The utility of data-based algorithms in research has been questioned because of errors in identification of cancer recurrences. We adapted previously published breast cancer recurrence algorithms, selectively using medical record (MR) data to improve classification. Methods: We evaluated second breast cancer event (SBCE) and recurrence-specific algorithms previously published by Chubak and colleagues in 1535 women from the Life After Cancer Epidemiology (LACE) and 225 women from the Women’s Health Initiative cohorts and compared classification statistics to published values. We also sought to improve classification with minimal MR examination. We selected pairs of algorithms—one with high sensitivity/high positive predictive value (PPV) and another with high specificity/high PPV—using MR information to resolve discrepancies between algorithms, properly classifying events based on review; we called this “triangulation.” Finally, in LACE, we compared associations between breast cancer survival risk factors and recurrence using MR data, single Chubak algorithms, and triangulation. Results: The SBCE algorithms performed well in identifying SBCE and recurrences. Recurrence-specific algorithms performed more poorly than published except for the high-specificity/high-PPV algorithm, which performed well. The triangulation method (sensitivity = 81.3%, specificity = 99.7%, PPV = 98.1%, NPV = 96.5%) improved recurrence classification over two single algorithms (sensitivity = 57.1%, specificity = 95.5%, PPV = 71.3%, NPV = 91.9%; and sensitivity = 74.6%, specificity = 97.3%, PPV = 84.7%, NPV = 95.1%), with 10.6% MR review. Triangulation performed well in survival risk factor analyses vs analyses using MR-identified recurrences. Conclusions: Use of multiple recurrence algorithms in administrative data, in combination with selective examination of MR data, may improve recurrence data quality and reduce research costs. PMID:26582243

Spectral feature variations in x-ray diffraction imaging systems

NASA Astrophysics Data System (ADS)

Wolter, Scott D.; Greenberg, Joel A.

2016-05-01

Materials with different atomic or molecular structures give rise to unique scatter spectra when measured by X-ray diffraction. The details of these spectra, though, can vary based on both intrinsic (e.g., degree of crystallinity or doping) and extrinsic (e.g., pressure or temperature) conditions. While this sensitivity is useful for detailed characterizations of the material properties, these dependences make it difficult to perform more general classification tasks, such as explosives threat detection in aviation security. A number of challenges, therefore, currently exist for reliable substance detection including the similarity in spectral features among some categories of materials combined with spectral feature variations from materials processing and environmental factors. These factors complicate the creation of a material dictionary and the implementation of conventional classification and detection algorithms. Herein, we report on two prominent factors that lead to variations in spectral features: crystalline texture and temperature variations. Spectral feature comparisons between materials categories will be described for solid metallic sheet, aqueous liquids, polymer sheet, and metallic, organic, and inorganic powder specimens. While liquids are largely immune to texture effects, they are susceptible to temperature changes that can modify their density or produce phase changes. We will describe in situ temperature-dependent measurement of aqueous-based commercial goods in the temperature range of -20°C to 35°C.

Hydrological classification of natural flow regimes to support environmental flow assessments in intensively regulated Mediterranean rivers, Segura River Basin (Spain).

PubMed

Belmar, Oscar; Velasco, Josefa; Martinez-Capel, Francisco

2011-05-01

Hydrological classification constitutes the first step of a new holistic framework for developing regional environmental flow criteria: the "Ecological Limits of Hydrologic Alteration (ELOHA)". The aim of this study was to develop a classification for 390 stream sections of the Segura River Basin based on 73 hydrological indices that characterize their natural flow regimes. The hydrological indices were calculated with 25 years of natural monthly flows (1980/81-2005/06) derived from a rainfall-runoff model developed by the Spanish Ministry of Environment and Public Works. These indices included, at a monthly or annual basis, measures of duration of droughts and central tendency and dispersion of flow magnitude (average, low and high flow conditions). Principal Component Analysis (PCA) indicated high redundancy among most hydrological indices, as well as two gradients: flow magnitude for mainstream rivers and temporal variability for tributary streams. A classification with eight flow-regime classes was chosen as the most easily interpretable in the Segura River Basin, which was supported by ANOSIM analyses. These classes can be simplified in 4 broader groups, with different seasonal discharge pattern: large rivers, perennial stable streams, perennial seasonal streams and intermittent and ephemeral streams. They showed a high degree of spatial cohesion, following a gradient associated with climatic aridity from NW to SE, and were well defined in terms of the fundamental variables in Mediterranean streams: magnitude and temporal variability of flows. Therefore, this classification is a fundamental tool to support water management and planning in the Segura River Basin. Future research will allow us to study the flow alteration-ecological response relationship for each river type, and set the basis to design scientifically credible environmental flows following the ELOHA framework.

Enzymatic Decontamination of Environmental Organophosphorus Compounds

DTIC Science & Technology

2006-12-04

ABSTRACT (Maximum 200 words) The abstract is below since many authors do not follow the 200 word limit 14. SUBJECT TERMS organophosphorus compounds ...5404 Enzymatic decontamination of environmental organophosphorus compounds REPORT DOCUMENTATION PAGE 18. SECURITY CLASSIFICATION ON THIS PAGE...239-18 298-102 15. NUMBER OF PAGES 20. LIMITATION OF ABSTRACT UL - 4-Dec-2006 Enzymatic decontamination of environmental organophosphorus compounds

The Integrative Studies of Genetic and Environmental Factors in Systemic Sclerosis

DTIC Science & Technology

2008-05-01

15. SUBJECT TERMS Scleroderma (SSc), fibroblasts, fibrosis, silica, environmental particles, susceptibility. 16. SECURITY CLASSIFICATION OF...factors in a viable system - human fibroblasts. Fibroblasts with a scleroderma (SSc) susceptible genetic background may be more vulnerable to...for understanding environmental contributions to fibrosing diseases such as scleroderma (SSc). Third, in the studies of specific biological

Accounting for data variability, a key factor in in vivo/in vitro relationships: application to the skin sensitization potency (in vivo LLNA versus in vitro DPRA) example.

PubMed

Dimitrov, S; Detroyer, A; Piroird, C; Gomes, C; Eilstein, J; Pauloin, T; Kuseva, C; Ivanova, H; Popova, I; Karakolev, Y; Ringeissen, S; Mekenyan, O

2016-12-01

When searching for alternative methods to animal testing, confidently rescaling an in vitro result to the corresponding in vivo classification is still a challenging problem. Although one of the most important factors affecting good correlation is sample characteristics, they are very rarely integrated into correlation studies. Usually, in these studies, it is implicitly assumed that both compared values are error-free numbers, which they are not. In this work, we propose a general methodology to analyze and integrate data variability and thus confidence estimation when rescaling from one test to another. The methodology is demonstrated through the case study of rescaling the in vitro Direct Peptide Reactivity Assay (DPRA) reactivity to the in vivo Local Lymph Node Assay (LLNA) skin sensitization potency classifications. In a first step, a comprehensive statistical analysis evaluating the reliability and variability of LLNA and DPRA as such was done. These results allowed us to link the concept of gray zones and confidence probability, which in turn represents a new perspective for a more precise knowledge of the classification of chemicals within their in vivo OR in vitro test. Next, the novelty and practical value of our methodology introducing variability into the threshold optimization between the in vitro AND in vivo test resides in the fact that it attributes a confidence probability to the predicted classification. The methodology, classification and screening approach presented in this study are not restricted to skin sensitization only. They could be helpful also for fate, toxicity and health hazard assessment where plenty of in vitro and in chemico assays and/or QSARs models are available. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Sleep staging with movement-related signals.

PubMed

Jansen, B H; Shankar, K

1993-05-01

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.

Observations and Modelling of Alternative Tree Cover States of the Boreal Ecosystem

NASA Astrophysics Data System (ADS)

Abis, B.; Brovkin, V.

2017-12-01

Recently, multimodality of the tree cover distribution of the boreal forests has been detected, revealing the existence of three alternative vegetation modes. Identifying which are the regions with a potential for alternative tree cover states, and assessing which are the main factors underlying their existence, is important to project future change of natural vegetation cover and its effect on climate.Through the use of generalised additive models and phase-space analysis, we study the link between tree cover distribution and eight globally-observed environmental factors, such as rainfall, temperature, and permafrost distribution. Using a classification based on these factors, we show the location of areas with potentially alternative tree cover states under the same environmental conditions in the boreal region. Furthermore, to explain the multimodality found in the data and the asymmetry between North America and Eurasia, we study a conceptual model based on tree species competition, and use it to simulate the sensitivity of tree cover to changes in environmental factors.We find that the link between individual environmental variables and tree cover differs regionally. Nonetheless, environmental conditions uniquely determine the vegetation state among the three dominant modes in ˜95% of the cases. On the other hand, areas with potentially alternative tree cover states encompass ˜1.1 million km2, and correspond to possible transition zones with a reduced resilience to disturbances. Employing our conceptual model, we show that multimodality can be explained through competition between tree species with different adaptations to environmental factors and disturbances. Moreover, the model is able to reproduce the asymmetry in tree species distribution between Eurasia and North America. Finally, we find that changes in permafrost could be associated with bifurcation points of the model, corroborating the importance of permafrost in a changing climate.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models.

PubMed

Mulder, Han A; Rönnegård, Lars; Fikse, W Freddy; Veerkamp, Roel F; Strandberg, Erling

2013-07-04

Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike's information criterion using h-likelihood to select the best fitting model. We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike's information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike's information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Multi-scale Visualization of Remote Sensing and Topographic Data of the Amazon Rain Forest for Environmental Monitoring of the Petroleum Industry.

NASA Astrophysics Data System (ADS)

Fonseca, L.; Miranda, F. P.; Beisl, C. H.; Souza-Fonseca, J.

2002-12-01

PETROBRAS (the Brazilian national oil company) built a pipeline to transport crude oil from the Urucu River region to a terminal in the vicinities of Coari, a city located in the right margin of the Solimoes River. The oil is then shipped by tankers to another terminal in Manaus, capital city of the Amazonas state. At the city of Coari, changes in water level between dry and wet seasons reach up to 14 meters. This strong seasonal character of the Amazonian climate gives rise to four distinct scenarios in the annual hydrological cycle: low water, high water, receding water, and rising water. These scenarios constitute the main reference for the definition of oil spill response planning in the region, since flooded forests and flooded vegetation are the most sensitive fluvial environments to oil spills. This study focuses on improving information about oil spill environmental sensitivity in Western Amazon by using 3D visualization techniques to help the analysis and interpretation of remote sensing and digital topographic data, as follows: (a) 1995 low flood and 1996 high flood JERS-1 SAR mosaics, band LHH, 100m pixel; (b) 2000 low flood and 2001 high flood RADARSAT-1 W1 images, band CHH, 30m pixel; (c) 2002 high flood airborne SAR images from the SIVAM project (System for Surveillance of the Amazon), band LHH, 3m pixel and band XHH, 6m pixel; (d) GTOPO30 digital elevation model, 30' resolution; (e) Digital elevation model derived from topographic information acquired during seismic surveys, 25m resolution; (f) panoramic views obtained from low altitude helicopter flights. The methodology applied includes image processing, cartographic conversion and generation of value-added product using 3D visualization. A semivariogram textural classification was applied to the SAR images in order to identify areas of flooded forest and flooded vegetation. The digital elevation models were color shaded to highlight subtle topographic features. Both datasets were then converted to the same cartographic projection and inserted into the Fledermaus 3D visualization environment. 3D visualization proved to be an important aid in understanding the spatial distribution pattern of the environmentally sensitive vegetation cover. The dynamics of the hydrological cycle was depicted in a basin-wide scale, revealing new geomorphic information relevant to assess the environmental risk of oil spills. Results demonstrate that pipelines constitute an environmentally saver option for oil transportation in the region when compared to fluvial tanker routes.

Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears.

PubMed

Das, D K; Maiti, A K; Chakraborty, C

2015-03-01

In this paper, we propose a comprehensive image characterization cum classification framework for malaria-infected stage detection using microscopic images of thin blood smears. The methodology mainly includes microscopic imaging of Leishman stained blood slides, noise reduction and illumination correction, erythrocyte segmentation, feature selection followed by machine classification. Amongst three-image segmentation algorithms (namely, rule-based, Chan-Vese-based and marker-controlled watershed methods), marker-controlled watershed technique provides better boundary detection of erythrocytes specially in overlapping situations. Microscopic features at intensity, texture and morphology levels are extracted to discriminate infected and noninfected erythrocytes. In order to achieve subgroup of potential features, feature selection techniques, namely, F-statistic and information gain criteria are considered here for ranking. Finally, five different classifiers, namely, Naive Bayes, multilayer perceptron neural network, logistic regression, classification and regression tree (CART), RBF neural network have been trained and tested by 888 erythrocytes (infected and noninfected) for each features' subset. Performance evaluation of the proposed methodology shows that multilayer perceptron network provides higher accuracy for malaria-infected erythrocytes recognition and infected stage classification. Results show that top 90 features ranked by F-statistic (specificity: 98.64%, sensitivity: 100%, PPV: 99.73% and overall accuracy: 96.84%) and top 60 features ranked by information gain provides better results (specificity: 97.29%, sensitivity: 100%, PPV: 99.46% and overall accuracy: 96.73%) for malaria-infected stage classification. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

40 CFR 260.33 - Procedures for variances from classification as a solid waste, for variances to be classified as...

Code of Federal Regulations, 2013 CFR

2013-07-01

... classification as a solid waste, for variances to be classified as a boiler, or for non-waste determinations. 260.33 Section 260.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking Petitions § 260.33 Procedures for variances...

40 CFR 260.33 - Procedures for variances from classification as a solid waste, for variances to be classified as...

Code of Federal Regulations, 2014 CFR

2014-07-01

... classification as a solid waste, for variances to be classified as a boiler, or for non-waste determinations. 260.33 Section 260.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking Petitions § 260.33 Procedures for variances...

40 CFR 260.33 - Procedures for variances from classification as a solid waste, for variances to be classified as...

Code of Federal Regulations, 2012 CFR

2012-07-01

... classification as a solid waste, for variances to be classified as a boiler, or for non-waste determinations. 260.33 Section 260.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking Petitions § 260.33 Procedures for variances...

40 CFR 164.23 - Contents of the statement of issues to accompany notice of intent to hold a hearing.

Code of Federal Regulations, 2011 CFR

2011-07-01

... accompany notice of intent to hold a hearing. 164.23 Section 164.23 Protection of Environment ENVIRONMENTAL..., CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF REGISTRATIONS AND OTHER HEARINGS CALLED PURSUANT TO SECTION 6... registration should be canceled or its classification changed, whether its composition is such as to warrant...

40 CFR 164.23 - Contents of the statement of issues to accompany notice of intent to hold a hearing.

Code of Federal Regulations, 2010 CFR

2010-07-01

... accompany notice of intent to hold a hearing. 164.23 Section 164.23 Protection of Environment ENVIRONMENTAL..., CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF REGISTRATIONS AND OTHER HEARINGS CALLED PURSUANT TO SECTION 6... registration should be canceled or its classification changed, whether its composition is such as to warrant...

40 CFR 51.902 - Which classification and nonattainment area planning provisions of the CAA shall apply to areas...

Code of Federal Regulations, 2011 CFR

2011-07-01

... area planning provisions of the CAA shall apply to areas designated nonattainment for the 8-hour NAAQS? 51.902 Section 51.902 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... Implementation of 8-hour Ozone National Ambient Air Quality Standard § 51.902 Which classification and...

40 CFR 51.902 - Which classification and nonattainment area planning provisions of the CAA shall apply to areas...

Code of Federal Regulations, 2014 CFR

2014-07-01

... area planning provisions of the CAA shall apply to areas designated nonattainment for the 1997 8-hour NAAQS? 51.902 Section 51.902 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... Implementation of 8-hour Ozone National Ambient Air Quality Standard § 51.902 Which classification and...

40 CFR 51.902 - Which classification and nonattainment area planning provisions of the CAA shall apply to areas...

Code of Federal Regulations, 2012 CFR

2012-07-01

... area planning provisions of the CAA shall apply to areas designated nonattainment for the 1997 8-hour NAAQS? 51.902 Section 51.902 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... Implementation of 8-hour Ozone National Ambient Air Quality Standard § 51.902 Which classification and...

40 CFR 51.902 - Which classification and nonattainment area planning provisions of the CAA shall apply to areas...

Code of Federal Regulations, 2013 CFR

2013-07-01

... area planning provisions of the CAA shall apply to areas designated nonattainment for the 1997 8-hour NAAQS? 51.902 Section 51.902 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... Implementation of 8-hour Ozone National Ambient Air Quality Standard § 51.902 Which classification and...

40 CFR 51.902 - Which classification and nonattainment area planning provisions of the CAA shall apply to areas...

Code of Federal Regulations, 2010 CFR

2010-07-01

... area planning provisions of the CAA shall apply to areas designated nonattainment for the 8-hour NAAQS? 51.902 Section 51.902 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... Implementation of 8-hour Ozone National Ambient Air Quality Standard § 51.902 Which classification and...

40 CFR 164.111 - Procedure for disposition of motions.

Code of Federal Regulations, 2011 CFR

2011-07-01

.... 164.111 Section 164.111 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... thereafter, the Environmental Appeals Board shall announce its decision whether to grant or to deny the...

40 CFR 164.103 - Final decision or order on appeal or review.

Code of Federal Regulations, 2011 CFR

2011-07-01

... review. 164.103 Section 164.103 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... of an accelerated decision, unless otherwise stipulated by the parties, the Environmental Appeals...

40 CFR 164.103 - Final decision or order on appeal or review.

Code of Federal Regulations, 2010 CFR

2010-07-01

... review. 164.103 Section 164.103 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... of an accelerated decision, unless otherwise stipulated by the parties, the Environmental Appeals...

40 CFR 164.111 - Procedure for disposition of motions.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... 164.111 Section 164.111 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... thereafter, the Environmental Appeals Board shall announce its decision whether to grant or to deny the...

44 CFR 10.9 - Preparation of environmental assessments.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Preparation of environmental assessments. 10.9 Section 10.9 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY... quickly; (4) Likelihood of meaningful public comment; (5) National security classification issues; (6...

Sensitivity and accuracy of high-throughput metabarcoding methods for early detection of invasive fish species

EPA Science Inventory

For early detection biomonitoring of aquatic invasive species, sensitivity to rare individuals and accurate, high-resolution taxonomic classification are critical to minimize detection errors. Given the great expense and effort associated with morphological identification of many...

Classification of bacterial samples as negative or positive for a UTI and antibiogram using surface enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Kastanos, Evdokia; Hadjigeorgiou, Katerina; Kyriakides, Alexandros; Pitris, Costas

2011-03-01

Urinary tract infection (UTI) diagnosis requires an overnight culture to identify a sample as positive or negative for a UTI. Additional cultures are required to identify the pathogen responsible for the infection and to test its sensitivity to antibiotics. A rise in ineffective treatments, chronic infections, rising health care costs and antibiotic resistance are some of the consequences of this prolonged waiting period of UTI diagnosis. In this work, Surface Enhanced Raman Spectroscopy (SERS) is used for classifying bacterial samples as positive or negative for UTI. SERS spectra of serial dilutions of E.coli bacteria, isolated from a urine culture, were classified as positive (105-108 cells/ml) or negative (103-104 cells/ml) for UTI after mixing samples with gold nanoparticles. A leave-one-out cross validation was performed using the first two principal components resulting in the correct classification of 82% of all samples. Sensitivity of classification was 88% and specificity was 67%. Antibiotic sensitivity testing was also done using SERS spectra of various species of gram negative bacteria collected 4 hours after exposure to antibiotics. Spectral analysis revealed clear separation between the spectra of samples exposed to ciprofloxacin (sensitive) and amoxicillin (resistant). This study can become the basis for identifying urine samples as positive or negative for a UTI and determining their antibiogram without requiring an overnight culture.

Hyperspectral recognition of processing tomato early blight based on GA and SVM

NASA Astrophysics Data System (ADS)

Yin, Xiaojun; Zhao, SiFeng

2013-03-01

Processing tomato early blight seriously affect the yield and quality of its.Determine the leaves spectrum of different disease severity level of processing tomato early blight.We take the sensitive bands of processing tomato early blight as support vector machine input vector.Through the genetic algorithm(GA) to optimize the parameters of SVM, We could recognize different disease severity level of processing tomato early blight.The result show:the sensitive bands of different disease severity levels of processing tomato early blight is 628-643nm and 689-692nm.The sensitive bands are as the GA and SVM input vector.We get the best penalty parameters is 0.129 and kernel function parameters is 3.479.We make classification training and testing by polynomial nuclear,radial basis function nuclear,Sigmoid nuclear.The best classification model is the radial basis function nuclear of SVM. Training accuracy is 84.615%,Testing accuracy is 80.681%.It is combined GA and SVM to achieve multi-classification of processing tomato early blight.It is provided the technical support of prediction processing tomato early blight occurrence, development and diffusion rule in large areas.

Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique

PubMed Central

Fan, Mengbao; Wang, Qi; Cao, Binghua; Ye, Bo; Sunny, Ali Imam; Tian, Guiyun

2016-01-01

Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances. PMID:27164112

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

PubMed

Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

2017-06-09

Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique.

PubMed

Fan, Mengbao; Wang, Qi; Cao, Binghua; Ye, Bo; Sunny, Ali Imam; Tian, Guiyun

2016-05-07

Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances.

Accounting for both local aquatic community composition and bioavailability in setting site-specific quality standards for zinc.

PubMed

Peters, Adam; Simpson, Peter; Moccia, Alessandra

2014-01-01

Recent years have seen considerable improvement in water quality standards (QS) for metals by taking account of the effect of local water chemistry conditions on their bioavailability. We describe preliminary efforts to further refine water quality standards, by taking account of the composition of the local ecological community (the ultimate protection objective) in addition to bioavailability. Relevance of QS to the local ecological community is critical as it is important to minimise instances where quality classification using QS does not reconcile with a quality classification based on an assessment of the composition of the local ecology (e.g. using benthic macroinvertebrate quality assessment metrics such as River InVertebrate Prediction and Classification System (RIVPACS)), particularly where ecology is assessed to be at good or better status, whilst chemical quality is determined to be failing relevant standards. The alternative approach outlined here describes a method to derive a site-specific species sensitivity distribution (SSD) based on the ecological community which is expected to be present at the site in the absence of anthropogenic pressures (reference conditions). The method combines a conventional laboratory ecotoxicity dataset normalised for bioavailability with field measurements of the response of benthic macroinvertebrate abundance to chemical exposure. Site-specific QSref are then derived from the 5%ile of this SSD. Using this method, site QSref have been derived for zinc in an area impacted by historic mining activities. Application of QSref can result in greater agreement between chemical and ecological metrics of environmental quality compared with the use of either conventional (QScon) or bioavailability-based QS (QSbio). In addition to zinc, the approach is likely to be applicable to other metals and possibly other types of chemical stressors (e.g. pesticides). However, the methodology for deriving site-specific targets requires additional development and validation before they can be robustly applied during surface water classification.

Detection and Classification of Whale Acoustic Signals

NASA Astrophysics Data System (ADS)

Xian, Yin

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification. In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information. In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data. Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear. We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

Classification of ASKAP Vast Radio Light Curves

NASA Technical Reports Server (NTRS)

Rebbapragada, Umaa; Lo, Kitty; Wagstaff, Kiri L.; Reed, Colorado; Murphy, Tara; Thompson, David R.

2012-01-01

The VAST survey is a wide-field survey that observes with unprecedented instrument sensitivity (0.5 mJy or lower) and repeat cadence (a goal of 5 seconds) that will enable novel scientific discoveries related to known and unknown classes of radio transients and variables. Given the unprecedented observing characteristics of VAST, it is important to estimate source classification performance, and determine best practices prior to the launch of ASKAP's BETA in 2012. The goal of this study is to identify light curve characterization and classification algorithms that are best suited for archival VAST light curve classification. We perform our experiments on light curve simulations of eight source types and achieve best case performance of approximately 90% accuracy. We note that classification performance is most influenced by light curve characterization rather than classifier algorithm.

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

PubMed

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

40 CFR 164.122 - Final order and order of suspension.

Code of Federal Regulations, 2011 CFR

2011-07-01

.... 164.122 Section 164.122 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... Environmental Appeals Board shall issue a final decision and order. Such final order may accept or reject in...

40 CFR 164.122 - Final order and order of suspension.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... 164.122 Section 164.122 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... Environmental Appeals Board shall issue a final decision and order. Such final order may accept or reject in...

Measurement of Environmental Constructs in Disability Assessment Instruments

ERIC Educational Resources Information Center

Guscia, Roma; Ekberg, Stuart; Harries, Julia; Kirby, Neil

2006-01-01

The International Classification of Functioning, Disability and Health (ICF) assumes a biopsychosocial basis for disability and provides a framework for understanding how environmental factors contribute to the experience of disability. To determine the utility of prevalent disability assessment instruments, the authors examined the extent to…

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

18 CFR 3a.31 - Classification markings and special notations.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Confidential will be stamped in red ink, printed, or written in letters considerably larger than those used in... disclosure subject to criminal sanctions. (4) Sensitive intelligence information. For classified information or material relating to sensitive intelligence sources and methods, the following warning notice...

The Semantic Management of Environmental Resources within the Interoperable Context of the EuroGEOSS: Alignment of GEMET and the GEOSS SBAs

NASA Astrophysics Data System (ADS)

Cialone, Claudia; Stock, Kristin

2010-05-01

EuroGEOSS is a European Commission funded project. It aims at improving a scientific understanding of the complex mechanisms which drive changes affecting our planet, identifying and establishing interoperable arrangements between environmental information systems. These systems would be sustained and operated by organizations with a clear mandate and resources and rendered available following the specifications of already existent frameworks such as GEOSS (the Global Earth Observation System of systems)1 and INSPIRE (the Infrastructure for Spatial Information in the European Community)2. The EuroGEOSS project's infrastructure focuses on three thematic areas: forestry, drought and biodiversity. One of the important activities in the project is the retrieval, parsing and harmonization of the large amount of heterogeneous environmental data available at local, regional and global levels between these strategic areas. The challenge is to render it semantically and technically interoperable in a simple way. An initial step in achieving this semantic and technical interoperability involves the selection of appropriate classification schemes (for example, thesauri, ontologies and controlled vocabularies) to describe the resources in the EuroGEOSS framework. These classifications become a crucial part of the interoperable framework scaffolding because they allow data providers to describe their resources and thus support resource discovery, execution and orchestration of varying levels of complexity. However, at present, given the diverse range of environmental thesauri, controlled vocabularies and ontologies and the large number of resources provided by project participants, the selection of appropriate classification schemes involves a number of considerations. First of all, there is the semantic difficulty of selecting classification schemes that contain concepts that are relevant to each thematic area. Secondly, EuroGEOSS is intended to accommodate a number of existing environmental projects (for example, GEOSS and INSPIRE). This requirement imposes constraints on the selection. Thirdly, the selected classification scheme or group of schemes (if more than one) must be capable of alignment (establishing different kinds of mappings between concepts, hence preserving intact the original knowledge schemes) or merging (the creation of another unique ontology from the original ontological sources) (Pérez-Gómez et al., 2004). Last but not least, there is the issue of including multi-lingual schemes that are based on free, open standards (non-proprietary). Using these selection criteria, we aim to support open and convenient data discovery and exchange for users who speak different languages (particularly the European ones for the broad scopes of EuroGEOSS). In order to support the project, we have developed a solution that employs two classification schemes: the Societal Benefit Areas (SBAs)3: the upper-level environmental categorization developed for the GEOSS project and the GEneral Multilingual Environmental Thesaurus (GEMET)4: a general environmental thesaurus whose conceptual structure has already been integrated with the spatial data themes proposed by the INSPIRE project. The former seems to provide the spatial data keywords relevant to the INSPIRE's Directive (JRC, 2008). In this way, we provide users with a basic set of concepts to support resource description and discovery in the thematic areas while supporting the requirements of INSPIRE and GEOSS. Furthermore, the use of only two classification schemes together with the fact that the SBAs are very general categories while GEMET includes much more detailed, yet still top-level, concepts, makes alignment an achievable task. Alignment was selected over merging because it leaves the existing classification schemes intact and requires only a simple activity of defining mappings from GEMET to the SBAs. In order to accomplish this task we are developing a simple, automated, open-source application to assist thematic experts in defining the mappings between concepts in the two classification schemes. The application will then generate SKOS mappings (exactMatch, closeMatch, broadMatch, narrowMatch, relatedMatch) based on thematic expert selections between the concepts in GEMET with the SBAs (including both the general Societal Benefit Areas and their subcategories). Once these mappings are defined and the SKOS files generated, resource providers will be able to select concepts from either GEMET or the SBAs (or a mixture) to describe their resources, and discovery approaches will support selection of concepts from either classification scheme, also returning results classified using the other scheme. While the focus of our work has been on the SBAs and GEMET, we also plan to provide a method for resource providers to further extend the semantic infrastructure by defining alignments to new classification schemes if these are required to support particular specialized thematic areas that are not covered by GEMET. In this way, the approach is flexible and suited to the general scope of EuroGEOSS, allowing specialists to increase at will the level of semantic quality and specificity of data to the initial infrastructural skeleton of the project. References ____________________________________________ Joint research Centre (JRC), 2008. INSPIRE Metadata Editor User Guide Pérez-Gómez A., Fernandez-Lopez M., Corcho O. Ontological engineering: With Examples from the Areas of Knowledge Management, e-Commerce and the Semantic Web.Spinger: London, 2004

Probabilistic multiple sclerosis lesion classification based on modeling regional intensity variability and local neighborhood information.

PubMed

Harmouche, Rola; Subbanna, Nagesh K; Collins, D Louis; Arnold, Douglas L; Arbel, Tal

2015-05-01

In this paper, a fully automatic probabilistic method for multiple sclerosis (MS) lesion classification is presented, whereby the posterior probability density function over healthy tissues and two types of lesions (T1-hypointense and T2-hyperintense) is generated at every voxel. During training, the system explicitly models the spatial variability of the intensity distributions throughout the brain by first segmenting it into distinct anatomical regions and then building regional likelihood distributions for each tissue class based on multimodal magnetic resonance image (MRI) intensities. Local class smoothness is ensured by incorporating neighboring voxel information in the prior probability through Markov random fields. The system is tested on two datasets from real multisite clinical trials consisting of multimodal MRIs from a total of 100 patients with MS. Lesion classification results based on the framework are compared with and without the regional information, as well as with other state-of-the-art methods against the labels from expert manual raters. The metrics for comparison include Dice overlap, sensitivity, and positive predictive rates for both voxel and lesion classifications. Statistically significant improvements in Dice values ( ), for voxel-based and lesion-based sensitivity values ( ), and positive predictive rates ( and respectively) are shown when the proposed method is compared to the method without regional information, and to a widely used method [1]. This holds particularly true in the posterior fossa, an area where classification is very challenging. The proposed method allows us to provide clinicians with accurate tissue labels for T1-hypointense and T2-hyperintense lesions, two types of lesions that differ in appearance and clinical ramifications, and with a confidence level in the classification, which helps clinicians assess the classification results.

Performance of the ASAS classification criteria for axial and peripheral spondyloarthritis: a systematic literature review and meta-analysis.

PubMed

Sepriano, Alexandre; Rubio, Roxana; Ramiro, Sofia; Landewé, Robert; van der Heijde, Désirée

2017-05-01

To summarise the evidence on the performance of the Assessment of SpondyloArthritis international Society (ASAS) classification criteria for axial spondyloarthritis (axSpA) (also imaging and clinical arm separately), peripheral (p)SpA and the entire set, when tested against the rheumatologist's diagnosis ('reference standard'). A systematic literature review was performed to identify eligible studies. Raw data on SpA diagnosis and classification were extracted or, if necessary, obtained from the authors of the selected publications. A meta-analysis was performed to obtain pooled estimates for sensitivity, specificity, positive and negative likelihood ratios, by fitting random effects models. Nine papers fulfilled the inclusion criteria (N=5739 patients). The entire set of the ASAS SpA criteria yielded a high pooled sensitivity (73%) and specificity (88%). Similarly, good results were found for the axSpA criteria (sensitivity: 82%; specificity: 88%). Splitting the axSpA criteria in 'imaging arm only' and 'clinical arm only' resulted in much lower sensitivity (30% and 23% respectively), but very high specificity was retained (97% and 94% respectively). The pSpA criteria were less often tested than the axSpA criteria and showed a similarly high pooled specificity (87%) but lower sensitivity (63%). Accumulated evidence from studies with more than 5500 patients confirms the good performance of the various ASAS SpA criteria as tested against the rheumatologist's diagnosis. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

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

Jeffries, H. P.

Distributions of organic constituents in marine communities should yield a worldwide classification scheme within which any localized phenomena would be immediately apparent. This idea was tested on the zooplankton along an environmental gradient extending from Rhode Island Sound, through Narragansett Bay and into its polluted tributary, the Providence River. On the basis of fatty acid composition, both the macro- and microzooplankton could be precisely classified to the habitat of origin. Biochemically the microzooplankton changed uniformly with respect to linear distance though the riverine, estuarine and offshore habitats. In macrozooplankton, the relation between biochemical change and linear distance from the rivermore » seaward was a power curve: sharply changing at first, becoming more nearly constant offshore. Particulate pollution in the river merely reinforced natural fatty acid sources in the zooplankton's food - part of a pattern in which environmentally induced effects were expressed inshore, genetic influences offshore. In each habitat species diversity was inversely related to the community's stability of fatty acid composition. These estimates revealed greatest dynamical robustness in the prolific yet simple riverine zooplankton, suggesting that the stable domain of parameter space was likewise greater here than offshore. Despite its diversity, microzooplankton was more dynamically fragile than the macrozooplankton, in agreement with current theory on the stability of communities. We conclude that monomeric composition offers a basic rationale for characterizing the sensitivities of natural communities to environmental change.« less

Cytotoxicity assays with fish cells as an alternative to the acute lethality test with fish.

PubMed

Segner, Helmut

2004-10-01

In ecotoxicology, in vitro assays with fish cells are currently applied for mechanistic studies, bioanalytical purposes and toxicity screening. This paper discusses the potential of cytotoxicity assays with fish cells to reduce, refine or replace acute lethality tests using fish. Basal cytotoxicity data obtained with fish cell lines or fish primary cell cultures show a reasonable to good correlation with lethality data from acute toxicity tests, with the exception of compounds that exert a specific mode of toxic action. Basal cytotoxicity data from fish cell lines also correlate well with cytotoxicity data from mammalian cell lines. However, both the piscine and mammalian in vitro assays are clearly less sensitive than the fish test. Therefore, in vivo LC50 values (concentrations of the test compounds that are lethal to 50% of the fish in the experiment within 96 hours) currently cannot be predicted from in vitro values. This in vitro-in vivo difference in sensitivity appears to be true for both fish cell lines and mammalian cell lines. Given the good in vitro-in vivo correlation in toxicity ranking, together with the clear-cut difference in sensitivity, the role of cytotoxicity assays in a tiered alternative testing strategy could be in priority setting in relation to toxic hazard and in the toxicity classification of chemicals and environmental samples.

Genetic variability of environmental sensitivity revealed by phenotypic variation in body weight and (its) correlations to physiological and behavioral traits

PubMed Central

Quillet, Edwige; Bégout, Marie-Laure; Aupérin, Benoit; Khaw, Hooi Ling; Millot, Sandie; Valotaire, Claudiane; Kernéis, Thierry; Labbé, Laurent; Prunet, Patrick; Dupont-Nivet, Mathilde

2017-01-01

Adaptive phenotypic plasticity is a key component of the ability of organisms to cope with changing environmental conditions. Fish have been shown to exhibit a substantial level of phenotypic plasticity in response to abiotic and biotic factors. In the present study, we investigate the link between environmental sensitivity assessed globally (revealed by phenotypic variation in body weight) and more targeted physiological and behavioral indicators that are generally used to assess the sensitivity of a fish to environmental stressors. We took advantage of original biological material, the rainbow trout isogenic lines, which allowed the disentangling of the genetic and environmental parts of the phenotypic variance. Ten lines were characterized for the changes of body weight variability (weight measurements taken every month during 18 months), the plasma cortisol response to confinement stress (3 challenges) and a set of selected behavioral indicators. This study unambiguously demonstrated the existence of genetic determinism of environmental sensitivity, with some lines being particularly sensitive to environmental fluctuations and others rather insensitive. Correlations between coefficient of variation (CV) for body weight and behavioral and physiological traits were observed. This confirmed that CV for body weight could be used as an indicator of environmental sensitivity. As the relationship between indicators (CV weight, risk-taking, exploration and cortisol) was shown to be likely depending on the nature and intensity of the stressor, the joint use of several indicators should help to investigate the biological complexity of environmental sensitivity. PMID:29253015

Using ecological zones to increase the detail of Landsat classifications

NASA Technical Reports Server (NTRS)

Fox, L., III; Mayer, K. E.

1981-01-01

Changes in classification detail of forest species descriptions were made for Landsat data on 2.2 million acres in northwestern California. Because basic forest canopy structures may exhibit very similar E-M energy reflectance patterns in different environmental regions, classification labels based on Landsat spectral signatures alone become very generalized when mapping large heterogeneous ecological regions. By adding a seven ecological zone stratification, a 167% improvement in classification detail was made over the results achieved without it. The seven zone stratification is a less costly alternative to the inclusion of complex collateral information, such as terrain data and soil type, into the Landsat data base when making inventories of areas greater than 500,000 acres.

[Difficulties of the methods for studying environmental exposure and neural tube defects].

PubMed

Borja-Aburto, V H; Bermúdez-Castro, O; Lacasaña-Navarro, M; Kuri, P; Bustamante-Montes, P; Torres-Meza, V

1999-01-01

To discuss the attitudes in the assessment of environmental exposures as risk factors associated with neural tube defects, and to present the main risk factors studied to date. Environmental exposures have been suggested to have a roll in the genesis of birth defects. However, studies conducted in human populations have found difficulties in the design and conduction to show such an association for neural tube defects (anencephaly, espina bifida and encephalocele) because of problems raised from: a) the frequency measures used to compare time trends and communities, b) the classification of heterogeneous malformations, c) the inclusion of maternal, paternal and fetal factors as an integrated process and, d) the assessment of environmental exposures. Hypothetically both maternal and paternal environmental exposures can produce damage before and after conception by direct action on the embryo and the fetus-placenta complex. Therefore, in the assessment of environmental exposures we need to take into account: a) both paternal and maternal exposures; b) the critical exposure period, three months before conception for paternal exposures and one month around the conceptional period for maternal exposures; c) quantitatively evaluate environmental exposures when possible, avoiding a dichotomous classification; d) the use of biological markers of exposure is highly recommended as well as markers of genetic susceptibility.

Conjuring a New Category of Disability from Prenatal Cocaine Exposure: Are the Infants Unique Biological or Caretaking Casualties?

ERIC Educational Resources Information Center

Schutter, Linda S.; Brinker, Richard P.

1992-01-01

A review of the literature on biological and environmental effects of cocaine use suggests that the classification of infants and young children as prenatally cocaine exposed is neither descriptive nor predictive of behavior. The classification of behavior rather than labeling of the child is encouraged, as are partnerships with families of…

Thermographic image analysis for classification of ACL rupture disease, bone cancer, and feline hyperthyroid, with Gabor filters

NASA Astrophysics Data System (ADS)

Alvandipour, Mehrdad; Umbaugh, Scott E.; Mishra, Deependra K.; Dahal, Rohini; Lama, Norsang; Marino, Dominic J.; Sackman, Joseph

2017-05-01

Thermography and pattern classification techniques are used to classify three different pathologies in veterinary images. Thermographic images of both normal and diseased animals were provided by the Long Island Veterinary Specialists (LIVS). The three pathologies are ACL rupture disease, bone cancer, and feline hyperthyroid. The diagnosis of these diseases usually involves radiology and laboratory tests while the method that we propose uses thermographic images and image analysis techniques and is intended for use as a prescreening tool. Images in each category of pathologies are first filtered by Gabor filters and then various features are extracted and used for classification into normal and abnormal classes. Gabor filters are linear filters that can be characterized by the two parameters wavelength λ and orientation θ. With two different wavelength and five different orientations, a total of ten different filters were studied. Different combinations of camera views, filters, feature vectors, normalization methods, and classification methods, produce different tests that were examined and the sensitivity, specificity and success rate for each test were produced. Using the Gabor features alone, sensitivity, specificity, and overall success rates of 85% for each of the pathologies was achieved.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

PubMed Central

2013-01-01

Background Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring. PMID:23827014

Relationships (I) of International Classification of High-resolution Computed Tomography for Occupational and Environmental Respiratory Diseases with the ILO International Classification of Radiographs of Pneumoconioses for parenchymal abnormalities.

PubMed

Tamura, Taro; Suganuma, Narufumi; Hering, Kurt G; Vehmas, Tapio; Itoh, Harumi; Akira, Masanori; Takashima, Yoshihiro; Hirano, Harukazu; Kusaka, Yukinori

2015-01-01

The International Classification of High-resolution Computed Tomography (HRCT) for Occupational and Environmental Respiratory Diseases (ICOERD) has been developed for the screening, diagnosis, and epidemiological reporting of respiratory diseases caused by occupational hazards. This study aimed to establish a correlation between readings of HRCT (according to the ICOERD) and those of chest radiography (CXR) pneumoconiotic parenchymal opacities (according to the International Labor Organization Classification/International Classification of Radiographs of Pneumoconioses [ILO/ICRP]). Forty-six patients with and 28 controls without mineral dust exposure underwent posterior-anterior CXR and HRCT. We recorded all subjects' exposure and smoking history. Experts independently read CXRs (using ILO/ICRP). Experts independently assessed HRCT using the ICOERD parenchymal abnormalities grades for well-defined rounded opacities (RO), linear and/or irregular opacities (IR), and emphysema (EM). The correlation between the ICOERD summed grades and ILO/ICRP profusions was evaluated using Spearman's rank-order correlation. Twenty-three patients had small opacities on CXR. HRCT showed that 21 patients had RO; 20 patients, IR opacities; and 23 patients, EM. The correlation between ILO/ICRP profusions and the ICOERD grades was 0.844 for rounded opacities (p<0.01). ICOERD readings from HRCT scans correlated well with previously validated ILO/ICRP criteria. The ICOERD adequately detects pneumoconiotic micronodules and can be used for the interpretation of pneumoconiosis.

Comparison of support vector machine classification to partial least squares dimension reduction with logistic descrimination of hyperspectral data

NASA Astrophysics Data System (ADS)

Wilson, Machelle; Ustin, Susan L.; Rocke, David

2003-03-01

Remote sensing technologies with high spatial and spectral resolution show a great deal of promise in addressing critical environmental monitoring issues, but the ability to analyze and interpret the data lags behind the technology. Robust analytical methods are required before the wealth of data available through remote sensing can be applied to a wide range of environmental problems for which remote detection is the best method. In this study we compare the classification effectiveness of two relatively new techniques on data consisting of leaf-level reflectance from plants that have been exposed to varying levels of heavy metal toxicity. If these methodologies work well on leaf-level data, then there is some hope that they will also work well on data from airborne and space-borne platforms. The classification methods compared were support vector machine classification of exposed and non-exposed plants based on the reflectance data, and partial east squares compression of the reflectance data followed by classification using logistic discrimination (PLS/LD). PLS/LD was performed in two ways. We used the continuous concentration data as the response during compression, and then used the binary response required during logistic discrimination. We also used a binary response during compression followed by logistic discrimination. The statistics we used to compare the effectiveness of the methodologies was the leave-one-out cross validation estimate of the prediction error.

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers

PubMed Central

2014-01-01

Background Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). Methods This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. Results The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. Conclusions A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients. PMID:24903422

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers.

PubMed

Huang, Huifang; Liu, Jie; Zhu, Qiang; Wang, Ruiping; Hu, Guangshu

2014-06-05

Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients.

Evaluation of host and viral factors associated with severe dengue based on the 2009 WHO classification.

PubMed

Pozo-Aguilar, Jorge O; Monroy-Martínez, Verónica; Díaz, Daniel; Barrios-Palacios, Jacqueline; Ramos, Celso; Ulloa-García, Armando; García-Pillado, Janet; Ruiz-Ordaz, Blanca H

2014-12-11

Dengue fever (DF) is the most prevalent arthropod-borne viral disease affecting humans. The World Health Organization (WHO) proposed a revised classification in 2009 to enable the more effective identification of cases of severe dengue (SD). This was designed primarily as a clinical tool, but it also enables cases of SD to be differentiated into three specific subcategories (severe vascular leakage, severe bleeding, and severe organ dysfunction). However, no study has addressed whether this classification has advantage in estimating factors associated with the progression of disease severity or dengue pathogenesis. We evaluate in a dengue outbreak associated risk factors that could contribute to the development of SD according to the 2009 WHO classification. A prospective cross-sectional study was performed during an epidemic of dengue in 2009 in Chiapas, Mexico. Data were analyzed for host and viral factors associated with dengue cases, using the 1997 and 2009 WHO classifications. The cost-benefit ratio (CBR) was also estimated. The sensitivity in the 1997 WHO classification for determining SD was 75%, and the specificity was 97.7%. For the 2009 scheme, these were 100% and 81.1%, respectively. The 2009 classification showed a higher benefit (537%) with a lower cost (10.2%) than the 1997 WHO scheme. A secondary antibody response was strongly associated with SD. Early viral load was higher in cases of SD than in those with DF. Logistic regression analysis identified predictive SD factors (secondary infection, disease phase, viral load) within the 2009 classification. However, within the 1997 scheme it was not possible to differentiate risk factors between DF and dengue hemorrhagic fever or dengue shock syndrome. The critical clinical stage for determining SD progression was the transition from fever to defervescence in which plasma leakage can occur. The clinical phenotype of SD is influenced by the host (secondary response) and viral factors (viral load). The 2009 WHO classification showed greater sensitivity to identify SD in real time. Timely identification of SD enables accurate early decisions, allowing proper management of health resources for the benefit of patients at risk for SD. This is possible based on the 2009 WHO classification.

[Naïve Bayes classification for classifying injury-cause groups from Emergency Room data in the Friuli Venezia Giulia region (Northern Italy)].

PubMed

Valent, Francesca; Clagnan, Elena; Zanier, Loris

2014-01-01

to assess whether Naïve Bayes Classification could be used to classify injury causes from the Emergency Room (ER) database, because in the Friuli Venezia Giulia Region (Northern Italy) the electronic ER data have never been used to study the epidemiology of injuries, because the proportion of generic "accidental" causes is much higher than that of injuries with a specific cause. application of the Naïve Bayes Classification method to the regional ER database. sensitivity, specificity, positive and negative predictive values, agreement, and the kappa statistic were calculated for the train dataset and the distribution of causes of injury for the test dataset. on 22.248 records with known cause, the classifications assigned by the model agreed moderately (kappa =0.53) with those assigned by ER personnel. The model was then used on 76.660 unclassified cases. Although sensitivity and positive predictive value of the method were generally poor, mainly due to limitations in the ER data, it allowed to estimate for the first time the frequency of specific injury causes in the Region. the model was useful to provide the "big picture" of non-fatal injuries in the Region. To improve the collection of injury data at the ER, the options available for injury classification in the ER software are being revised to make categories exhaustive and mutually exclusive.

Faces of poverty: sensitivity and specificity of economic classifications in rural Vietnam.

PubMed

Khe, Nguyen Duy; Eriksson, Bo; Phuong, Do Nguyen; Höjer, Bengt; Diwan, Vinod K

2003-01-01

Poverty concepts and measurements have occupied philosophers for centuries and are subject to debate by researchers. A wide range of possible measures have been developed and used. Most research is country specific and different methods produce different pictures of poverty. This study aimed to compare measures of poverty within an epidemiological field laboratory in Bavi District, northern Vietnam (FilaBavi) and specifically to find out whether the official economic classification made by the local authority matched other measurements of socioeconomic status. Structured questionnaires were used to collect socioeconomic information in 11,547 households. In addition, the official classification for individual households was recorded. Five economic indicators were constructed: income, expenditure, household assets, housing conditions, and local authority's estimation. Official economic classification and housing score were symmetrically distributed, while assets score and particularly income were highly skewed. Design effects were high because of high intra-cluster correlations. No indicator was closely correlated with any other. Sensitivity and positive predictive value for poverty were generally low for all indicators. The authors' findings do not suggest that any of the indicators used is substantially better than the other or better than the Official Economic Classification made by local authority. The results also show that no indicator is particularly useful to predict the values of any other indicator and different poverty indicators may classify different socioeconomic groups as poor.

40 CFR 164.101 - Appeals from or review of initial decisions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... decisions. 164.101 Section 164.101 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... appeal such exceptions to the Environmental Appeals Board for decision by filing them in writing with the...

40 CFR 164.101 - Appeals from or review of initial decisions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... decisions. 164.101 Section 164.101 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... RODENTICIDE ACT, ARISING FROM REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS... appeal such exceptions to the Environmental Appeals Board for decision by filing them in writing with the...

Lauren classification and individualized chemotherapy in gastric cancer.

PubMed

Ma, Junli; Shen, Hong; Kapesa, Linda; Zeng, Shan

2016-05-01

Gastric cancer is one of the most common malignancies worldwide. During the last 50 years, the histological classification of gastric carcinoma has been largely based on Lauren's criteria, in which gastric cancer is classified into two major histological subtypes, namely intestinal type and diffuse type adenocarcinoma. This classification was introduced in 1965, and remains currently widely accepted and employed, since it constitutes a simple and robust classification approach. The two histological subtypes of gastric cancer proposed by the Lauren classification exhibit a number of distinct clinical and molecular characteristics, including histogenesis, cell differentiation, epidemiology, etiology, carcinogenesis, biological behaviors and prognosis. Gastric cancer exhibits varied sensitivity to chemotherapy drugs and significant heterogeneity; therefore, the disease may be a target for individualized therapy. The Lauren classification may provide the basis for individualized treatment for advanced gastric cancer, which is increasingly gaining attention in the scientific field. However, few studies have investigated individualized treatment that is guided by pathological classification. The aim of the current review is to analyze the two major histological subtypes of gastric cancer, as proposed by the Lauren classification, and to discuss the implications of this for personalized chemotherapy.

Feature selection and classification of multiparametric medical images using bagging and SVM

NASA Astrophysics Data System (ADS)

Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

2008-03-01

This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

Paving the way of systems biology and precision medicine in allergic diseases: the MeDALL success story: Mechanisms of the Development of ALLergy; EU FP7-CP-IP; Project No: 261357; 2010-2015.

PubMed

Bousquet, J; Anto, J M; Akdis, M; Auffray, C; Keil, T; Momas, I; Postma, D S; Valenta, R; Wickman, M; Cambon-Thomsen, A; Haahtela, T; Lambrecht, B N; Lodrup Carlsen, K C; Koppelman, G H; Sunyer, J; Zuberbier, T; Annesi-Maesano, I; Arno, A; Bindslev-Jensen, C; De Carlo, G; Forastiere, F; Heinrich, J; Kowalski, M L; Maier, D; Melén, E; Palkonen, S; Smit, H A; Standl, M; Wright, J; Asarnoj, A; Benet, M; Ballardini, N; Garcia-Aymerich, J; Gehring, U; Guerra, S; Hohman, C; Kull, I; Lupinek, C; Pinart, M; Skrindo, I; Westman, M; Smagghe, D; Akdis, C; Albang, R; Anastasova, V; Anderson, N; Bachert, C; Ballereau, S; Ballester, F; Basagana, X; Bedbrook, A; Bergstrom, A; von Berg, A; Brunekreef, B; Burte, E; Carlsen, K H; Chatzi, L; Coquet, J M; Curin, M; Demoly, P; Eller, E; Fantini, M P; Gerhard, B; Hammad, H; von Hertzen, L; Hovland, V; Jacquemin, B; Just, J; Keller, T; Kerkhof, M; Kiss, R; Kogevinas, M; Koletzko, S; Lau, S; Lehmann, I; Lemonnier, N; McEachan, R; Mäkelä, M; Mestres, J; Minina, E; Mowinckel, P; Nadif, R; Nawijn, M; Oddie, S; Pellet, J; Pin, I; Porta, D; Rancière, F; Rial-Sebbag, A; Saeys, Y; Schuijs, M J; Siroux, V; Tischer, C G; Torrent, M; Varraso, R; De Vocht, J; Wenger, K; Wieser, S; Xu, C

2016-11-01

MeDALL (Mechanisms of the Development of ALLergy; EU FP7-CP-IP; Project No: 261357; 2010-2015) has proposed an innovative approach to develop early indicators for the prediction, diagnosis, prevention and targets for therapy. MeDALL has linked epidemiological, clinical and basic research using a stepwise, large-scale and integrative approach: MeDALL data of precisely phenotyped children followed in 14 birth cohorts spread across Europe were combined with systems biology (omics, IgE measurement using microarrays) and environmental data. Multimorbidity in the same child is more common than expected by chance alone, suggesting that these diseases share causal mechanisms irrespective of IgE sensitization. IgE sensitization should be considered differently in monosensitized and polysensitized individuals. Allergic multimorbidities and IgE polysensitization are often associated with the persistence or severity of allergic diseases. Environmental exposures are relevant for the development of allergy-related diseases. To complement the population-based studies in children, MeDALL included mechanistic experimental animal studies and in vitro studies in humans. The integration of multimorbidities and polysensitization has resulted in a new classification framework of allergic diseases that could help to improve the understanding of genetic and epigenetic mechanisms of allergy as well as to better manage allergic diseases. Ethics and gender were considered. MeDALL has deployed translational activities within the EU agenda. © 2016 The Authors. Allergy Published by John Wiley & Sons Ltd.

Sensitivity to Peer Evaluation and Its Genetic and Environmental Determinants: Findings from a Population-Based Twin Study.

PubMed

Klippel, Annelie; Reininghaus, Ulrich; Viechtbauer, Wolfgang; Decoster, Jeroen; Delespaul, Philippe; Derom, Cathérine; de Hert, Marc; Jacobs, Nele; Menne-Lothmann, Claudia; Rutten, Bart; Thiery, Evert; van Os, Jim; van Winkel, Ruud; Myin-Germeys, Inez; Wichers, Marieke

2018-02-23

Adolescents and young adults are highly focused on peer evaluation, but little is known about sources of their differential sensitivity. We examined to what extent sensitivity to peer evaluation is influenced by interacting environmental and genetic factors. A sample of 354 healthy adolescent twin pairs (n = 708) took part in a structured, laboratory task in which they were exposed to peer evaluation. The proportion of the variance in sensitivity to peer evaluation due to genetic and environmental factors was estimated, as was the association with specific a priori environmental risk factors. Differences in sensitivity to peer evaluation between adolescents were explained mainly by non-shared environmental influences. The results on shared environmental influences were not conclusive. No impact of latent genetic factors or gene-environment interactions was found. Adolescents with lower self-rated positions on the social ladder or who reported to have been bullied more severely showed significantly stronger responses to peer evaluation. Not genes, but subjective social status and past experience of being bullied seem to impact sensitivity to peer evaluation. This suggests that altered response to peer evaluation is the outcome of cumulative sensitization to social interactions.

The Short Term Effectiveness of an Outdoor Environmental Education on Environmental Awareness and Sensitivity of In-Service Teachers

ERIC Educational Resources Information Center

Okur-Berberoglu, Emel; Ozdilek, Hasan Göksel; Yalcin-Ozdilek, Sükran

2015-01-01

Outdoor education is mostly mentioned in terms of environmental education. The aim of this research is to determine the short term effectiveness of an outdoor environmental education program on biodiversity awareness, environmental awareness and sensitivity to natural environment. The data is collected from an outdoor environmental education…

Factors That Affect Large Subunit Ribosomal DNA Amplicon Sequencing Studies of Fungal Communities: Classification Method, Primer Choice, and Error

PubMed Central

Porter, Teresita M.; Golding, G. Brian

2012-01-01

Nuclear large subunit ribosomal DNA is widely used in fungal phylogenetics and to an increasing extent also amplicon-based environmental sequencing. The relatively short reads produced by next-generation sequencing, however, makes primer choice and sequence error important variables for obtaining accurate taxonomic classifications. In this simulation study we tested the performance of three classification methods: 1) a similarity-based method (BLAST + Metagenomic Analyzer, MEGAN); 2) a composition-based method (Ribosomal Database Project naïve Bayesian classifier, NBC); and, 3) a phylogeny-based method (Statistical Assignment Package, SAP). We also tested the effects of sequence length, primer choice, and sequence error on classification accuracy and perceived community composition. Using a leave-one-out cross validation approach, results for classifications to the genus rank were as follows: BLAST + MEGAN had the lowest error rate and was particularly robust to sequence error; SAP accuracy was highest when long LSU query sequences were classified; and, NBC runs significantly faster than the other tested methods. All methods performed poorly with the shortest 50–100 bp sequences. Increasing simulated sequence error reduced classification accuracy. Community shifts were detected due to sequence error and primer selection even though there was no change in the underlying community composition. Short read datasets from individual primers, as well as pooled datasets, appear to only approximate the true community composition. We hope this work informs investigators of some of the factors that affect the quality and interpretation of their environmental gene surveys. PMID:22558215

Classification of human coronary atherosclerotic plaques using ex vivo high-resolution multicontrast-weighted MRI compared with histopathology.

PubMed

Li, Tao; Li, Xin; Zhao, Xihai; Zhou, Weihua; Cai, Zulong; Yang, Li; Guo, Aitao; Zhao, Shaohong

2012-05-01

The objective of our study was to evaluate the feasibility of ex vivo high-resolution multicontrast-weighted MRI to accurately classify human coronary atherosclerotic plaques according to the American Heart Association classification. Thirteen human cadaver heart specimens were imaged using high-resolution multicontrast-weighted MR technique (T1-weighted, proton density-weighted, and T2-weighted). All MR images were matched with histopathologic sections according to the landmark of the bifurcation of the left main coronary artery. The sensitivity and specificity of MRI for the classification of plaques were determined, and Cohen's kappa analysis was applied to evaluate the agreement between MRI and histopathology in the classification of atherosclerotic plaques. One hundred eleven MR cross-sectional images obtained perpendicular to the long axis of the proximal left anterior descending artery were successfully matched with the histopathologic sections. For the classification of plaques, the sensitivity and specificity of MRI were as follows: type I-II (near normal), 60% and 100%; type III (focal lipid pool), 80% and 100%; type IV-V (lipid, necrosis, fibrosis), 96.2% and 88.2%; type VI (hemorrhage), 100% and 99.0%; type VII (calcification), 93% and 100%; and type VIII (fibrosis without lipid core), 100% and 99.1%, respectively. Isointensity, which indicates lipid composition on histopathology, was detected on MRI in 48.8% of calcified plaques. Agreement between MRI and histopathology for plaque classification was 0.86 (p < 0.001). Ex vivo high-resolution multicontrast-weighted MRI can accurately classify advanced atherosclerotic plaques in human coronary arteries.

Local Subspace Classifier with Transform-Invariance for Image Classification

NASA Astrophysics Data System (ADS)

Hotta, Seiji

A family of linear subspace classifiers called local subspace classifier (LSC) outperforms the k-nearest neighbor rule (kNN) and conventional subspace classifiers in handwritten digit classification. However, LSC suffers very high sensitivity to image transformations because it uses projection and the Euclidean distances for classification. In this paper, I present a combination of a local subspace classifier (LSC) and a tangent distance (TD) for improving accuracy of handwritten digit recognition. In this classification rule, we can deal with transform-invariance easily because we are able to use tangent vectors for approximation of transformations. However, we cannot use tangent vectors in other type of images such as color images. Hence, kernel LSC (KLSC) is proposed for incorporating transform-invariance into LSC via kernel mapping. The performance of the proposed methods is verified with the experiments on handwritten digit and color image classification.

Computer-aided classification of optical images for diagnosis of osteoarthritis in the finger joints.

PubMed

Zhang, Jiang; Wang, James Z; Yuan, Zhen; Sobel, Eric S; Jiang, Huabei

2011-01-01

This study presents a computer-aided classification method to distinguish osteoarthritis finger joints from healthy ones based on the functional images captured by x-ray guided diffuse optical tomography. Three imaging features, joint space width, optical absorption, and scattering coefficients, are employed to train a Least Squares Support Vector Machine (LS-SVM) classifier for osteoarthritis classification. The 10-fold validation results show that all osteoarthritis joints are clearly identified and all healthy joints are ruled out by the LS-SVM classifier. The best sensitivity, specificity, and overall accuracy of the classification by experienced technicians based on manual calculation of optical properties and visual examination of optical images are only 85%, 93%, and 90%, respectively. Therefore, our LS-SVM based computer-aided classification is a considerably improved method for osteoarthritis diagnosis.

Classification systems for natural resource management

USGS Publications Warehouse

Kleckner, Richard L.

1981-01-01

Resource managers employ various types of resource classification systems in their management activities such as inventory, mapping, and data analysis. Classification is the ordering or arranging of objects into groups or sets on the basis of their relationships, and as such, provide the resource managers with a structure for organizing their needed information. In addition of conforming to certain logical principles, resource classifications should be flexible, widely applicable to a variety of environmental conditions, and useable with minimal training. The process of classification may be approached from the bottom up (aggregation) or the top down (subdivision) or a combination of both, depending on the purpose of the classification. Most resource classification systems in use today focus on a single resource and are used for a single, limited purpose. However, resource managers now must employ the concept of multiple use in their management activities. What they need is an integrated, ecologically based approach to resource classification which would fulfill multiple-use mandates. In an effort to achieve resource-data compatibility and data sharing among Federal agencies, and interagency agreement has been signed by five Federal agencies to coordinate and cooperate in the area of resource classification and inventory.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.

PubMed

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-12-16

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

Gravitational Wave (GW) Classification, Space GW Detection Sensitivities and AMIGO (Astrodynamical Middle-frequency Interferometric GW Observatory)

NASA Astrophysics Data System (ADS)

Ni, Wei-Tou

2018-01-01

After first reviewing the gravitational wave (GW) spectral classification. we discuss the sensitivities of GW detection in space aimed at low frequency band (100 nHz-100 mHz) and middle frequency band (100 mHz-10 Hz). The science goals are to detect GWs from (i) Supermassive Black Holes; (ii) Extreme-Mass-Ratio Black Hole Inspirals; (iii) Intermediate-Mass Black Holes; (iv) Galactic Compact Binaries; (v) Stellar-Size Black Hole Binaries; and (vi) Relic GW Background. The detector proposals have arm length ranging from 100 km to 1.35×109 km (9 AU) including (a) Solar orbiting detectors and (b) Earth orbiting detectors. We discuss especially the sensitivities in the frequency band 0.1-10 μHz and the middle frequency band (0.1 Hz-10 Hz). We propose and discuss AMIGO as an Astrodynamical Middlefrequency Interferometric GW Observatory.

Sensitivity of predicted bioaerosol exposure from open windrow composting facilities to ADMS dispersion model parameters.

PubMed

Douglas, P; Tyrrel, S F; Kinnersley, R P; Whelan, M; Longhurst, P J; Walsh, K; Pollard, S J T; Drew, G H

2016-12-15

Bioaerosols are released in elevated quantities from composting facilities and are associated with negative health effects, although dose-response relationships are not well understood, and require improved exposure classification. Dispersion modelling has great potential to improve exposure classification, but has not yet been extensively used or validated in this context. We present a sensitivity analysis of the ADMS dispersion model specific to input parameter ranges relevant to bioaerosol emissions from open windrow composting. This analysis provides an aid for model calibration by prioritising parameter adjustment and targeting independent parameter estimation. Results showed that predicted exposure was most sensitive to the wet and dry deposition modules and the majority of parameters relating to emission source characteristics, including pollutant emission velocity, source geometry and source height. This research improves understanding of the accuracy of model input data required to provide more reliable exposure predictions. Copyright © 2016. Published by Elsevier Ltd.

Parallel k-Means Clustering for Quantitative Ecoregion Delineation Using Large Data Sets

Treesearch

Jitendra Kumar; Richard T. Mills; Forrest M Hoffman; William W Hargrove

2011-01-01

Identification of geographic ecoregions has long been of interest to environmental scientists and ecologists for identifying regions of similar ecological and environmental conditions. Such classifications are important for predicting suitable species ranges, for stratification of ecological samples, and to help prioritize habitat preservation and remediation efforts....

Classification and Dose-Response Characterization of Environmental Chemicals Based On Structured Toxicity Information From ToxRefDB

EPA Science Inventory

Thirty years and over a billion of today’s dollars worth of pesticide registration toxicity studies, historically stored as hardcopy and scanned documents, have been digitized into highly standardized and structured toxicity data, within the U.S. Environmental Protection Agency’s...

40 CFR 164.110 - Motion for reopening hearings; for rehearing; for reargument of any proceeding; or for...

Code of Federal Regulations, 2010 CFR

2010-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF PRACTICE... REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF REGISTRATIONS AND... reconsideration of the order, must be made by motion to the Environmental Appeals Board filed with the hearing...

40 CFR 164.4 - Arrangements for examining Agency records, transcripts, orders, and decisions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... records, transcripts, orders, and decisions. 164.4 Section 164.4 Protection of Environment ENVIRONMENTAL..., CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF REGISTRATIONS AND OTHER HEARINGS CALLED PURSUANT TO SECTION 6... signed documents required by the rules in this part, whether issued by the Environmental Appeals Board or...

40 CFR 164.110 - Motion for reopening hearings; for rehearing; for reargument of any proceeding; or for...

Code of Federal Regulations, 2011 CFR

2011-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF PRACTICE... REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF REGISTRATIONS AND... reconsideration of the order, must be made by motion to the Environmental Appeals Board filed with the hearing...

40 CFR 164.4 - Arrangements for examining Agency records, transcripts, orders, and decisions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... records, transcripts, orders, and decisions. 164.4 Section 164.4 Protection of Environment ENVIRONMENTAL..., CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF REGISTRATIONS AND OTHER HEARINGS CALLED PURSUANT TO SECTION 6... signed documents required by the rules in this part, whether issued by the Environmental Appeals Board or...

COMPARING STRENGTHS OF GEOGRAPHIC AND NONGEOGRAPHIC CLASSIFICATIONS OF STREAM BENTHIC MACROINVERTEBRATES IN THE MID-ATLANTIC HIGHLANDS, USA

EPA Science Inventory

The US Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program (EMAP) sampled - 500 wadeable streams in the Mid-Atlantic Highlands region of the US during the late spring of 1993 to 1995 for a variety of physical, chemical, and biological indicat...

Sensitivity and accuracy of high-throughput metabarcoding methods used to describe aquatic communities for early detection of invasve fish species

EPA Science Inventory

For early detection biomonitoring of aquatic invasive species, sensitivity to rare individuals and accurate, high-resolution taxonomic classification are critical to minimize Type I and II detection errors. Given the great expense and effort associated with morphological identifi...

Discrimination-Aware Classifiers for Student Performance Prediction

ERIC Educational Resources Information Center

Luo, Ling; Koprinska, Irena; Liu, Wei

2015-01-01

In this paper we consider discrimination-aware classification of educational data. Mining and using rules that distinguish groups of students based on sensitive attributes such as gender and nationality may lead to discrimination. It is desirable to keep the sensitive attributes during the training of a classifier to avoid information loss but…

Centrifuge: rapid and sensitive classification of metagenomic sequences.

PubMed

Kim, Daehwan; Song, Li; Breitwieser, Florian P; Salzberg, Steven L

2016-12-01

Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space. © 2016 Kim et al.; Published by Cold Spring Harbor Laboratory Press.

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

NASA Astrophysics Data System (ADS)

Janaki Sathya, D.; Geetha, K.

2017-12-01

Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.

Automated detection of neovascularization for proliferative diabetic retinopathy screening.

PubMed

Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

2016-08-01

Neovascularization is the primary manifestation of proliferative diabetic retinopathy (PDR) that can lead to acquired blindness. This paper presents a novel method that classifies neovascularizations in the 1-optic disc (OD) diameter region (NVD) and elsewhere (NVE) separately to achieve low false positive rates of neovascularization classification. First, the OD region and blood vessels are extracted. Next, the major blood vessel segments in the 1-OD diameter region are classified for NVD, and minor blood vessel segments elsewhere are classified for NVE. For NVD and NVE classifications, optimal region-based feature sets of 10 and 6 features, respectively, are used. The proposed method achieves classification sensitivity, specificity and accuracy for NVD and NVE of 74%, 98.2%, 87.6%, and 61%, 97.5%, 92.1%, respectively. Also, the proposed method achieves 86.4% sensitivity and 76% specificity for screening images with PDR from public and local data sets. Thus, the proposed NVD and NVE detection methods can play a key role in automated screening and prioritization of patients with diabetic retinopathy.

Diagnosis of mild chronic pancreatitis (Cambridge classification): comparative study using secretin injection-magnetic resonance cholangiopancreatography and endoscopic retrograde pancreatography.

PubMed

Sai, Jin-Kan; Suyama, Masafumi; Kubokawa, Yoshihiro; Watanabe, Sumio

2008-02-28

To investigate the usefulness of secretin injection-MRCP for the diagnosis of mild chronic pancreatitis. Sixteen patients having mild chronic pancreatitis according to the Cambridge classification and 12 control subjects with no abnormal findings on the pancreatogram were examined for the diagnostic accuracy of secretin injection-MRCP regarding abnormal branch pancreatic ducts associated with mild chronic pancreatitis (Cambridge Classification), using endoscopic retrograde cholangiopancreatography (ERCP) for comparison. The sensitivity and specificity for abnormal branch pancreatic ducts determined by two reviewers were respectively 55%-63% and 75%-83% in the head, 57%-64% and 82%-83% in the body, and 44%-44% and 72%-76% in the tail of the pancreas. The sensitivity and specificity for mild chronic pancreatitis were 56%-63% and 92%-92%, respectively. Interobserver agreement (kappa statistics) concerning the diagnosis of an abnormal branch pancreatic duct and of mild chronic pancreatitis was good to excellent. Secretin injection-MRCP might be useful for the diagnosis of mild chronic pancreatitis.

Integration of environmental simulation models with satellite remote sensing and geographic information systems technologies: case studies

USGS Publications Warehouse

Steyaert, Louis T.; Loveland, Thomas R.; Brown, Jesslyn F.; Reed, Bradley C.

1993-01-01

Environmental modelers are testing and evaluating a prototype land cover characteristics database for the conterminous United States developed by the EROS Data Center of the U.S. Geological Survey and the University of Nebraska Center for Advanced Land Management Information Technologies. This database was developed from multi temporal, 1-kilometer advanced very high resolution radiometer (AVHRR) data for 1990 and various ancillary data sets such as elevation, ecological regions, and selected climatic normals. Several case studies using this database were analyzed to illustrate the integration of satellite remote sensing and geographic information systems technologies with land-atmosphere interactions models at a variety of spatial and temporal scales. The case studies are representative of contemporary environmental simulation modeling at local to regional levels in global change research, land and water resource management, and environmental simulation modeling at local to regional levels in global change research, land and water resource management and environmental risk assessment. The case studies feature land surface parameterizations for atmospheric mesoscale and global climate models; biogenic-hydrocarbons emissions models; distributed parameter watershed and other hydrological models; and various ecological models such as ecosystem, dynamics, biogeochemical cycles, ecotone variability, and equilibrium vegetation models. The case studies demonstrate the important of multi temporal AVHRR data to develop to develop and maintain a flexible, near-realtime land cover characteristics database. Moreover, such a flexible database is needed to derive various vegetation classification schemes, to aggregate data for nested models, to develop remote sensing algorithms, and to provide data on dynamic landscape characteristics. The case studies illustrate how such a database supports research on spatial heterogeneity, land use, sensitivity analysis, and scaling issues involving regional extrapolations and parameterizations of dynamic land processes within simulation models.

Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods

NASA Astrophysics Data System (ADS)

Vanrolleghem, Peter A.; Mannina, Giorgio; Cosenza, Alida; Neumann, Marc B.

2015-03-01

Sensitivity analysis represents an important step in improving the understanding and use of environmental models. Indeed, by means of global sensitivity analysis (GSA), modellers may identify both important (factor prioritisation) and non-influential (factor fixing) model factors. No general rule has yet been defined for verifying the convergence of the GSA methods. In order to fill this gap this paper presents a convergence analysis of three widely used GSA methods (SRC, Extended FAST and Morris screening) for an urban drainage stormwater quality-quantity model. After the convergence was achieved the results of each method were compared. In particular, a discussion on peculiarities, applicability, and reliability of the three methods is presented. Moreover, a graphical Venn diagram based classification scheme and a precise terminology for better identifying important, interacting and non-influential factors for each method is proposed. In terms of convergence, it was shown that sensitivity indices related to factors of the quantity model achieve convergence faster. Results for the Morris screening method deviated considerably from the other methods. Factors related to the quality model require a much higher number of simulations than the number suggested in literature for achieving convergence with this method. In fact, the results have shown that the term "screening" is improperly used as the method may exclude important factors from further analysis. Moreover, for the presented application the convergence analysis shows more stable sensitivity coefficients for the Extended-FAST method compared to SRC and Morris screening. Substantial agreement in terms of factor fixing was found between the Morris screening and Extended FAST methods. In general, the water quality related factors exhibited more important interactions than factors related to water quantity. Furthermore, in contrast to water quantity model outputs, water quality model outputs were found to be characterised by high non-linearity.

Comparison of staging diagnosis by two magnifying endoscopy classification for superficial oesophageal cancer.

PubMed

Ebi, Masahide; Shimura, Takaya; Murakami, Kenji; Yamada, Tomonori; Hirata, Yoshikazu; Tsukamoto, Hironobu; Mizoshita, Tsutomu; Tanida, Satoshi; Kataoka, Hiromi; Kamiya, Takeshi; Joh, Takashi

2012-11-01

Due to the possibility of lymph node metastasis, surgical resection is indicated for superficial oesophageal cancer with invasion to a depth greater than the muscularis mucosa. Although two magnifying endoscopy classifications are currently used to diagnose the depth of invasion, which classification is more suitable remains controversial. To compare and evaluate the clinical outcomes of two classifications for superficial oesophageal squamous cell carcinoma. This cross-sectional study consists of 44 superficial oesophageal squamous cell carcinoma lesions with magnification image-enhanced endoscopy images. Only magnifying endoscopic images were displayed to two experienced endoscopists who independently diagnosed the depth of invasion according to both classifications. The sensitivity of invasion greater than the muscularis mucosa tended to be higher in Inoue's classification than Arima's classification (78.3±6.2% vs. 50.0±3.0%; P=0.144), whereas the specificity was significantly lower in Inoue's classification than in Arima's classification (61.9±0.0% vs. 97.6±3.4%; P=0.043). For both classifications, rates of concordance were 90.9% and 84.4%, and κ statistics were 0.81 and 0.66, respectively. Our results suggest that Arima's classification is suitable for general screening before treatment to avoid unnecessary surgery. Inoue's classification is appropriate for assessing wide lesion. Copyright © 2012 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Recipe creation for automated defect classification with a 450mm surface scanning inspection system based on the bidirectional reflectance distribution function of native defects

NASA Astrophysics Data System (ADS)

Yathapu, Nithin; McGarvey, Steve; Brown, Justin; Zhivotovsky, Alexander

2016-03-01

This study explores the feasibility of Automated Defect Classification (ADC) with a Surface Scanning Inspection System (SSIS). The defect classification was based upon scattering sensitivity sizing curves created via modeling of the Bidirectional Reflectance Distribution Function (BRDF). The BRDF allowed for the creation of SSIS sensitivity/sizing curves based upon the optical properties of both the filmed wafer samples and the optical architecture of the SSIS. The elimination of Polystyrene Latex Sphere (PSL) and Silica deposition on both filmed and bare Silicon wafers prior to SSIS recipe creation and ADC creates a challenge for light scattering surface intensity based defect binning. This study explored the theoretical maximal SSIS sensitivity based on native defect recipe creation in conjunction with the maximal sensitivity derived from BRDF modeling recipe creation. Single film and film stack wafers were inspected with recipes based upon BRDF modeling. Following SSIS recipe creation, initially targeting maximal sensitivity, selected recipes were optimized to classify defects commonly found on non-patterned wafers. The results were utilized to determine the ADC binning accuracy of the native defects and evaluate the SSIS recipe creation methodology. A statistically valid sample of defects from the final inspection results of each SSIS recipe and filmed substrate were reviewed post SSIS ADC processing on a Defect Review Scanning Electron Microscope (SEM). Native defect images were collected from each statistically valid defect bin category/size for SEM Review. The data collected from the Defect Review SEM was utilized to determine the statistical purity and accuracy of each SSIS defect classification bin. This paper explores both, commercial and technical, considerations of the elimination of PSL and Silica deposition as a precursor to SSIS recipe creation targeted towards ADC. Successful integration of SSIS ADC in conjunction with recipes created via BRDF modeling has the potential to dramatically reduce the workload requirements of a Defect Review SEM and save a significant amount of capital expenditure for 450mm SSIS recipe creation.

Applying geologic sensitivity analysis to environmental risk management: The financial implications

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

Rogers, D.T.

The financial risks associated with environmental contamination can be staggering and are often difficult to identify and accurately assess. Geologic sensitivity analysis is gaining recognition as a significant and useful tool that can empower the user with crucial information concerning environmental risk management and brownfield redevelopment. It is particularly useful when (1) evaluating the potential risks associated with redevelopment of historical industrial facilities (brownfields) and (2) planning for future development, especially in areas of rapid development because the number of potential contaminating sources often increases with an increase in economic development. An examination of the financial implications relating to geologicmore » sensitivity analysis in southeastern Michigan from numerous case studies indicate that the environmental cost of contamination may be 100 to 1,000 times greater at a geologically sensitive location compared to the least sensitive location. Geologic sensitivity analysis has demonstrated that near-surface geology may influence the environmental impact of a contaminated site to a greater extent than the amount and type of industrial development.« less

A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

NASA Astrophysics Data System (ADS)

Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

2015-08-01

Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

Weight-elimination neural networks applied to coronary surgery mortality prediction.

PubMed

Ennett, Colleen M; Frize, Monique

2003-06-01

The objective was to assess the effectiveness of the weight-elimination cost function in improving classification performance of artificial neural networks (ANNs) and to observe how changing the a priori distribution of the training set affects network performance. Backpropagation feedforward ANNs with and without weight-elimination estimated mortality for coronary artery surgery patients. The ANNs were trained and tested on cases with 32 input variables describing the patient's medical history; the output variable was in-hospital mortality (mortality rates: training 3.7%, test 3.8%). Artificial training sets with mortality rates of 20%, 50%, and 80% were created to observe the impact of training with a higher-than-normal prevalence. When the results were averaged, weight-elimination networks achieved higher sensitivity rates than those without weight-elimination. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates at the cost of lower specificity and correct classification. The weight-elimination cost function can improve the classification performance when the network is trained with a higher-than-normal prevalence. A network trained with a moderately high artificial mortality rate (artificial mortality rate of 20%) can improve the sensitivity of the model without significantly affecting other aspects of the model's performance. The ANN mortality model achieved comparable performance as additive and statistical models for coronary surgery mortality estimation in the literature.

Infant homicide and accidental death in the United States, 1940-2005: ethics and epidemiological classification.

PubMed

Riggs, Jack E; Hobbs, Gerald R

2011-07-01

Potential ethical issues can arise during the process of epidemiological classification. For example, unnatural infant deaths are classified as accidental deaths or homicides. Societal sensitivity to the physical abuse and neglect of children has increased over recent decades. This enhanced sensitivity could impact reported infant homicide rates. Infant homicide and accident mortality rates in boys and girls in the USA from 1940 to 2005 were analysed. In 1940, infant accident mortality rates were over 20 times greater than infant homicide rates in both boys and girls. After about 1980, when the ratio of infant accident mortality rates to infant homicide rates decreased to less than five, and the sum of infant accident and homicide rates became relatively constant, further decreases in infant accident mortality rates were associated with increases in reported infant homicide rates. These findings suggest that the dramatic decline of accidental infant mortality and recent increased societal sensitivity to child abuse may be related to the increased infant homicide rates observed in the USA since 1980 rather than an actual increase in societal violence directed against infants. Ethical consequences of epidemiological classification, involving the principles of beneficence, non-maleficence and justice, are suggested by observed patterns in infant accidental deaths and homicides in the USA from 1940 to 2005.

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

NASA Technical Reports Server (NTRS)

Zevin, M.; Coughlin, S.; Bahaadini, S.; Besler, E.; Rohani, N.; Allen, S.; Cabero, M.; Crowston, K.; Katsaggelos, A. K.; Littenberg, T. B.

2017-01-01

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science.

PubMed

Zevin, M; Coughlin, S; Bahaadini, S; Besler, E; Rohani, N; Allen, S; Cabero, M; Crowston, K; Katsaggelos, A K; Larson, S L; Lee, T K; Lintott, C; Littenberg, T B; Lundgren, A; Østerlund, C; Smith, J R; Trouille, L; Kalogera, V

2017-01-01

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches , which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

NASA Astrophysics Data System (ADS)

Zevin, M.; Coughlin, S.; Bahaadini, S.; Besler, E.; Rohani, N.; Allen, S.; Cabero, M.; Crowston, K.; Katsaggelos, A. K.; Larson, S. L.; Lee, T. K.; Lintott, C.; Littenberg, T. B.; Lundgren, A.; Østerlund, C.; Smith, J. R.; Trouille, L.; Kalogera, V.

2017-03-01

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO’s first observing run.

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

PubMed Central

Zevin, M; Coughlin, S; Bahaadini, S; Besler, E; Rohani, N; Allen, S; Cabero, M; Crowston, K; Katsaggelos, A K; Larson, S L; Lee, T K; Lintott, C; Littenberg, T B; Lundgren, A; Østerlund, C; Smith, J R; Trouille, L; Kalogera, V

2018-01-01

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO’s first observing run. PMID:29722360

Prediction of customer behaviour analysis using classification algorithms

NASA Astrophysics Data System (ADS)

Raju, Siva Subramanian; Dhandayudam, Prabha

2018-04-01

Customer Relationship management plays a crucial role in analyzing of customer behavior patterns and their values with an enterprise. Analyzing of customer data can be efficient performed using various data mining techniques, with the goal of developing business strategies and to enhance the business. In this paper, three classification models (NB, J48, and MLPNN) are studied and evaluated for our experimental purpose. The performance measures of the three classifications are compared using three different parameters (accuracy, sensitivity, specificity) and experimental results expose J48 algorithm has better accuracy with compare to NB and MLPNN algorithm.

Relationship between urban eco-environment and competitiveness with the background of globalization: statistical explanation based on industry type newly classified with environment demand and environment pressure.

PubMed

Kang, Xiao-guang; Ma, Qing-Bin

2005-01-01

Within the global urban system, the statistical relationship between urban eco-environment (UE) and urban competitiveness (UC) (RUEC) is researched. Data showed that there is a statistically inverted-U relationship between UE and UC. Eco-environmental factor is put into the classification of industries, and gets six industrial types by two indexes viz. industries' eco-environmental demand and pressure. The statistical results showed that there is a strong relationship, for new industrial classification, between the changes of industrial structure and evolvement of UE. The drive mechanism of the evolvement of urban eco-environment, with human demand and global work division was analyzed. The conclusion is that the development stratege, industrial policies of cities, and environmental policies fo cities must be fit with their ranks among the global urban system. At the era of globalization, so far as the environmental policies, their rationality could not be assessed with the level of strictness, but it can enhance cities' competitiveness when they are fit with cities' capabilities to attract and control some sections of the industry's value-chain. None but these kinds of environmental policies can probably enhance the UC.

Survey Definitions of Gout for Epidemiologic Studies: Comparison With Crystal Identification as the Gold Standard.

PubMed

Dalbeth, Nicola; Schumacher, H Ralph; Fransen, Jaap; Neogi, Tuhina; Jansen, Tim L; Brown, Melanie; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Scire, Carlo A; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; da Rocha Castelar-Pinheiro, Geraldo; Janssen, Matthijs; Chen, Jiunn-Horng; Cimmino, Marco A; Uhlig, Till; Taylor, William J

2016-12-01

To identify the best-performing survey definition of gout from items commonly available in epidemiologic studies. Survey definitions of gout were identified from 34 epidemiologic studies contributing to the Global Urate Genetics Consortium (GUGC) genome-wide association study. Data from the Study for Updated Gout Classification Criteria (SUGAR) were randomly divided into development and test data sets. A data-driven case definition was formed using logistic regression in the development data set. This definition, along with definitions used in GUGC studies and the 2015 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) gout classification criteria were applied to the test data set, using monosodium urate crystal identification as the gold standard. For all tested GUGC definitions, the simple definition of "self-report of gout or urate-lowering therapy use" had the best test performance characteristics (sensitivity 82%, specificity 72%). The simple definition had similar performance to a SUGAR data-driven case definition with 5 weighted items: self-report, self-report of doctor diagnosis, colchicine use, urate-lowering therapy use, and hyperuricemia (sensitivity 87%, specificity 70%). Both of these definitions performed better than the 1977 American Rheumatism Association survey criteria (sensitivity 82%, specificity 67%). Of all tested definitions, the 2015 ACR/EULAR criteria had the best performance (sensitivity 92%, specificity 89%). A simple definition of "self-report of gout or urate-lowering therapy use" has the best test performance characteristics of existing definitions that use routinely available data. A more complex combination of features is more sensitive, but still lacks good specificity. If a more accurate case definition is required for a particular study, the 2015 ACR/EULAR gout classification criteria should be considered. © 2016, American College of Rheumatology.

Validation of Case Finding Algorithms for Hepatocellular Cancer from Administrative Data and Electronic Health Records using Natural Language Processing

PubMed Central

Sada, Yvonne; Hou, Jason; Richardson, Peter; El-Serag, Hashem; Davila, Jessica

2013-01-01

Background Accurate identification of hepatocellular cancer (HCC) cases from automated data is needed for efficient and valid quality improvement initiatives and research. We validated HCC ICD-9 codes, and evaluated whether natural language processing (NLP) by the Automated Retrieval Console (ARC) for document classification improves HCC identification. Methods We identified a cohort of patients with ICD-9 codes for HCC during 2005–2010 from Veterans Affairs administrative data. Pathology and radiology reports were reviewed to confirm HCC. The positive predictive value (PPV), sensitivity, and specificity of ICD-9 codes were calculated. A split validation study of pathology and radiology reports was performed to develop and validate ARC algorithms. Reports were manually classified as diagnostic of HCC or not. ARC generated document classification algorithms using the Clinical Text Analysis and Knowledge Extraction System. ARC performance was compared to manual classification. PPV, sensitivity, and specificity of ARC were calculated. Results 1138 patients with HCC were identified by ICD-9 codes. Based on manual review, 773 had HCC. The HCC ICD-9 code algorithm had a PPV of 0.67, sensitivity of 0.95, and specificity of 0.93. For a random subset of 619 patients, we identified 471 pathology reports for 323 patients and 943 radiology reports for 557 patients. The pathology ARC algorithm had PPV of 0.96, sensitivity of 0.96, and specificity of 0.97. The radiology ARC algorithm had PPV of 0.75, sensitivity of 0.94, and specificity of 0.68. Conclusion A combined approach of ICD-9 codes and NLP of pathology and radiology reports improves HCC case identification in automated data. PMID:23929403

A prospective study to validate various clinical criteria used in classification of leprosy: a study from a tertiary care center in India.

PubMed

Thapa, Manisha; Sendhil Kumaran, Muthu; Narang, Tarun; Saikia, Uma N; Sawatkar, Gitesh U; Dogra, Sunil

2018-05-29

Various clinical criteria are used to categorize leprosy patients into paucibacillary (PB) and multibacillary (MB), thus aiding in appropriate treatment. However, comprehensive studies validating these criteria are minimal. To assess sensitivity and specificity of different clinical criteria individually and in combination for classifying leprosy into PB/MB spectrum. A prospective study was conducted wherein 50 newly diagnosed, untreated leprosy cases were recruited and classified into PB and MB using the following clinical criteria: number of skin lesions (NSL), number of body areas affected (NBAA), and size of largest skin lesion (SLSL). Patients with pure neuritic leprosy, diffuse macular type of lepromatous leprosy, and with reactions were excluded. Sensitivity and specificity of these clinical criteria in classification was calculated taking histopathological findings as gold standard. Among 50 patients, 37 were males and 13 were females with a mean age of 32.08 ± 16.55 years. The sensitivity and specificity of NSL, NBAA, and SLSL was 94.74 and 87.1%, 94.74 and 61.29%, and 73.68 and 16.13%, respectively. Combining all three criteria, the sensitivity increased to 100%, but specificity decreased drastically to 12.9%. The ROC curve for NSL, NBAA, and SLSL showed a cutoff of ≥6 skin lesions, ≥3 body areas affected, and ≤2 cm lesion to classify as MB. The current WHO system of leprosy classification based on NSL seems to be best among available clinical criteria. Uniform and sensible application of this criteria itself assures appropriate categorizing and leprosy treatment with reasonable sensitivity and specificity. © 2018 The International Society of Dermatology.

Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Zhang, Daoqiang; Wang, Yaping; Zhou, Luping; Yuan, Hong; Shen, Dinggang

2011-01-01

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attentions recently. So far, multiple biomarkers have been shown sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51 AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers. PMID:21236349

A novel method and software for automatically classifying Alzheimer's disease patients by magnetic resonance imaging analysis.

PubMed

Previtali, F; Bertolazzi, P; Felici, G; Weitschek, E

2017-05-01

The cause of the Alzheimer's disease is poorly understood and to date no treatment to stop or reverse its progression has been discovered. In developed countries, the Alzheimer's disease is one of the most financially costly diseases due to the requirement of continuous treatments as well as the need of assistance or supervision with the most cognitively demanding activities as time goes by. The objective of this work is to present an automated approach for classifying the Alzheimer's disease from magnetic resonance imaging (MRI) patient brain scans. The method is fast and reliable for a suitable and straightforward deploy in clinical applications for helping diagnosing and improving the efficacy of medical treatments by recognising the disease state of the patient. Many features can be extracted from magnetic resonance images, but most are not suitable for the classification task. Therefore, we propose a new feature extraction technique from patients' MRI brain scans that is based on a recent computer vision method, called Oriented FAST and Rotated BRIEF. The extracted features are processed with the definition and the combination of two new metrics, i.e., their spatial position and their distribution around the patient's brain, and given as input to a function-based classifier (i.e., Support Vector Machines). We report the comparison with recent state-of-the-art approaches on two established medical data sets (ADNI and OASIS). In the case of binary classification (case vs control), our proposed approach outperforms most state-of-the-art techniques, while having comparable results with the others. Specifically, we obtain 100% (97%) of accuracy, 100% (97%) sensitivity and 99% (93%) specificity for the ADNI (OASIS) data set. When dealing with three or four classes (i.e., classification of all subjects) our method is the only one that reaches remarkable performance in terms of classification accuracy, sensitivity and specificity, outperforming the state-of-the-art approaches. In particular, in the ADNI data set we obtain a classification accuracy, sensitivity and specificity of 99% while in the OASIS data set a classification accuracy and sensitivity of 77% and specificity of 79% when dealing with four classes. By providing a quantitative comparison on the two established data sets with many state-of-the-art techniques, we demonstrated the effectiveness of our proposed approach in classifying the Alzheimer's disease from MRI patient brain scans. Copyright © 2017 Elsevier B.V. All rights reserved.

The NASA John C. Stennis Environmental Geographic Information System

NASA Technical Reports Server (NTRS)

Cohan, Tyrus

2002-01-01

Contents include the following: 1. Introduction: Background information. Initial applications of the SSC EGIS. Ongoing projects. 2.Scope of SSC EGIS. 3. Data layers. 4. Onsite operations. 5. Landcover classifications. 6. Current activities. 7. GIS/Key. 8. Infrastructure base map - development. 9. Infrastructure base map - application. 10. Incorrected layer. 11. Corrected layer. 12. Emergency environmental response tool. 13. Future directions. 14. Bridging the gaps. 15. Environmental geographical information system.

A new multidimensional stoichiometric classification of compounds: moving beyond the van Krevelen diagram.

NASA Astrophysics Data System (ADS)

Rivas-Ubach, A.; Liu, Y.; Bianchi, T. S.; Tolic, N.; Jansson, C.; Paša-Tolić, L.

2017-12-01

The role of nutrients in organisms, especially primary producers, has been a topic of special interest in ecosystem research for understanding the ecosystem structure and function. The majority of macro-elements in organisms, such as C, H, O, N and P, do not act as single elements but are components of organic compounds (lipids, peptides, carbohydrates, etc), which are more directly related to the physiology of organisms and thus to the ecosystem function. However, accurately deciphering the overall content of the main compound classes (lipids, proteins, carbohydrates,…) in organisms is still a major challenge. van Krevelen (vK) diagrams have been widely used as an estimation of the main compound categories present in environmental samples based on O:C vs H:C molecular ratios, but a stoichiometric classification based exclusively on O:C and H:C ratios is feeble. Different compound classes show large O:C and H:C ratio overlapping and other heteroatoms, such as N and P, should be considered to robustly distinguish the different classes. We propose a new compound classification for biological/environmental samples based on the C:H:O:N:P stoichiometric ratios of thousands of molecular formulas of characterized compounds from 6 different main categories: lipids, peptides, amino-sugars, carbohydrates, nucleotides and phytochemical compounds (oxy-aromatic compounds). This new multidimensional stoichiometric compound constraints classification (MSCC) can be applied to data obtained with high resolution mass spectrometry (HRMS), allowing an accurate overview of the relative abundances of the main compound categories present in organismal samples. The MSCC has been optimized for plants, but it could be also applied to different organisms and serve as a strong starting point to further investigate other environmental complex matrices (soils, aerosols, etc). The proposed MSCC advances environmental research, especially eco-metabolomics, ecophysiology and ecological stoichiometry studies, providing a new tool to understand the ecosystem structure and function at the molecular level.

Nanofluidic Pre-Concentration Devices for Enhancing the Detection Sensitivity and Selectivity of Biomarkers for Human Performance Monitoring

DTIC Science & Technology

2016-10-17

AFRL-AFOSR-JP-TR-2016-0082 Nanofluidic Pre -Concentration Devices for Enhancing the Detection Sensitivity and Selectivity of Biomarkers for Human...Nanofluidic Pre -Concentration Devices for Enhancing the Detection Sensitivity and Selectivity of Biomarkers for Human Performance Monitoring 5a...SUBJECT TERMS Biomarkers, Nanofluidics, Pre -concentration Devices, Sensing, AOARD 16.  SECURITY CLASSIFICATION OF: 17.  LIMITATION OF ABSTRACT SAR 18

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment.

PubMed

Lemaire, Edward D; Tundo, Marco D; Baddour, Natalie

2015-12-11

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated. The mobile versus immobile classification set had a sensitivity of 86.30% ± 7.2% and specificity of 98.96% ± 0.6%, while the second prediction set had a sensitivity of 88.35% ± 7.80% and specificity of 98.51% ± 0.62%. For the third classification set, sensitivity was 84.92% ± 6.38% and specificity was 98.17 ± 0.62. F1 scores for the first, second and third classification sets were 86.17 ± 6.3, 80.19 ± 6.36, and 78.42 ± 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

Ground truth management system to support multispectral scanner /MSS/ digital analysis

NASA Technical Reports Server (NTRS)

Coiner, J. C.; Ungar, S. G.

1977-01-01

A computerized geographic information system for management of ground truth has been designed and implemented to relate MSS classification results to in situ observations. The ground truth system transforms, generalizes and rectifies ground observations to conform to the pixel size and shape of high resolution MSS aircraft data. These observations can then be aggregated for comparison to lower resolution sensor data. Construction of a digital ground truth array allows direct pixel by pixel comparison between classification results of MSS data and ground truth. By making comparisons, analysts can identify spatial distribution of error within the MSS data as well as usual figures of merit for the classifications. Use of the ground truth system permits investigators to compare a variety of environmental or anthropogenic data, such as soil color or tillage patterns, with classification results and allows direct inclusion of such data into classification operations. To illustrate the system, examples from classification of simulated Thematic Mapper data for agricultural test sites in North Dakota and Kansas are provided.

CNN universal machine as classificaton platform: an art-like clustering algorithm.

PubMed

Bálya, David

2003-12-01

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.

Determination of total Cr in wastewaters of Cr electroplating factories in the I.organize industry region (Kayseri, Turkey) by ICP-AES.

PubMed

Yilmaz, Selehattin; Türe, Melike; Sadikoglu, Murat; Duran, Ali

2010-08-01

The wastewater pollution in industrial areas is one of the most important environmental problems. Heavy metal pollution, especially chromium pollution in the wastewater sources from electroplating, dyeing, and tannery, has affected the life on earth. This pollution can affect on all ecosystems and human health directly or by food chain. Therefore, the determination of total chromium in this study is of great importance. In this study, accurate, rapid, sensitive, selective, simple, and low-cost technique for the direct determination of total Cr in wastewater samples collected from the some Cr electroplating factories in March 2008 by inductively coupled plasma-atomic emission spectrometry has been developed. The analysis of a given sample is completed in about 15 min by this technique applied. As the result of the chromium analysis, the limit of quantification for the total Cr were founded to be over the limit value (0.05 mg L(-1); WHO, EPA, TSE 266, and inland water quality classification) as 1,898.78+/-0.34 mg/L at station 1 and 3,189.02+/-0.56 mg/L at station 2. The found concentration of total Cr has been determined to be IV class quality water according to the inland water classification. In order to validate the applied method, recovery studies were performed.

Forecasting urban growth across the United States-Mexico border

USGS Publications Warehouse

Norman, L.M.; Feller, M.; Phillip, Guertin D.

2009-01-01

The sister-city area of Nogales, Arizona, and Nogales, Sonora, Mexico, is known collectively as Ambos (both) Nogales. This area was historically one city and was administratively divided by the Gadsden Purchase in 1853. These arid-lands have limited and sensitive natural resources. Environmental planning can support sustainable development to accommodate the predicted influx of population. The objective of this research is to quantify the amount of predicted urban growth for the Ambos Nogales watershed to support future planning for sustainable development. Two modeling regimes are explored. Our goal is to identify possible growth patterns associated with the twin-city area as a whole and with the two cities modeled as separate entities. We analyzed the cross-border watershed using regression analysis from satellite images from 1975, 1983, 1996, and 2002 and created urban area classifications. We used these classifications as input to the urban growth model, SLEUTH, to simulate likely patterns of development and define projected conversion probabilities. Model results indicate that the two cities are undergoing very different patterns of change and identify locations of expected growth based on historical development. Growth in Nogales, Arizona is stagnant while the urban area in Nogales, Sonora is exploding. This paper demonstrates an application that portrays how future binational urban growth could develop and affect the environment. This research also provides locations of potential growth for use in city planning.

PG-Metrics: A chemometric-based approach for classifying bacterial peptidoglycan data sets and uncovering their subjacent chemical variability

PubMed Central

Kumar, Keshav; Espaillat, Akbar; Cava, Felipe

2017-01-01

Bacteria cells are protected from osmotic and environmental stresses by an exoskeleton-like polymeric structure called peptidoglycan (PG) or murein sacculus. This structure is fundamental for bacteria’s viability and thus, the mechanisms underlying cell wall assembly and how it is modulated serve as targets for many of our most successful antibiotics. Therefore, it is now more important than ever to understand the genetics and structural chemistry of the bacterial cell walls in order to find new and effective methods of blocking it for the treatment of disease. In the last decades, liquid chromatography and mass spectrometry have been demonstrated to provide the required resolution and sensitivity to characterize the fine chemical structure of PG. However, the large volume of data sets that can be produced by these instruments today are difficult to handle without a proper data analysis workflow. Here, we present PG-metrics, a chemometric based pipeline that allows fast and easy classification of bacteria according to their muropeptide chromatographic profiles and identification of the subjacent PG chemical variability between e.g. bacterial species, growth conditions and, mutant libraries. The pipeline is successfully validated here using PG samples from different bacterial species and mutants in cell wall proteins. The obtained results clearly demonstrated that PG-metrics pipeline is a valuable bioanalytical tool that can lead us to cell wall classification and biomarker discovery. PMID:29040278

Experimental studies on the nature of sensitive skin.

PubMed

Kligman, A M; Sadiq, Iqbal; Zhen, Yaxian; Crosby, Marilyn

2006-11-01

In the USA, Europe and Japan 40 to 50% of women report that they have sensitive skin, defined as abnormal sub-clinical sensory responses to drugs, cosmetics and toiletries in the absence of visible signs of irritation. Itching, burning, stinging and tightness are the commonest complaints, which mainly afflict women. Manufacturers of skin care products have made available a large variety of products which are designed for persons with sensitive skin. Such products are not required by regulatory agencies to submit evidence of safety and efficacy, allowing marketers to make claims that are often exaggerated, irrational and even preposterous. The consumer with self-assessed sensitive skin has no way of judging which products are likely to be most beneficial and least harmful. The marketplace is awash with products for which there is no evidence that the rosy claims have been substantiated by appropriate testing procedures. There is no internationally accepted consensus regarding the criteria which define sensitive skin. Many papers have been published in the last 15 years, mainly originating from industry, which express widely differing views regarding what constitutes sensitive skin. For some, any adverse reaction to a product topically applied to sensitive skin, including breakouts, redness, scaling etc., a panoply of adverse reactions which is virtually meaningless. Others include environmental factors as causative, including cold, dry wind, heat and high humidity, solar radiation, etc., which add to the manifest complexities of the subject. This is the first paper in a series which provides a comprehensive review of the subject, emphasizing the all too many controversies and confusions arising from the lack of a consensus regarding the identification, classification, epidemiology, prevalence and pathogenesis of sensitive skin. Sensitive skin is a biologic reality and not a psychological, fashionable fantasy on the part of impressionable women. There is an urgent necessity to establish rigorous methodologies for estimating the quality and severity of sensitive skin, a heterogeneous condition involving multi-factorial factors. Subsequent papers in this series will describe in detail the experimental approach our group has used to bring some clarity and credibility to this querulous, but important subject.

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

NASA Astrophysics Data System (ADS)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

ECG signal analysis through hidden Markov models.

PubMed

Andreão, Rodrigo V; Dorizzi, Bernadette; Boudy, Jérôme

2006-08-01

This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application.

Classification of Stellar Spectra with Fuzzy Minimum Within-Class Support Vector Machine

NASA Astrophysics Data System (ADS)

Zhong-bao, Liu; Wen-ai, Song; Jing, Zhang; Wen-juan, Zhao

2017-06-01

Classification is one of the important tasks in astronomy, especially in spectra analysis. Support Vector Machine (SVM) is a typical classification method, which is widely used in spectra classification. Although it performs well in practice, its classification accuracies can not be greatly improved because of two limitations. One is it does not take the distribution of the classes into consideration. The other is it is sensitive to noise. In order to solve the above problems, inspired by the maximization of the Fisher's Discriminant Analysis (FDA) and the SVM separability constraints, fuzzy minimum within-class support vector machine (FMWSVM) is proposed in this paper. In FMWSVM, the distribution of the classes is reflected by the within-class scatter in FDA and the fuzzy membership function is introduced to decrease the influence of the noise. The comparative experiments with SVM on the SDSS datasets verify the effectiveness of the proposed classifier FMWSVM.

Genetic and environmental contributions to age of onset of alcohol dependence symptoms in male twins.

PubMed

Liu, I-Chao; Blacker, Deborah L; Xu, Ronghui; Fitzmaurice, Garrett; Tsuang, Ming T; Lyons, Michael J

2004-11-01

To investigate genetic and environmental influences on the development of specific alcohol dependence symptoms. A classical twin study of 3372 male-male twin pairs in the Vietnam Era Twin (VET) Registry based on telephone interviews about alcohol use. The nine diagnostic symptoms according to the Diagnostic and Statistical Manual of Mental Disorder, version III (revised) (DSM-III-R) definition of alcohol dependence. Symptoms were grouped into those based on impaired control, biological effects and social consequences (Beresford's classification) or early versus late symptoms (Nelson's classification). Survival models with random effects were used to examine the age of onset of each symptom. Approximately 38% of the variation in age of onset of each symptom group based on Beresford's classification is due to additive genetic factors. The age of onset of late symptoms from Nelson's classification appears to be most affected by genetic factors. Estimates of genetic effects for impaired control symptoms are greatly decreased when twins with comorbid psychiatric disorders are excluded. Our results support the heritability of age of onset of DSM-III-R-defined symptoms for alcohol dependence. However, no symptom group in Beresford's classification could be identified as more heritable than other symptom groups. A strong association between genetic vulnerability and co-occurring diseases for symptoms indicative of impaired control could be found. In addition, our findings show that the late symptom group could be a good candidate for subsequent genetic research.

Binary Classification of a Large Collection of Environmental Chemicals from Estrogen Receptor Assays by Quantitative Structure-Activity Relationship and Machine Learning Methods

EPA Science Inventory

ABSTRACT: There are thousands of environmental chemicals subject to regulatory decisions for endocrine disrupting potential. A promising approach to manage this large universe of untested chemicals is to use a prioritization filter that combines in vitro assays with in silico QSA...

The Study on Integrating WebQuest with Mobile Learning for Environmental Education

ERIC Educational Resources Information Center

Chang, Cheng-Sian; Chen, Tzung-Shi; Hsu, Wei-Hsiang

2011-01-01

This study is to demonstrate the impact of different teaching strategies on the learning performance of environmental education using quantitative methods. Students learned about resource recycling and classification through an instructional website based on the teaching tool of WebQuest. There were 103 sixth-grade students participating in this…

COMPARING THE STRENGTHS OF GEOGRAPHIC AND NON-GEOGRAPHIC CLASSIFICATIONS OF STREAM BENTHIC MACROINVERTEBRATES IN THE MID-ATLANTIC HIGHLANDS, USA

EPA Science Inventory

The US Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program (EMAP) sampled approximately 500 wadeable streams in the Mid-Atlantic Highlands region of the US during the late spring of 1993 to 1995 for a variety of physical, chemical and biologi...

IBIS FOR FISH AND MACROINVERTEBRATES DEVELOPED FOR GREAT LAKES COASTAL WETLANDS: AN EPA REGIONAL ENVIRONMENTAL MONITORING AND ASSESSMENT PROGRAM (REMAP) PROJECT

EPA Science Inventory

The research from this REMAP project produced results that demonstrate various stages of an assessment strategy and produced tools including an inventory classification, field methods and multimetric biotic indices that are now available for use by environmental resource managers...

Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units.

PubMed

Youn, Su Hyun; Sim, Taeyong; Choi, Ahnryul; Song, Jinsung; Shin, Ki Young; Lee, Il Kwon; Heo, Hyun Mu; Lee, Daeweon; Mun, Joung Hwan

2015-06-01

Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher (p<0.05 or <0.01) than that of existing single classifiers for all electrode types. In particular, the classification accuracy of the proposed model was highest when the 3mm invasive electrode (Type-II) was used (sensitivity=97.33-100.00%; PPV=96.71-100.00%). The results of this study are an important contribution to achieving automatic optimal output power adjustment of USUs according to the properties of individual tissues. Copyright © 2015 Elsevier Ltd. All rights reserved.

Breast cancer Ki67 expression preoperative discrimination by DCE-MRI radiomics features

NASA Astrophysics Data System (ADS)

Ma, Wenjuan; Ji, Yu; Qin, Zhuanping; Guo, Xinpeng; Jian, Xiqi; Liu, Peifang

2018-02-01

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer. In this institutional review board approved retrospective study, we collected 377 cases Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low- vs. high- Ki67 expression was performed. A set of 52 quantitative radiomics features, including morphological, gray scale statistic, and texture features, were extracted from the segmented lesion area. Three most common machine learning classification methods, including Naive Bayes, k-Nearest Neighbor and support vector machine with Gaussian kernel, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. The model that used Naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC value, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Our study showed that quantitative radiomics imaging features of breast tumor extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

An examination of the potential applications of automatic classification techniques to Georgia management problems

NASA Technical Reports Server (NTRS)

Rado, B. Q.

1975-01-01

Automatic classification techniques are described in relation to future information and natural resource planning systems with emphasis on application to Georgia resource management problems. The concept, design, and purpose of Georgia's statewide Resource AS Assessment Program is reviewed along with participation in a workshop at the Earth Resources Laboratory. Potential areas of application discussed include: agriculture, forestry, water resources, environmental planning, and geology.

Classifying environmental pollutants: Part 3. External validation of the classification system.

PubMed

Verhaar, H J; Solbé, J; Speksnijder, J; van Leeuwen, C J; Hermens, J L

2000-04-01

In order to validate a classification system for the prediction of the toxic effect concentrations of organic environmental pollutants to fish, all available fish acute toxicity data were retrieved from the ECETOC database, a database of quality-evaluated aquatic toxicity measurements created and maintained by the European Centre for the Ecotoxicology and Toxicology of Chemicals. The individual chemicals for which these data were available were classified according to the rulebase under consideration and predictions of effect concentrations or ranges of possible effect concentrations were generated. These predictions were compared to the actual toxicity data retrieved from the database. The results of this comparison show that generally, the classification system provides adequate predictions of either the aquatic toxicity (class 1) or the possible range of toxicity (other classes) of organic compounds. A slight underestimation of effect concentrations occurs for some highly water soluble, reactive chemicals with low log K(ow) values. On the other end of the scale, some compounds that are classified as belonging to a relatively toxic class appear to belong to the so-called baseline toxicity compounds. For some of these, additional classification rules are proposed. Furthermore, some groups of compounds cannot be classified, although they should be amenable to predictions. For these compounds additional research as to class membership and associated prediction rules is proposed.

Scalable clustering algorithms for continuous environmental flow cytometry.

PubMed

Hyrkas, Jeremy; Clayton, Sophie; Ribalet, Francois; Halperin, Daniel; Armbrust, E Virginia; Howe, Bill

2016-02-01

Recent technological innovations in flow cytometry now allow oceanographers to collect high-frequency flow cytometry data from particles in aquatic environments on a scale far surpassing conventional flow cytometers. The SeaFlow cytometer continuously profiles microbial phytoplankton populations across thousands of kilometers of the surface ocean. The data streams produced by instruments such as SeaFlow challenge the traditional sample-by-sample approach in cytometric analysis and highlight the need for scalable clustering algorithms to extract population information from these large-scale, high-frequency flow cytometers. We explore how available algorithms commonly used for medical applications perform at classification of such a large-scale, environmental flow cytometry data. We apply large-scale Gaussian mixture models to massive datasets using Hadoop. This approach outperforms current state-of-the-art cytometry classification algorithms in accuracy and can be coupled with manual or automatic partitioning of data into homogeneous sections for further classification gains. We propose the Gaussian mixture model with partitioning approach for classification of large-scale, high-frequency flow cytometry data. Source code available for download at https://github.com/jhyrkas/seaflow_cluster, implemented in Java for use with Hadoop. hyrkas@cs.washington.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

FHR patterns that become significant in connection with ST waveform changes and metabolic acidosis at birth.

PubMed

Rosén, Karl G; Norén, Håkan; Carlsson, Ann

2018-04-18

Recent developments have produced new CTG classification systems and the question is to what extent these may affect the model of FHR + ST interpretation? The two new systems (FIGO2015 and SSOG2017) classify FHR + ST events differently from the current CTG classification system used in the STAN interpretation algorithm (STAN2007). Identify the predominant FHR patterns in connection with ST events in cases of cord artery metabolic acidosis missed by the different CTG classification systems. Indicate to what extent STAN clinical guidelines could be modified enhancing the sensitivity. Provide a pathophysiological rationale. Forty-four cases with umbilical cord artery metabolic acidosis were retrieved from a European multicenter database. Significant FHR + ST events were evaluated post hoc in consensus by an expert panel. Eighteen cases were not identified as in need of intervention and regarded as negative in the sensitivity analysis. In 12 cases, ST changes occurred but the CTG was regarded as reassuring. Visual analysis of the FHR + ST tracings revealed specific FHR patterns: Conclusion: These findings indicate FHR + ST analysis may be undertaken regardless of CTG classification system provided there is a more physiologically oriented approach to FHR assessment in connection with an ST event.

Plaque Burden Influences Accurate Classification of Fibrous Cap Atheroma by In-Vivo Optical Coherence Tomography in a Porcine Model of Advanced Coronary Atherosclerosis.

PubMed

Poulsen, Christian B; Pedrigi, Ryan M; Pareek, Nilesh; Kilic, Ismail D; Holm, Niels Ramsing; Bentzon, Jacob F; Bøtker, Hans Erik; Falk, Erling; Krams, Rob; de Silva, Ranil

2018-04-03

In-vivo validation of coronary optical coherence tomography (OCT) against histology and the effects of plaque burden (PB) on plaque classification remain unreported. We investigated this in a porcine model with human-like coronary atherosclerosis. Five female Yucatan D374Y-PCSK9 transgenic hypercholesterolemic mini-pigs were implanted with a coronary shear-modifying stent to induce advanced atherosclerosis. OCT frames (n=201) were obtained 34 weeks after implantation. Coronary arteries were perfusion-fixed, serially sectioned and co-registered with OCT using a validated algorithm. Lesions were adjudicated using the Virmani classification and PB assessed from histology. OCT had a high sensitivity, but modest specificity (92.9% and 74.6%), for identifying fibrous cap atheroma (FCA). The reduced specificity for OCT was due to misclassification of plaques with histologically defined pathological intimal thickening (PIT) as FCA (46.1% of the frames with histological PIT were misclassified). PIT lesions misclassified as FCA by OCT had a statistically higher PB than in other OCT frames (median 32.0% versus 13.4%; p<0.0001). Misclassification of PIT lesions by OCT occurred when PB exceeded approximately 20%. Compared with histology, in-vivo OCT classification of FCA had high sensitivity but reduced specificity due to misclassification of PITs with high PB.

Classification of CT examinations for COPD visual severity analysis

NASA Astrophysics Data System (ADS)

Tan, Jun; Zheng, Bin; Wang, Xingwei; Pu, Jiantao; Gur, David; Sciurba, Frank C.; Leader, J. Ken

2012-03-01

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

USGS Publications Warehouse

Wright, C.; Gallant, Alisa L.

2007-01-01

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub–shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.

Automated detection of radioisotopes from an aircraft platform by pattern recognition analysis of gamma-ray spectra.

PubMed

Dess, Brian W; Cardarelli, John; Thomas, Mark J; Stapleton, Jeff; Kroutil, Robert T; Miller, David; Curry, Timothy; Small, Gary W

2018-03-08

A generalized methodology was developed for automating the detection of radioisotopes from gamma-ray spectra collected from an aircraft platform using sodium-iodide detectors. Employing data provided by the U.S Environmental Protection Agency Airborne Spectral Photometric Environmental Collection Technology (ASPECT) program, multivariate classification models based on nonparametric linear discriminant analysis were developed for application to spectra that were preprocessed through a combination of altitude-based scaling and digital filtering. Training sets of spectra for use in building classification models were assembled from a combination of background spectra collected in the field and synthesized spectra obtained by superimposing laboratory-collected spectra of target radioisotopes onto field backgrounds. This approach eliminated the need for field experimentation with radioactive sources for use in building classification models. Through a bi-Gaussian modeling procedure, the discriminant scores that served as the outputs from the classification models were related to associated confidence levels. This provided an easily interpreted result regarding the presence or absence of the signature of a specific radioisotope in each collected spectrum. Through the use of this approach, classifiers were built for cesium-137 ( 137 Cs) and cobalt-60 ( 60 Co), two radioisotopes that are of interest in airborne radiological monitoring applications. The optimized classifiers were tested with field data collected from a set of six geographically diverse sites, three of which contained either 137 Cs, 60 Co, or both. When the optimized classification models were applied, the overall percentages of correct classifications for spectra collected at these sites were 99.9 and 97.9% for the 60 Co and 137 Cs classifiers, respectively. Copyright © 2018 Elsevier Ltd. All rights reserved.

Heat stress disorders and headache: a case of new daily persistent headache secondary to heat stroke.

PubMed

Di Lorenzo, C; Ambrosini, A; Coppola, G; Pierelli, F

2009-01-01

Headache is considered as a common symptom of heat stress disorders (HSD), but no forms of secondary headache from heat exposure are reported in the International Classification of Headache Disorders-2 Edition (ICHD-II). Heat-stroke (HS) is the HSD most severe condition, it may be divided into two forms: classic (due to a long period environmental heat exposure) and exertional (a severe condition caused by strenuous physical exercises in heat environmental conditions). Here we report the case of a patient who developed a headache clinical picture fulfilling the diagnostic criteria for new daily persistent headache (NDPH), after an exertional HS, and discuss about possible pathophysiological mechanisms and classification aspects of headache induced by heat conditions.

Heat stress disorders and headache: a case of new daily persistent headache secondary to heat stroke

PubMed Central

Di Lorenzo, C; Ambrosini, A; Coppola, G; Pierelli, F

2009-01-01

Headache is considered as a common symptom of heat stress disorders (HSD), but no forms of secondary headache from heat exposure are reported in the International Classification of Headache Disorders-2 Edition (ICHD-II). Heat-stroke (HS) is the HSD most severe condition, it may be divided into two forms: classic (due to a long period environmental heat exposure) and exertional (a severe condition caused by strenuous physical exercises in heat environmental conditions). Here we report the case of a patient who developed a headache clinical picture fulfilling the diagnostic criteria for new daily persistent headache (NDPH), after an exertional HS, and discuss about possible pathophysiological mechanisms and classification aspects of headache induced by heat conditions. PMID:21686677

Rapid assessment of antimicrobial resistance prevalence using a Lot Quality Assurance sampling approach.

PubMed

van Leth, Frank; den Heijer, Casper; Beerepoot, Mariëlle; Stobberingh, Ellen; Geerlings, Suzanne; Schultsz, Constance

2017-04-01

Increasing antimicrobial resistance (AMR) requires rapid surveillance tools, such as Lot Quality Assurance Sampling (LQAS). LQAS classifies AMR as high or low based on set parameters. We compared classifications with the underlying true AMR prevalence using data on 1335 Escherichia coli isolates from surveys of community-acquired urinary tract infection in women, by assessing operating curves, sensitivity and specificity. Sensitivity and specificity of any set of LQAS parameters was above 99% and between 79 and 90%, respectively. Operating curves showed high concordance of the LQAS classification with true AMR prevalence estimates. LQAS-based AMR surveillance is a feasible approach that provides timely and locally relevant estimates, and the necessary information to formulate and evaluate guidelines for empirical treatment.

A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).

PubMed

Suzuki, Kenji

2009-09-21

Computer-aided diagnosis (CAD) has been an active area of study in medical image analysis. A filter for the enhancement of lesions plays an important role for improving the sensitivity and specificity in CAD schemes. The filter enhances objects similar to a model employed in the filter; e.g. a blob-enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model; e.g. a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with internal inhomogeneities such as a nodule with spiculations and ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for the enhancement of actual lesions (as opposed to a lesion model) by use of a massive-training artificial neural network (MTANN) in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With a database of 69 lung cancers, nodule candidate detection by the MTANN filter achieved a 97% sensitivity with 6.7 false positives (FPs) per section, whereas nodule candidate detection by a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section. Classification-MTANNs were applied for further reduction of the FPs. The classification-MTANNs removed 60% of the FPs with a loss of one true positive; thus, it achieved a 96% sensitivity with 2.7 FPs per section. Overall, with our CAD scheme based on the MTANN filter and classification-MTANNs, an 84% sensitivity with 0.5 FPs per section was achieved.

Noise sensitivity: Symptoms, health status, illness behavior and co-occurring environmental sensitivities

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

Baliatsas, Christos, E-mail: c.baliatsas@nivel.nl

Epidemiological evidence on the symptomatic profile, health status and illness behavior of people with subjective sensitivity to noise is still scarce. Also, it is unknown to what extent noise sensitivity co-occurs with other environmental sensitivities such as multi-chemical sensitivity and sensitivity to electromagnetic fields (EMF). A cross-sectional study performed in the Netherlands, combining self-administered questionnaires and electronic medical records of non-specific symptoms (NSS) registered by general practitioners (GP) allowed us to explore this further. The study sample consisted of 5806 participants, drawn from 21 general practices. Among participants, 722 (12.5%) responded “absolutely agree” to the statement “I am sensitive tomore » noise”, comprising the high noise-sensitive (HNS) group. Compared to the rest of the sample, people in the HNS group reported significantly higher scores on number and duration of self-reported NSS, increased psychological distress, decreased sleep quality and general health, more negative symptom perceptions and higher prevalence of healthcare contacts, GP-registered NSS and prescriptions for antidepressants and benzodiazepines. These results remained robust after adjustment for demographic, residential and lifestyle characteristics, objectively measured nocturnal noise exposure from road-traffic and GP-registered morbidity. Co-occurrence rates with other environmental sensitivities varied between 9% and 50%. Individuals with self-declared sensitivity to noise are characterized by high prevalence of multiple NSS, poorer health status and increased illness behavior independently of noise exposure levels. Findings support the notion that different types of environmental sensitivities partly overlap. - Highlights: • People with self-reported noise sensitivity experience multiple non-specific symptoms. • They also report comparatively poorer health and increased illness behavior. • Co-occurrence with other environmental sensitivities is moderate to high. • Road-traffic noise and GP-registered morbidity did not account for these results.« less

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

PubMed Central

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-01-01

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

Species Profiles: Life Histories and Environmental Requirements of Coastal Vertebrates and Invertebrates Pacific Ocean Region. Report 5. The Parrotfishes, Family Scaridae

DTIC Science & Technology

1991-03-01

AND ENVIRONMENTAL REQUIREMENTS OF COASTAL VERTEBRATES AND INVERTEBRATES PACIFIC OCEAN REGION Report 5 THE PARROTFISHES, FAMILY SCARIDAE by R. E. Brock...SeaGrant College Program and . _Hawaii Institute of Marine Biology University of Hawaii P.O. Box 1346, Coconut Island Kaneohe, Hawaii 96744 ,A DTIC...TITLE (Include Security Classification) Species Profiles: Life Histories and Environmental Requirements of’Coastal Vertebrates and Invertebrates

Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems with Correlated Random Variables

DTIC Science & Technology

2010-08-01

a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. a ...SECURITY CLASSIFICATION OF: This study presents a methodology for computing stochastic sensitivities with respect to the design variables, which are the...Random Variables Report Title ABSTRACT This study presents a methodology for computing stochastic sensitivities with respect to the design variables

Automatic classification of transiently evoked otoacoustic emissions using an artificial neural network.

PubMed

Buller, G; Lutman, M E

1998-08-01

The increasing use of transiently evoked otoacoustic emissions (TEOAE) in large neonatal hearing screening programmes makes a standardized method of response classification desirable. Until now methods have been either subjective or based on arbitrary response characteristics. This study takes an expert system approach to standardize the subjective judgements of an experienced scorer. The method that is developed comprises three stages. First, it transforms TEOAEs from waveforms in the time domain into a simplified parameter set. Second, the parameter set is classified by an artificial neural network that has been taught on a large database TEOAE waveforms and corresponding expert scores. Third, additional fuzzy logic rules automatically detect probable artefacts in the waveforms and synchronized spontaneous emission components. In this way, the knowledge of the experienced scorer is encapsulated in the expert system software and thereafter can be accessed by non-experts. Teaching and evaluation of the neural network was based on TEOAEs from a database totalling 2190 neonatal hearing screening tests. The database was divided into learning and test groups with 820 and 1370 waveforms respectively. From each recorded waveform a set of 12 parameters was calculated, representing signal static and dynamic properties. The artifical network was taught with parameter sets of only the learning groups. Reproduction of the human scorer classification by the neural net in the learning group showed a sensitivity for detecting screen fails of 99.3% (299 from 301 failed results on subjective scoring) and a specificity for detecting screen passes of 81.1% (421 of 519 pass results). To quantify the post hoc performance of the net (generalization), the test group was then presented to the network input. Sensitivity was 99.4% (474 from 477) and specificity was 87.3% (780 from 893). To check the efficiency of the classification method, a second learning group was selected out of the previous test group, and the previous learning group was used as the test group. Repeating learning and test procedures yielded 99.3% sensitivity and 80.7% specificity for reproduction, and 99.4% sensitivity and 86.7% specificity for generalization. In all respects, performance was better than for a previously optimized method based simply on cross-correlation between replicate non-linear waveforms. It is concluded that classification methods based on neural networks show promise for application to large neonatal screening programmes utilizing TEOAEs.

U.S. Geological Survey ArcMap Sediment Classification tool

USGS Publications Warehouse

O'Malley, John

2007-01-01

The U.S. Geological Survey (USGS) ArcMap Sediment Classification tool is a custom toolbar that extends the Environmental Systems Research Institute, Inc. (ESRI) ArcGIS 9.2 Desktop application to aid in the analysis of seabed sediment classification. The tool uses as input either a point data layer with field attributes containing percentage of gravel, sand, silt, and clay or four raster data layers representing a percentage of sediment (0-100%) for the various sediment grain size analysis: sand, gravel, silt and clay. This tool is designed to analyze the percent of sediment at a given location and classify the sediments according to either the Folk (1954, 1974) or Shepard (1954) as modified by Schlee(1973) classification schemes. The sediment analysis tool is based upon the USGS SEDCLASS program (Poppe, et al. 2004).

Classification of Aerial Photogrammetric 3d Point Clouds

NASA Astrophysics Data System (ADS)

Becker, C.; Häni, N.; Rosinskaya, E.; d'Angelo, E.; Strecha, C.

2017-05-01

We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.

Polarization-based material classification technique using passive millimeter-wave polarimetric imagery.

PubMed

Hu, Fei; Cheng, Yayun; Gui, Liangqi; Wu, Liang; Zhang, Xinyi; Peng, Xiaohui; Su, Jinlong

2016-11-01

The polarization properties of thermal millimeter-wave emission capture inherent information of objects, e.g., material composition, shape, and surface features. In this paper, a polarization-based material-classification technique using passive millimeter-wave polarimetric imagery is presented. Linear polarization ratio (LPR) is created to be a new feature discriminator that is sensitive to material type and to remove the reflected ambient radiation effect. The LPR characteristics of several common natural and artificial materials are investigated by theoretical and experimental analysis. Based on a priori information about LPR characteristics, the optimal range of incident angle and the classification criterion are discussed. Simulation and measurement results indicate that the presented classification technique is effective for distinguishing between metals and dielectrics. This technique suggests possible applications for outdoor metal target detection in open scenes.

European validation of The Comprehensive International Classification of Functioning, Disability and Health Core Set for Osteoarthritis from the perspective of patients with osteoarthritis of the knee or hip.

PubMed

Weigl, Martin; Wild, Heike

2017-09-15

To validate the International Classification of Functioning, Disability and Health Comprehensive Core Set for Osteoarthritis from the patient perspective in Europe. This multicenter cross-sectional study involved 375 patients with knee or hip osteoarthritis. Trained health professionals completed the Comprehensive Core Set, and patients completed the Short-Form 36 questionnaire. Content validity was evaluated by calculating prevalences of impairments in body function and structures, limitations in activities and participation and environmental factors, which were either barriers or facilitators. Convergent construct validity was evaluated by correlating the International Classification of Functioning, Disability and Health categories with the Short-Form 36 Physical Component Score and the SF-36 Mental Component Score in a subgroup of 259 patients. The prevalences of all body function, body structure and activities and participation categories were >40%, >32% and >20%, respectively, and all environmental factors were relevant for >16% of patients. Few categories showed relevant differences between knee and hip osteoarthritis. All body function categories and all but two activities and participation categories showed significant correlations with the Physical Component Score. Body functions from the ICF chapter Mental Functions showed higher correlations with the Mental Component Score than with the Physical Component Score. This study supports the validity of the International Classification of Functioning, Disability and Health Comprehensive Core Set for Osteoarthritis. Implications for Rehabilitation Comprehensive International Classification of Functioning, Disability and Health Core Sets were developed as practical tools for application in multidisciplinary assessments. The validity of the Comprehensive International Classification of Functioning, Disability and Health Core Set for Osteoarthritis in this study supports its application in European patients with osteoarthritis. The differences in results between this Europe validation study and a previous Singaporean validation study underscore the need to validate the International Classification of Functioning, Disability and Health Core Sets in different regions of the world.

Gradient Analysis and Classification of Carolina Bay Vegetation: A Framework for Bay Wetlands Conservation and Restoration

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

Diane De Steven,Ph.D.; Maureen Tone,PhD.

1997-10-01

This report address four project objectives: (1) Gradient model of Carolina bay vegetation on the SRS--The authors use ordination analyses to identify environmental and landscape factors that are correlated with vegetation composition. Significant factors can provide a framework for site-based conservation of existing diversity, and they may also be useful site predictors for potential vegetation in bay restorations. (2) Regional analysis of Carolina bay vegetation diversity--They expand the ordination analyses to assess the degree to which SRS bays encompass the range of vegetation diversity found in the regional landscape of South Carolina's western Upper Coastal Plain. Such comparisons can indicatemore » floristic status relative to regional potentials and identify missing species or community elements that might be re-introduced or restored. (3) Classification of vegetation communities in Upper Coastal Plain bays--They use cluster analysis to identify plant community-types at the regional scale, and explore how this classification may be functional with respect to significant environmental and landscape factors. An environmentally-based classification at the whole-bay level can provide a system of templates for managing bays as individual units and for restoring bays to desired plant communities. (4) Qualitative model for bay vegetation dynamics--They analyze present-day vegetation in relation to historic land uses and disturbances. The distinctive history of SRS bays provides the possibility of assessing pathways of post-disturbance succession. They attempt to develop a coarse-scale model of vegetation shifts in response to changing site factors; such qualitative models can provide a basis for suggesting management interventions that may be needed to maintain desired vegetation in protected or restored bays.« less

Design of a hybrid model for cardiac arrhythmia classification based on Daubechies wavelet transform.

PubMed

Rajagopal, Rekha; Ranganathan, Vidhyapriya

2018-06-05

Automation in cardiac arrhythmia classification helps medical professionals make accurate decisions about the patient's health. The aim of this work was to design a hybrid classification model to classify cardiac arrhythmias. The design phase of the classification model comprises the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through Daubechies wavelet transform, and arrhythmia classification using a collaborative decision from the K nearest neighbor classifier (KNN) and a support vector machine (SVM). The proposed model is able to classify 5 arrhythmia classes as per the ANSI/AAMI EC57: 1998 classification standard. Level 1 of the proposed model involves classification using the KNN and the classifier is trained with examples from all classes. Level 2 involves classification using an SVM and is trained specifically to classify overlapped classes. The final classification of a test heartbeat pertaining to a particular class is done using the proposed KNN/SVM hybrid model. The experimental results demonstrated that the average sensitivity of the proposed model was 92.56%, the average specificity 99.35%, the average positive predictive value 98.13%, the average F-score 94.5%, and the average accuracy 99.78%. The results obtained using the proposed model were compared with the results of discriminant, tree, and KNN classifiers. The proposed model is able to achieve a high classification accuracy.

Optimal two-phase sampling design for comparing accuracies of two binary classification rules.

PubMed

Xu, Huiping; Hui, Siu L; Grannis, Shaun

2014-02-10

In this paper, we consider the design for comparing the performance of two binary classification rules, for example, two record linkage algorithms or two screening tests. Statistical methods are well developed for comparing these accuracy measures when the gold standard is available for every unit in the sample, or in a two-phase study when the gold standard is ascertained only in the second phase in a subsample using a fixed sampling scheme. However, these methods do not attempt to optimize the sampling scheme to minimize the variance of the estimators of interest. In comparing the performance of two classification rules, the parameters of primary interest are the difference in sensitivities, specificities, and positive predictive values. We derived the analytic variance formulas for these parameter estimates and used them to obtain the optimal sampling design. The efficiency of the optimal sampling design is evaluated through an empirical investigation that compares the optimal sampling with simple random sampling and with proportional allocation. Results of the empirical study show that the optimal sampling design is similar for estimating the difference in sensitivities and in specificities, and both achieve a substantial amount of variance reduction with an over-sample of subjects with discordant results and under-sample of subjects with concordant results. A heuristic rule is recommended when there is no prior knowledge of individual sensitivities and specificities, or the prevalence of the true positive findings in the study population. The optimal sampling is applied to a real-world example in record linkage to evaluate the difference in classification accuracy of two matching algorithms. Copyright © 2013 John Wiley & Sons, Ltd.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

PubMed Central

ChariDingari, Narahara; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P.; Kumar, G. Manoj

2012-01-01

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real world applications, e.g. quality assurance and process monitoring. Specifically, variability in sample, system and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a non-linear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), due to its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data – highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples as well as in related areas of forensic and biological sample analysis. PMID:22292496

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability.

PubMed

Dingari, Narahara Chari; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P; Kumar Gundawar, Manoj

2012-03-20

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

Classification accuracy of brief parent report measures of language development in Spanish-speaking toddlers.

PubMed

Guiberson, Mark; Rodríguez, Barbara L; Dale, Philip S

2011-10-01

The purpose of the current study was to examine the concurrent validity and classification accuracy of 3 parent report measures of language development in Spanish-speaking toddlers. Forty-five Spanish-speaking parents and their 2-year-old children participated. Twenty-three children had expressive language delays (ELDs) as determined through multiple sources of information, and 22 had typical language development (TD). Parents completed the Spanish version of the Ages and Stages Questionnaire (Spanish ASQ; Squires, Potter, & Bricker, 1999) and the short-form of the Inventarios del Desarrollo de Habilidades Comunicativas Palabras y Enunciados (INV-II; Jackson-Maldonado, Bates, & Thal, 1992; Jackson-Maldonado et al., 2003), which is the Spanish version of the MacArthur-Bates Communicative Development Inventories Words and Sentences form, and reported children's 3 longest utterances (M3L-W). Children were administered the Preschool Language Scale, Fourth Edition, Spanish Edition (SPLS-4; Zimmerman, Steiner, & Pond, 2002) at early childhood centers. All 3 parent report measures were significantly correlated with the SPLS-4, establishing their concurrent validity. Children with ELDs scored significantly lower than TD children on all 3 parent report measures. The Spanish ASQ demonstrated less than desirable levels of sensitivity and specificity; both the short-form INV-II and M3L-W measures demonstrated favorable sensitivity and specificity. Of these measures, M3L-W demonstrated the strongest classification accuracy qualities, including sensitivity, negative predictive value, and area under the receiver operating characteristics curve. The short-form INV-II and M3L-W demonstrated highly satisfactory classification accuracy of ELDs, but M3L-W demonstrated slightly stronger accuracy. These results indicate that these measures may be useful in screening for ELDs in Spanish-speaking toddlers.

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

NASA Astrophysics Data System (ADS)

Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2016-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

The Role of Configural Processing in Face Classification by Race: An ERP Study

PubMed Central

Lv, Jing; Yan, Tianyi; Tao, Luyang; Zhao, Lun

2015-01-01

The current study investigated the time course of the other-race classification advantage (ORCA) in the subordinate classification of normally configured faces and distorted faces by race. Slightly distorting the face configuration delayed the categorization of own-race faces and had no conspicuous effects on other-race faces. The N170 was sensitive neither to configural distortions nor to faces' races. The P3 was enhanced for other-race than own-race faces and reduced by configural manipulation only for own-race faces. We suggest that the source of ORCA is the configural analysis applied by default while processing own-race faces. PMID:26733850