Identifying Medication Management Smartphone App Features Suitable for Young Adults With Developmental Disabilities: Delphi Consensus Study

PubMed Central

Salgado, Teresa M; Fedrigon, Alexa; Riccio Omichinski, Donna; Meade, Michelle A

2018-01-01

Background Smartphone apps can be a tool to facilitate independent medication management among persons with developmental disabilities. At present, multiple medication management apps exist in the market, but only 1 has been specifically designed for persons with developmental disabilities. Before initiating further app development targeting this population, input from stakeholders including persons with developmental disabilities, caregivers, and professionals regarding the most preferred features should be obtained. Objective The aim of this study was to identify medication management app features that are suitable to promote independence in the medication management process by young adults with developmental disabilities using a Delphi consensus method. Methods A compilation of medication management app features was performed by searching the iTunes App Store, United States, in February 2016, using the following terms: adherence, medication, medication management, medication list, and medication reminder. After identifying features within the retrieved apps, a final list of 42 features grouped into 4 modules (medication list, medication reminder, medication administration record, and additional features) was included in a questionnaire for expert consensus rating. A total of 52 experts in developmental disabilities, including persons with developmental disabilities, caregivers, and professionals, were invited to participate in a 3-round Delphi technique. The purpose was to obtain consensus on features that are preferred and suitable to promote independence in the medication management process among persons with developmental disabilities. Consensus for the first, second, and third rounds was defined as ≥90%, ≥80%, and ≥75% agreement, respectively. Results A total of 75 responses were received over the 3 Delphi rounds—30 in the first round, 24 in the second round, and 21 in the third round. At the end of the third round, cumulative consensus was achieved for 60% (12/20) items in the medication list module, 100% (3/3) in the medication reminder module, 67% (2/3) in the medication administration record module, and 63% (10/16) in the additional features module. In addition to the medication list, medication reminder, and medication administration record features, experts selected the following top 3 most important additional features: automatic refills through pharmacies; ability to share medication information from the app with providers; and ability to share medication information from the app with family, friends, and caregivers. The top 3 least important features included a link to an official drug information source, privacy settings and password protection, and prescription refill reminders. Conclusions Although several mobile apps for medication management exist, few are specifically designed to support persons with developmental disabilities in the complex medication management process. Of the 42 different features assessed, 64% (27/42) achieved consensus for inclusion in a future medication management app. This study provides information on the features of a medication management app that are most important to persons with developmental disabilities, caregivers, and professionals. PMID:29792292

Documentation of a digital spatial data base for hydrologic investigations, Broward County, Florida

USGS Publications Warehouse

Sonenshein, R.S.

1992-01-01

Geographic information systems have become an important tool in planning for the protection and development of natural resources, including ground water and surface water. A digital spatial data base consisting of 18 data layers that can be accessed by a geographic information system was developed for Broward County, Florida. Five computer programs, including one that can be used to create documentation files for each data layer and four that can be used to create data layers from data files not already in geographic information system format, were also developed. Four types of data layers have been developed. Data layers for manmade features include major roads, municipal boundaries, the public land-survey section grid, land use, and underground storage tank facilities. The data layer for topographic features consists of surveyed point land-surface elevations. Data layers for hydrologic features include surface-water and rainfall data-collection stations, surface-water bodies, water-control district boundaries, and water-management basins. Data layers for hydrogeologic features include soil associations, transmissivity polygons, hydrogeologic unit depths, and a finite-difference model grid for south-central Broward County. Each data layer is documented as to the extent of the features, number of features, scale, data sources, and a description of the attribute tables where applicable.

Modeling asthma: Pitfalls, promises, and the road ahead.

PubMed

Rosenberg, Helene F; Druey, Kirk M

2018-02-16

Asthma is a chronic, heterogeneous, and recurring inflammatory disease of the lower airways, with exacerbations that feature airway inflammation and bronchial hyperresponsiveness. Asthma has been modeled extensively via disease induction in both wild-type and genetically manipulated laboratory mice (Mus musculus). Antigen sensitization and challenge strategies have reproduced numerous important features of airway inflammation characteristic of human asthma, notably the critical roles of type 2 T helper cell cytokines. Recent models of disease induction have advanced to include physiologic aeroallergens with prolonged respiratory challenge without systemic sensitization; others incorporate tobacco, respiratory viruses, or bacteria as exacerbants. Nonetheless, differences in lung size, structure, and physiologic responses limit the degree to which airway dynamics measured in mice can be compared to human subjects. Other rodent allergic airways models, including those featuring the guinea pig (Cavia porcellus) might be considered for lung function studies. Finally, domestic cats (Feline catus) and horses (Equus caballus) develop spontaneous obstructive airway disorders with clinical and pathologic features that parallel human asthma. Information on pathogenesis and treatment of these disorders is an important resource. ©2018 Society for Leukocyte Biology.

Developing a radiomics framework for classifying non-small cell lung carcinoma subtypes

NASA Astrophysics Data System (ADS)

Yu, Dongdong; Zang, Yali; Dong, Di; Zhou, Mu; Gevaert, Olivier; Fang, Mengjie; Shi, Jingyun; Tian, Jie

2017-03-01

Patient-targeted treatment of non-small cell lung carcinoma (NSCLC) has been well documented according to the histologic subtypes over the past decade. In parallel, recent development of quantitative image biomarkers has recently been highlighted as important diagnostic tools to facilitate histological subtype classification. In this study, we present a radiomics analysis that classifies the adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). We extract 52-dimensional, CT-based features (7 statistical features and 45 image texture features) to represent each nodule. We evaluate our approach on a clinical dataset including 324 ADCs and 110 SqCCs patients with CT image scans. Classification of these features is performed with four different machine-learning classifiers including Support Vector Machines with Radial Basis Function kernel (RBF-SVM), Random forest (RF), K-nearest neighbor (KNN), and RUSBoost algorithms. To improve the classifiers' performance, optimal feature subset is selected from the original feature set by using an iterative forward inclusion and backward eliminating algorithm. Extensive experimental results demonstrate that radiomics features achieve encouraging classification results on both complete feature set (AUC=0.89) and optimal feature subset (AUC=0.91).

CASPER Version 2.0

NASA Technical Reports Server (NTRS)

Chien, Steve; Rabideau, Gregg; Tran, Daniel; Knight, Russell; Chouinard, Caroline; Estlin, Tara; Gaines, Daniel; Clement, Bradley; Barrett, Anthony

2007-01-01

CASPER is designed to perform automated planning of interdependent activities within a system subject to requirements, constraints, and limitations on resources. In contradistinction to the traditional concept of batch planning followed by execution, CASPER implements a concept of continuous planning and replanning in response to unanticipated changes (including failures), integrated with execution. Improvements over other, similar software that have been incorporated into CASPER version 2.0 include an enhanced executable interface to facilitate integration with a wide range of execution software systems and supporting software libraries; features to support execution while reasoning about urgency, importance, and impending deadlines; features that enable accommodation to a wide range of computing environments that include various central processing units and random- access-memory capacities; and improved generic time-server and time-control features.

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

PubMed Central

Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu

2012-01-01

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017

Prioritizing the mHealth Design Space: A Mixed-Methods Analysis of Smokers’ Perspectives

PubMed Central

BlueSpruce, June; Catz, Sheryl L; McClure, Jennifer B

2016-01-01

Background Smoking remains the leading cause of preventable disease and death in the United States. Therefore, researchers are constantly exploring new ways to promote smoking cessation. Mobile health (mHealth) technologies could be effective cessation tools. Despite the availability of commercial quit-smoking apps, little research to date has examined smokers’ preferred treatment intervention components (ie, design features). Honoring these preferences is important for designing programs that are appealing to smokers and may be more likely to be adopted and used. Objective The aim of this study was to understand smokers’ preferred design features of mHealth quit-smoking tools. Methods We used a mixed-methods approach consisting of focus groups and written surveys to understand the design preferences of adult smokers who were interested in quitting smoking (N=40). Focus groups were stratified by age to allow differing perspectives to emerge between older (>40 years) and younger (<40 years) participants. Focus group discussion included a “blue-sky” brainstorming exercise followed by participant reactions to contrasting design options for communicating with smokers, providing social support, and incentivizing program use. Participants rated the importance of preselected design features on an exit survey. Qualitative analyses examined emergent discussion themes and quantitative analyses compared feature ratings to determine which were perceived as most important. Results Participants preferred a highly personalized and adaptive mHealth experience. Their ideal mHealth quit-smoking tool would allow personalized tracking of their progress, adaptively tailored feedback, and real-time peer support to help manage smoking cravings. Based on qualitative analysis of focus group discussion, participants preferred pull messages (ie, delivered upon request) over push messages (ie, sent automatically) and preferred interaction with other smokers through closed social networks. Preferences for entertaining games or other rewarding incentives to encourage program use differed by age group. Based on quantitative analysis of surveys, participants rated the importance of select design features significantly differently (P<.001). Design features rated as most important included personalized content, the ability to track one’s progress, and features designed to help manage nicotine withdrawal and medication side effects. Design features rated least important were quit-smoking videos and posting on social media. Communicating with stop-smoking experts was rated more important than communicating with family and friends about quitting (P=.03). Perceived importance of various design features varied by age, experience with technology, and frequency of smoking. Conclusions Future mHealth cessation aids should be designed with an understanding of smokers’ needs and preferences for these tools. Doing so does not guarantee treatment effectiveness, but balancing user preferences with best-practice treatment considerations could enhance program adoption and improve treatment outcomes. Grounded in the perspectives of smokers, we identify several design considerations, which should be prioritized when designing future mHealth cessation tools and which warrant additional empirical validation. PMID:27496593

Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm

PubMed Central

Wang, ShaoPeng; Zhang, Yu-Hang; Lu, Jing; Cui, Weiren; Hu, Jerry; Cai, Yu-Dong

2016-01-01

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request. PMID:26955638

Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm.

PubMed

Wang, ShaoPeng; Zhang, Yu-Hang; Lu, Jing; Cui, Weiren; Hu, Jerry; Cai, Yu-Dong

2016-01-01

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request.

Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China.

PubMed

Zhai, Binxu; Chen, Jianguo

2018-04-18

A stacked ensemble model is developed for forecasting and analyzing the daily average concentrations of fine particulate matter (PM 2.5 ) in Beijing, China. Special feature extraction procedures, including those of simplification, polynomial, transformation and combination, are conducted before modeling to identify potentially significant features based on an exploratory data analysis. Stability feature selection and tree-based feature selection methods are applied to select important variables and evaluate the degrees of feature importance. Single models including LASSO, Adaboost, XGBoost and multi-layer perceptron optimized by the genetic algorithm (GA-MLP) are established in the level 0 space and are then integrated by support vector regression (SVR) in the level 1 space via stacked generalization. A feature importance analysis reveals that nitrogen dioxide (NO 2 ) and carbon monoxide (CO) concentrations measured from the city of Zhangjiakou are taken as the most important elements of pollution factors for forecasting PM 2.5 concentrations. Local extreme wind speeds and maximal wind speeds are considered to extend the most effects of meteorological factors to the cross-regional transportation of contaminants. Pollutants found in the cities of Zhangjiakou and Chengde have a stronger impact on air quality in Beijing than other surrounding factors. Our model evaluation shows that the ensemble model generally performs better than a single nonlinear forecasting model when applied to new data with a coefficient of determination (R 2 ) of 0.90 and a root mean squared error (RMSE) of 23.69μg/m 3 . For single pollutant grade recognition, the proposed model performs better when applied to days characterized by good air quality than when applied to days registering high levels of pollution. The overall classification accuracy level is 73.93%, with most misclassifications made among adjacent categories. The results demonstrate the interpretability and generalizability of the stacked ensemble model. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Parity violation in electron scattering

DOE PAGES

Souder, P.; Paschke, K. D.

2015-12-22

By comparing the cross sections for left- and right-handed electrons scattered from various unpolarized nuclear targets, the small parity-violating asymmetry can be measured. These asymmetry data probe a wide variety of important topics, including searches for new fundamental interactions and important features of nuclear structure that cannot be studied with other probes. A special feature of these experiments is that the results are interpreted with remarkably few theoretical uncertainties, which justifies pushing the experiments to the highest possible precision. To measure the small asymmetries accurately, a number of novel experimental techniques have been developed.

An Analysis of Activities in Saudi Arabian Middle School Science Textbooks and Workbooks for the Inclusion of Essential Features of Inquiry

NASA Astrophysics Data System (ADS)

Aldahmash, Abdulwali H.; Mansour, Nasser S.; Alshamrani, Saeed M.; Almohi, Saeed

2016-12-01

This study examines Saudi Arabian middle school science textbooks' coverage of the essential features of scientific inquiry. All activities in the middle school science textbooks and workbooks were analyzed by using the scientific inquiry `essential features' rubric. The results indicated that the essential features are included in about 59 % of the analyzed science activities. However, feature 2, `making learner give priority to evidence in responding to questions' and feature 3, `allowing learner to formulate explanations from evidence' appeared more frequently than the other three features (feature 1: engaging learner in scientifically oriented questions, feature 4: helping learner connect explanations to scientific knowledge, and feature 5: helping learner communicate and justify explanations to others), whether in the activities as a whole, or in the activities included in each of the four science domains (physical science, Earth science, life science and chemistry). These features are represented in almost all activities. This means that almost all activities in the middle school science textbooks and the workbooks include features 2 and 3. Meanwhile, the mean level of inclusion of the five essential features of scientific inquiry found in the middle school science textbooks and workbooks as a whole is 2.55. However, results found for features 1, 4, 5 and for in-level inclusion of the inquiry features in each of the science domains indicate that the inclusion of the essential inquiry features is teacher-centred. As a result, neither science textbooks nor workbooks provide students with the opportunity or encouragement to develop their inquiry skills. Consequently, the results suggest important directions for educational administrators and policy-makers in the preparation and use of science educational content.

Inclusion of dosimetric data as covariates in toxicity-related radiogenomic studies : A systematic review.

PubMed

Yahya, Noorazrul; Chua, Xin-Jane; Manan, Hanani A; Ismail, Fuad

2018-05-17

This systematic review evaluates the completeness of dosimetric features and their inclusion as covariates in genetic-toxicity association studies. Original research studies associating genetic features and normal tissue complications following radiotherapy were identified from PubMed. The use of dosimetric data was determined by mining the statement of prescription dose, dose fractionation, target volume selection or arrangement and dose distribution. The consideration of the dosimetric data as covariates was based on the statement mentioned in the statistical analysis section. The significance of these covariates was extracted from the results section. Descriptive analyses were performed to determine their completeness and inclusion as covariates. A total of 174 studies were found to satisfy the inclusion criteria. Studies published ≥2010 showed increased use of dose distribution information (p = 0.07). 33% of studies did not include any dose features in the analysis of gene-toxicity associations. Only 29% included dose distribution features as covariates and reported the results. 59% of studies which included dose distribution features found significant associations to toxicity. A large proportion of studies on the correlation of genetic markers with radiotherapy-related side effects considered no dosimetric parameters. Significance of dose distribution features was found in more than half of the studies including these features, emphasizing their importance. Completeness of radiation-specific clinical data may have increased in recent years which may improve gene-toxicity association studies.

Explaining Andean Potato Weevils in Relation to Local and Landscape Features: A Facilitated Ecoinformatics Approach

PubMed Central

Parsa, Soroush; Ccanto, Raúl; Olivera, Edgar; Scurrah, María; Alcázar, Jesús; Rosenheim, Jay A.

2012-01-01

Background Pest impact on an agricultural field is jointly influenced by local and landscape features. Rarely, however, are these features studied together. The present study applies a “facilitated ecoinformatics” approach to jointly screen many local and landscape features of suspected importance to Andean potato weevils (Premnotrypes spp.), the most serious pests of potatoes in the high Andes. Methodology/Principal Findings We generated a comprehensive list of predictors of weevil damage, including both local and landscape features deemed important by farmers and researchers. To test their importance, we assembled an observational dataset measuring these features across 138 randomly-selected potato fields in Huancavelica, Peru. Data for local features were generated primarily by participating farmers who were trained to maintain records of their management operations. An information theoretic approach to modeling the data resulted in 131,071 models, the best of which explained 40.2–46.4% of the observed variance in infestations. The best model considering both local and landscape features strongly outperformed the best models considering them in isolation. Multi-model inferences confirmed many, but not all of the expected patterns, and suggested gaps in local knowledge for Andean potato weevils. The most important predictors were the field's perimeter-to-area ratio, the number of nearby potato storage units, the amount of potatoes planted in close proximity to the field, and the number of insecticide treatments made early in the season. Conclusions/Significance Results underscored the need to refine the timing of insecticide applications and to explore adjustments in potato hilling as potential control tactics for Andean weevils. We believe our study illustrates the potential of ecoinformatics research to help streamline IPM learning in agricultural learning collaboratives. PMID:22693551

Toolkits and Libraries for Deep Learning.

PubMed

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth

2017-08-01

Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

Visual observations over oceans

NASA Technical Reports Server (NTRS)

Terry, R. D.

1979-01-01

Important factors in locating, identifying, describing, and photographing ocean features from space are presented. On the basis of crew comments and other findings, the following recommendations can be made for Earth observations on Space Shuttle missions: (1) flyover exercises must include observations and photography of both temperate and tropical/subtropical waters; (2) sunglint must be included during some observations of ocean features; (3) imaging remote sensors should be used together with conventional photographic systems to document visual observations; (4) greater consideration must be given to scheduling earth observation targets likely to be obscured by clouds; and (5) an annotated photographic compilation of ocean features can be used as a training aid before the mission and as a reference book during space flight.

The role of experiential avoidance, psychopathology, and borderline personality features in experiencing positive emotions: a path analysis.

PubMed

Jacob, Gitta A; Ower, Nicole; Buchholz, Angela

2013-03-01

Experiential avoidance (EA) is an important factor in maintaining different forms of psychopathology including borderline personality pathology (BPD). So far little is known about the functions of EA, BPD features and general psychopathology for positive emotions. In this study we investigated three different anticipated pathways of their influence on positive emotions. A total of 334 subjects varying in general psychopathology &/or BPD features completed an online survey including self-ratings of BPD features, psychopathology, negative and positive emotions, and EA. Measures of positive emotions included both a general self-rating (PANAS) and emotional changes induced by two positive movie clips. Data were analyzed by means of path analysis. In comparing the three path models, one model was found clearly superior: In this model, EA acts as a mediator of the influence of psychopathology, BPD features, and negative emotions in the prediction of both measures of positive emotions. EA plays a central role in maintaining lack of positive emotions. Therapeutic implications and study limitations are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

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

Apte, A; Veeraraghavan, H; Oh, J

Purpose: To present an open source and free platform to facilitate radiomics research — The “Radiomics toolbox” in CERR. Method: There is scarcity of open source tools that support end-to-end modeling of image features to predict patient outcomes. The “Radiomics toolbox” strives to fill the need for such a software platform. The platform supports (1) import of various kinds of image modalities like CT, PET, MR, SPECT, US. (2) Contouring tools to delineate structures of interest. (3) Extraction and storage of image based features like 1st order statistics, gray-scale co-occurrence and zonesize matrix based texture features and shape features andmore » (4) Statistical Analysis. Statistical analysis of the extracted features is supported with basic functionality that includes univariate correlations, Kaplan-Meir curves and advanced functionality that includes feature reduction and multivariate modeling. The graphical user interface and the data management are performed with Matlab for the ease of development and readability of code and features for wide audience. Open-source software developed with other programming languages is integrated to enhance various components of this toolbox. For example: Java-based DCM4CHE for import of DICOM, R for statistical analysis. Results: The Radiomics toolbox will be distributed as an open source, GNU copyrighted software. The toolbox was prototyped for modeling Oropharyngeal PET dataset at MSKCC. The analysis will be presented in a separate paper. Conclusion: The Radiomics Toolbox provides an extensible platform for extracting and modeling image features. To emphasize new uses of CERR for radiomics and image-based research, we have changed the name from the “Computational Environment for Radiotherapy Research” to the “Computational Environment for Radiological Research”.« less

Applications of alignment-free methods in epigenomics.

PubMed

Pinello, Luca; Lo Bosco, Giosuè; Yuan, Guo-Cheng

2014-05-01

Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.

A novel feature extraction approach for microarray data based on multi-algorithm fusion

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277

A novel feature extraction approach for microarray data based on multi-algorithm fusion.

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.

EXFILE: A program for compiling irradiation data on UN and UC fuel pins

NASA Technical Reports Server (NTRS)

Mayer, J. T.; Smith, R. L.; Weinstein, M. B.; Davison, H. W.

1973-01-01

A FORTRAN-4 computer program for handling fuel pin data is described. Its main features include standardized output, easy access for data manipulation, and tabulation of important material property data. An additional feature allows simplified preparation of input decks for a fuel swelling computer code (CYGRO-2). Data from over 300 high temperature nitride and carbide based fuel pin irradiations are listed.

Additivity of Feature-Based and Symmetry-Based Grouping Effects in Multiple Object Tracking

PubMed Central

Wang, Chundi; Zhang, Xuemin; Li, Yongna; Lyu, Chuang

2016-01-01

Multiple object tracking (MOT) is an attentional process wherein people track several moving targets among several distractors. Symmetry, an important indicator of regularity, is a general spatial pattern observed in natural and artificial scenes. According to the “laws of perceptual organization” proposed by Gestalt psychologists, regularity is a principle of perceptual grouping, such as similarity and closure. A great deal of research reported that feature-based similarity grouping (e.g., grouping based on color, size, or shape) among targets in MOT tasks can improve tracking performance. However, no additive feature-based grouping effects have been reported where the tracking objects had two or more features. “Additive effect” refers to a greater grouping effect produced by grouping based on multiple cues instead of one cue. Can spatial symmetry produce a similar grouping effect similar to that of feature similarity in MOT tasks? Are the grouping effects based on symmetry and feature similarity additive? This study includes four experiments to address these questions. The results of Experiments 1 and 2 demonstrated the automatic symmetry-based grouping effects. More importantly, an additive grouping effect of symmetry and feature similarity was observed in Experiments 3 and 4. Our findings indicate that symmetry can produce an enhanced grouping effect in MOT and facilitate the grouping effect based on color or shape similarity. The “where” and “what” pathways might have played an important role in the additive grouping effect. PMID:27199875

Metacatalog of Planetary Surface Features for Multicriteria Evaluation of Surface Evolution: the Integrated Planetary Feature Database

NASA Astrophysics Data System (ADS)

Hargitai, Henrik

2016-10-01

We have created a metacatalog, or catalog or catalogs, of surface features of Mars that also includes the actual data in the catalogs listed. The goal is to make mesoscale surface feature databases available in one place, in a GIS-ready format. The databases can be directly imported to ArcGIS or other GIS platforms, like Google Mars. Some of the catalogs in our database are also ingested into the JMARS platform.All catalogs have been previously published in a peer-reviewed journal, but they may contain updates of the published catalogs. Many of the catalogs are "integrated", i.e. they merge databases or information from various papers on the same topic, including references to each individual features listed.Where available, we have included shapefiles with polygon or linear features, however, most of the catalogs only contain point data of their center points and morphological data.One of the unexpected results of the planetary feature metacatalog is that some features have been described by several papers, using different, i.e., conflicting designations. This shows the need for the development of an identification system suitable for mesoscale (100s m to km sized) features that tracks papers and thus prevents multiple naming of the same feature.The feature database can be used for multicriteria analysis of a terrain, thus enables easy distribution pattern analysis and the correlation of the distribution of different landforms and features on Mars. Such catalog makes a scientific evaluation of potential landing sites easier and more effective during the selection process and also supports automated landing site selections.The catalog is accessible at https://planetarydatabase.wordpress.com/.

Borrowed beauty? Understanding identity in Asian facial cosmetic surgery.

PubMed

Aquino, Yves Saint James; Steinkamp, Norbert

2016-09-01

This review aims to identify (1) sources of knowledge and (2) important themes of the ethical debate related to surgical alteration of facial features in East Asians. This article integrates narrative and systematic review methods. In March 2014, we searched databases including PubMed, Philosopher's Index, Web of Science, Sociological Abstracts, and Communication Abstracts using key terms "cosmetic surgery," "ethnic*," "ethics," "Asia*," and "Western*." The study included all types of papers written in English that discuss the debate on rhinoplasty and blepharoplasty in East Asians. No limit was put on date of publication. Combining both narrative and systematic review methods, a total of 31 articles were critically appraised on their contribution to ethical reflection founded on the debates regarding the surgical alteration of Asian features. Sources of knowledge were drawn from four main disciplines, including the humanities, medicine or surgery, communications, and economics. Focusing on cosmetic surgery perceived as a westernising practice, the key debate themes included authenticity of identity, interpersonal relationships and socio-economic utility in the context of Asian culture. The study shows how cosmetic surgery of ethnic features plays an important role in understanding female identity in the Asian context. Based on the debate themes authenticity of identity, interpersonal relationships, and socio-economic utility, this article argues that identity should be understood as less individualistic and more as relational and transformational in the Asian context. In addition, this article also proposes to consider cosmetic surgery of Asian features as an interplay of cultural imperialism and cultural nationalism, which can both be a source of social pressure to modify one's appearance.

Patient Preferences for Features of Health Care Delivery Systems: A Discrete Choice Experiment.

PubMed

Mühlbacher, Axel C; Bethge, Susanne; Reed, Shelby D; Schulman, Kevin A

2016-04-01

To estimate the relative importance of organizational-, procedural-, and interpersonal-level features of health care delivery systems from the patient perspective. We designed four discrete choice experiments (DCEs) to measure patient preferences for 21 health system attributes. Participants were recruited through the online patient portal of a large health system. We analyzed the DCE data using random effects logit models. DCEs were performed in which respondents were provided with descriptions of alternative scenarios and asked to indicate which scenario they prefer. Respondents were randomly assigned to one of the three possible health scenarios (current health, new lung cancer diagnosis, or diabetes) and asked to complete 15 choice tasks. Each choice task included an annual out-of-pocket cost attribute. A total of 3,900 respondents completed the survey. The out-of-pocket cost attribute was considered the most important across the four different DCEs. Following the cost attribute, trust and respect, multidisciplinary care, and shared decision making were judged as most important. The relative importance of out-of-pocket cost was consistently lower in the hypothetical context of a new lung cancer diagnosis compared with diabetes or the patient's current health. This study demonstrates the complexity of patient decision making processes regarding features of health care delivery systems. Our findings suggest the importance of these features may change as a function of an individual's medical conditions. © Health Research and Educational Trust.

Remote Sensing in Archaeology: Visible Temporal Change of Archaeological Features of the Peten, Guatemala

NASA Technical Reports Server (NTRS)

Lowry, James D., Jr.

1999-01-01

The purpose of this archaeological research was two-fold; the location of Mayan sites and features in order to learn more of this cultural group, and the (cultural) preservation of these sites and features for the future using Landsat Thematic Mapper (TM) images. Because the rainy season, traditionally at least, lasts about six months (about June to December), the time of year the image is acquired plays an important role in spectral reflectance. Images from 1986, 1995, and 1997 were selected because it was felt they would provide the best opportunity for success in layering different bands from different years together to attempt to see features not completely visible in any one year. False-color composites were created including bands 3, 4, and 5 using a mixture of years and bands. One particular combination that yielded tremendously interesting results included band 5 from 1997, band 4 from 1995, and band 3 from 1986. A number of straight linear features (probably Mayan causeways) run through the bajos that Dr. Sever believes are features previously undiscovered. At this point, early indications are that this will be a successful method for locating "new" Mayan archaeological features in the Peten.

Anxiety or agitation in mood disorder with mixed features: A review with a focus on validity as a dimensional criterion.

PubMed

Shim, In Hee; Bae, Dong Sik; Bahk, Won-Myong

2016-08-01

The diagnostic validity of mixed features, excluding anxiety or psychomotor agitation in mood disorders, has not yet been fully examined. PubMed and relevant English-language literature (regardless of year) were searched. Keywords were mixed or mixed state or mixed features or mixed episode and anxious or anxiety or agitation and bipolar disorder or depressive disorder or mood disorder or affective disorder. Most studies on anxiety or psychomotor agitation have included a significant correlation relevant to the "with mixed features" specifier, although it is common in both poles of mood episodes regardless of the predominant polarity. There is some confusion between the characteristic of classical mixed states and the definition of the mixed features specifier with the newly added anxious distress specifier in DSM-5, specifically, whether to include anxiety and agitation as significant characteristics. This change is of concern because a large proportion of patients with mixed features are now unspecified, and this may influence treatment planning and prognosis. The findings of our review suggest that anxiety and psychomotor agitation can be core symptoms in mood episodes with mixed features and important clinical clues for prediction of treatment effects and disease course.

Adolescent Schizophrenia.

ERIC Educational Resources Information Center

Bauer, William; Bauer, Janet L.

1982-01-01

Describes the characteristics of adolescent schizophrenia, which is often confused with other conditions. Symptoms may include affective disturbance, autism, thought disorder, and ambivalence. Clinical features of the illness are discussed and the importance of early treatment is emphasized. (JAC)

Mud Volcanoes - A New Class of Sites for Geological and Astrobiological Exploration of Mars

NASA Technical Reports Server (NTRS)

Allen, C.C.; Oehler, D.Z.; Baker, D.M.

2009-01-01

Mud volcanoes provide a unique low-temperature window into the Earth s subsurface - including the deep biosphere - and may prove to be significant sources of atmospheric methane. The identification of analogous features on Mars would provide an important new class of sites for geological and astrobiological exploration. We report new work suggesting that features in Acidalia Planitia are most consistent with their being mud volcanoes.

Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST-Indel).

PubMed

Douville, Christopher; Masica, David L; Stenson, Peter D; Cooper, David N; Gygax, Derek M; Kim, Rick; Ryan, Michael; Karchin, Rachel

2016-01-01

Insertion/deletion variants (indels) alter protein sequence and length, yet are highly prevalent in healthy populations, presenting a challenge to bioinformatics classifiers. Commonly used features--DNA and protein sequence conservation, indel length, and occurrence in repeat regions--are useful for inference of protein damage. However, these features can cause false positives when predicting the impact of indels on disease. Existing methods for indel classification suffer from low specificities, severely limiting clinical utility. Here, we further develop our variant effect scoring tool (VEST) to include the classification of in-frame and frameshift indels (VEST-indel) as pathogenic or benign. We apply 24 features, including a new "PubMed" feature, to estimate a gene's importance in human disease. When compared with four existing indel classifiers, our method achieves a drastically reduced false-positive rate, improving specificity by as much as 90%. This approach of estimating gene importance might be generally applicable to missense and other bioinformatics pathogenicity predictors, which often fail to achieve high specificity. Finally, we tested all possible meta-predictors that can be obtained from combining the four different indel classifiers using Boolean conjunctions and disjunctions, and derived a meta-predictor with improved performance over any individual method. © 2015 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

Combining landscape variables and species traits can improve the utility of climate change vulnerability assessments

USGS Publications Warehouse

Nadeau, Christopher P.; Fuller, Angela K.

2016-01-01

Conservation organizations worldwide are investing in climate change vulnerability assessments. Most vulnerability assessment methods focus on either landscape features or species traits that can affect a species vulnerability to climate change. However, landscape features and species traits likely interact to affect vulnerability. We compare a landscape-based assessment, a trait-based assessment, and an assessment that combines landscape variables and species traits for 113 species of birds, herpetofauna, and mammals in the northeastern United States. Our aim is to better understand which species traits and landscape variables have the largest influence on assessment results and which types of vulnerability assessments are most useful for different objectives. Species traits were most important for determining which species will be most vulnerable to climate change. The sensitivity of species to dispersal barriers and the species average natal dispersal distance were the most important traits. Landscape features were most important for determining where species will be most vulnerable because species were most vulnerable in areas where multiple landscape features combined to increase vulnerability, regardless of species traits. The interaction between landscape variables and species traits was important when determining how to reduce climate change vulnerability. For example, an assessment that combines information on landscape connectivity, climate change velocity, and natal dispersal distance suggests that increasing landscape connectivity may not reduce the vulnerability of many species. Assessments that include landscape features and species traits will likely be most useful in guiding conservation under climate change.

Polycystic ovary syndrome: a complex condition with psychological, reproductive and metabolic manifestations that impacts on health across the lifespan.

PubMed

Teede, H; Deeks, A; Moran, L

2010-06-30

Polycystic ovary syndrome (PCOS) is of clinical and public health importance as it is very common, affecting up to one in five women of reproductive age. It has significant and diverse clinical implications including reproductive (infertility, hyperandrogenism, hirsutism), metabolic (insulin resistance, impaired glucose tolerance, type 2 diabetes mellitus, adverse cardiovascular risk profiles) and psychological features (increased anxiety, depression and worsened quality of life). Polycystic ovary syndrome is a heterogeneous condition and, as such, clinical and research agendas are broad and involve many disciplines. The phenotype varies widely depending on life stage, genotype, ethnicity and environmental factors including lifestyle and bodyweight. Importantly, PCOS has unique interactions with the ever increasing obesity prevalence worldwide as obesity-induced insulin resistance significantly exacerbates all the features of PCOS. Furthermore, it has clinical implications across the lifespan and is relevant to related family members with an increased risk for metabolic conditions reported in first-degree relatives. Therapy should focus on both the short and long-term reproductive, metabolic and psychological features. Given the aetiological role of insulin resistance and the impact of obesity on both hyperinsulinaemia and hyperandrogenism, multidisciplinary lifestyle improvement aimed at normalising insulin resistance, improving androgen status and aiding weight management is recognised as a crucial initial treatment strategy. Modest weight loss of 5% to 10% of initial body weight has been demonstrated to improve many of the features of PCOS. Management should focus on support, education, addressing psychological factors and strongly emphasising healthy lifestyle with targeted medical therapy as required. Monitoring and management of long-term metabolic complications is also an important part of routine clinical care. Comprehensive evidence-based guidelines are needed to aid early diagnosis, appropriate investigation, regular screening and treatment of this common condition. Whilst reproductive features of PCOS are well recognised and are covered here, this review focuses primarily on the less appreciated cardiometabolic and psychological features of PCOS.

Polycystic ovary syndrome: a complex condition with psychological, reproductive and metabolic manifestations that impacts on health across the lifespan

PubMed Central

2010-01-01

Polycystic ovary syndrome (PCOS) is of clinical and public health importance as it is very common, affecting up to one in five women of reproductive age. It has significant and diverse clinical implications including reproductive (infertility, hyperandrogenism, hirsutism), metabolic (insulin resistance, impaired glucose tolerance, type 2 diabetes mellitus, adverse cardiovascular risk profiles) and psychological features (increased anxiety, depression and worsened quality of life). Polycystic ovary syndrome is a heterogeneous condition and, as such, clinical and research agendas are broad and involve many disciplines. The phenotype varies widely depending on life stage, genotype, ethnicity and environmental factors including lifestyle and bodyweight. Importantly, PCOS has unique interactions with the ever increasing obesity prevalence worldwide as obesity-induced insulin resistance significantly exacerbates all the features of PCOS. Furthermore, it has clinical implications across the lifespan and is relevant to related family members with an increased risk for metabolic conditions reported in first-degree relatives. Therapy should focus on both the short and long-term reproductive, metabolic and psychological features. Given the aetiological role of insulin resistance and the impact of obesity on both hyperinsulinaemia and hyperandrogenism, multidisciplinary lifestyle improvement aimed at normalising insulin resistance, improving androgen status and aiding weight management is recognised as a crucial initial treatment strategy. Modest weight loss of 5% to 10% of initial body weight has been demonstrated to improve many of the features of PCOS. Management should focus on support, education, addressing psychological factors and strongly emphasising healthy lifestyle with targeted medical therapy as required. Monitoring and management of long-term metabolic complications is also an important part of routine clinical care. Comprehensive evidence-based guidelines are needed to aid early diagnosis, appropriate investigation, regular screening and treatment of this common condition. Whilst reproductive features of PCOS are well recognised and are covered here, this review focuses primarily on the less appreciated cardiometabolic and psychological features of PCOS. PMID:20591140

An interactive portal to empower cancer survivors: a qualitative study on user expectations.

PubMed

Kuijpers, Wilma; Groen, Wim G; Loos, Romy; Oldenburg, Hester S A; Wouters, Michel W J M; Aaronson, Neil K; van Harten, Wim H

2015-09-01

Portals are increasingly used to improve patient empowerment, but are still uncommon in oncology. In this study, we explored cancer survivors' and health professionals' expectations of possible features of an interactive portal. We conducted three focus groups with breast cancer survivors (n = 21), two with lung cancer survivors (n = 14), and four with health professionals (n = 31). Drafts of possible features of an interactive portal were presented as static screenshots: survivorship care plan (SCP), access to electronic medical record (EMR), appointments, e-consultation, online patient community, patient reported outcomes (PROs) plus feedback, telemonitoring service, online rehabilitation program, and online psychosocial self-management program. This presentation was followed by an open discussion. Focus groups were audiotaped, transcribed verbatim, and data were analyzed using content analysis. Important themes included fulfillment of information needs, communication, motivation, quality of feedback, and supervision. Cancer survivors were primarily interested in features that could fulfill their information needs: SCP, access to their EMR, and an overview of appointments. Health professionals considered PROs and telemonitoring as most useful features, as these provide relevant information about survivors' health status. We recommend to minimally include these features in an interactive portal for cancer survivors. This is the first study that evaluated the expectations of cancer survivors and health professionals concerning an interactive portal. Both groups were positive about the introduction of such a portal, although their preferences for the various features differed. These findings reflect their unique perspective and emphasize the importance of involving multiple stakeholders in the actual design process.

Appearance-based human gesture recognition using multimodal features for human computer interaction

NASA Astrophysics Data System (ADS)

Luo, Dan; Gao, Hua; Ekenel, Hazim Kemal; Ohya, Jun

2011-03-01

The use of gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present an appearance-based multimodal gesture recognition framework, which combines the different groups of features such as facial expression features and hand motion features which are extracted from image frames captured by a single web camera. We refer 12 classes of human gestures with facial expression including neutral, negative and positive meanings from American Sign Languages (ASL). We combine the features in two levels by employing two fusion strategies. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, and LDA is used to choose the most discriminative elements by projecting the feature on a discriminative expression space. The second strategy is applied on decision level. Weighted decisions from single modalities are fused in a later stage. A condensation-based algorithm is adopted for classification. We collected a data set with three to seven recording sessions and conducted experiments with the combination techniques. Experimental results showed that facial analysis improve hand gesture recognition, decision level fusion performs better than feature level fusion.

Advancement of Latent Trait Theory.

DTIC Science & Technology

1988-02-01

if I am the principal investigator, I find it practically impossible to include and systematize all the important findings and implications within a...methods are described in [1.21. Two important features of the principal investigator’s approach are the following. (1) It does not assume any specific...were described in the preceding chapter, the maximum likelihood estimate 0 of ability 0 , and also f of the transformed ability r play important roles

Giving Back: Outstanding Alumni Stress the Importance of Community and Public Service

ERIC Educational Resources Information Center

Ullman, Ellen

2010-01-01

This article features several community college alumni who share how community colleges contributed to their success later in their lives and how they are inspired to give back. These outstanding alumni stress the importance of community and public service. They include: (1) Dr. Richard Carmona, U.S. Surgeon General from 2002 to 2006; (2) Colonel…

Chain Reaction Polymerization.

ERIC Educational Resources Information Center

McGrath, James E.

1981-01-01

The salient features and importance of chain-reaction polymerization are discussed, including such topics as the thermodynamics of polymerization, free-radical polymerization kinetics, radical polymerization processes, copolymers, and free-radical chain, anionic, cationic, coordination, and ring-opening polymerizations. (JN)

A concentrated parameter model for the human cardiovascular system including heart valve dynamics and atrioventricular interaction.

PubMed

Korakianitis, Theodosios; Shi, Yubing

2006-09-01

Numerical modeling of the human cardiovascular system has always been an active research direction since the 19th century. In the past, various simulation models of different complexities were proposed for different research purposes. In this paper, an improved numerical model to study the dynamic function of the human circulation system is proposed. In the development of the mathematical model, the heart chambers are described with a variable elastance model. The systemic and pulmonary loops are described based on the resistance-compliance-inertia concept by considering local effects of flow friction, elasticity of blood vessels and inertia of blood in different segments of the blood vessels. As an advancement from previous models, heart valve dynamics and atrioventricular interaction, including atrial contraction and motion of the annulus fibrosus, are specifically modeled. With these improvements the developed model can predict several important features that were missing in previous numerical models, including regurgitant flow on heart valve closure, the value of E/A velocity ratio in mitral flow, the motion of the annulus fibrosus (called the KG diaphragm pumping action), etc. These features have important clinical meaning and their changes are often related to cardiovascular diseases. Successful simulation of these features enhances the accuracy of simulations of cardiovascular dynamics, and helps in clinical studies of cardiac function.

Periglacial and glacial analogs for Martian landforms

NASA Technical Reports Server (NTRS)

Rossbacher, Lisa A.

1992-01-01

The list of useful terrestrial analogs for Martian landforms has been expanded to include: features developed by desiccation processes; catastrophic flood features associated with boulder-sized materials; and sorted ground developed at a density boundary. Quantitative analytical techniques developed for physical geography have been adapted and applied to planetary studies, including: quantification of the patterns of polygonally fractured ground to describe pattern randomness independent of pattern size, with possible correlation to the mechanism of origin and quantification of the relative area of a geomorphic feature or region in comparison to planetary scale. Information about Martian geomorphology studied in this project was presented at professional meetings world-wide, at seven colleges and universities, in two interactive televised courses, and as part of two books. Overall, this project has expanded the understanding of the range of terrestrial analogs for Martian landforms, including identifying several new analogs. The processes that created these terrestrial features are characterized by both cold temperatures and low humidity, and therefore both freeze-thaw and desiccation processes are important. All these results support the conclusion that water has played a significant role in the geomorphic history of Mars.

Spatial features register: toward standardization of spatial features

USGS Publications Warehouse

Cascio, Janette

1994-01-01

As the need to share spatial data increases, more than agreement on a common format is needed to ensure that the data is meaningful to both the importer and the exporter. Effective data transfer also requires common definitions of spatial features. To achieve this, part 2 of the Spatial Data Transfer Standard (SDTS) provides a model for a spatial features data content specification and a glossary of features and attributes that fit this model. The model provides a foundation for standardizing spatial features. The glossary now contains only a limited subset of hydrographic and topographic features. For it to be useful, terms and definitions must be included for other categories, such as base cartographic, bathymetric, cadastral, cultural and demographic, geodetic, geologic, ground transportation, international boundaries, soils, vegetation, water, and wetlands, and the set of hydrographic and topographic features must be expanded. This paper will review the philosophy of the SDTS part 2 and the current plans for creating a national spatial features register as one mechanism for maintaining part 2.

The Application of Context Theory in English Teaching of Reading

ERIC Educational Resources Information Center

Zhu, Jiang; Han, Lemeng

2010-01-01

Context theory is a very important theory in English teaching, especially the teaching of reading. This paper first analyzes the theory of context, including the features of context and some principles in context theory. Then the paper discusses the application of context theory in English teaching of reading, including some problems met in…

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis.

PubMed

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo 18 were used,which included 200 healthy Brazilians of both genders. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.

VoIP Accessibility: A Usability Study of Voice over Internet Protocol (VoIP) Systems and A Survey of VoIP Users with Vision Loss

ERIC Educational Resources Information Center

Packer, Jaclyn; Reuschel, William

2018-01-01

Introduction: Accessibility of Voice over Internet Protocol (VoIP) systems was tested with a hands-on usability study and an online survey of VoIP users who are visually impaired. The survey examined the importance of common VoIP features, and both methods assessed difficulty in using those features. Methods: The usability test included four paid…

QA4, a language for artificial intelligence.

NASA Technical Reports Server (NTRS)

Derksen, J. A. C.

1973-01-01

Introduction of a language for problem solving and specifically robot planning, program verification, and synthesis and theorem proving. This language, called question-answerer 4 (QA4), embodies many features that have been found useful for constructing problem solvers but have to be programmed explicitly by the user of a conventional language. The most important features of QA4 are described, and examples are provided for most of the material introduced. Language features include backtracking, parallel processing, pattern matching, set manipulation, and pattern-triggered function activation. The language is most convenient for use in an interactive way and has extensive trace and edit facilities.

A virtual microscope for academic medical education: the pate project.

PubMed

Brochhausen, Christoph; Winther, Hinrich B; Hundt, Christian; Schmitt, Volker H; Schömer, Elmar; Kirkpatrick, C James

2015-05-11

Whole-slide imaging (WSI) has become more prominent and continues to gain in importance in student teaching. Applications with different scope have been developed. Many of these applications have either technical or design shortcomings. To design a survey to determine student expectations of WSI applications for teaching histological and pathological diagnosis. To develop a new WSI application based on the findings of the survey. A total of 216 students were questioned about their experiences and expectations of WSI applications, as well as favorable and undesired features. The survey included 14 multiple choice and two essay questions. Based on the survey, we developed a new WSI application called Pate utilizing open source technologies. The survey sample included 216 students-62.0% (134) women and 36.1% (78) men. Out of 216 students, 4 (1.9%) did not disclose their gender. The best-known preexisting WSI applications included Mainzer Histo Maps (199/216, 92.1%), Histoweb Tübingen (16/216, 7.4%), and Histonet Ulm (8/216, 3.7%). Desired features for the students were latitude in the slides (190/216, 88.0%), histological (191/216, 88.4%) and pathological (186/216, 86.1%) annotations, points of interest (181/216, 83.8%), background information (146/216, 67.6%), and auxiliary informational texts (113/216, 52.3%). By contrast, a discussion forum was far less important (9/216, 4.2%) for the students. The survey revealed that the students appreciate a rich feature set, including WSI functionality, points of interest, auxiliary informational texts, and annotations. The development of Pate was significantly influenced by the findings of the survey. Although Pate currently has some issues with the Zoomify file format, it could be shown that Web technologies are capable of providing a high-performance WSI experience, as well as a rich feature set.

The mental representation of living and nonliving things: differential weighting and interactivity of sensorial and non-sensorial features.

PubMed

Ventura, Paulo; Morais, José; Brito-Mendes, Carlos; Kolinsky, Régine

2005-02-01

Warrington and colleagues (Warrington & McCarthy, 1983, 1987; Warrington & Shallice, 1984) claimed that sensorial and functional-associative (FA) features are differentially important in determining the meaning of living things (LT) and nonliving things (NLT). The first aim of the present study was to evaluate this hypothesis through two different access tasks: feature generation (Experiment 1) and cued recall (Experiment 2). The results of both experiments provided consistent empirical support for Warrington and colleagues' assumption. The second aim of the present study was to test a new differential interactivity hypothesis that combines Warrington and colleagueS' assumption with the notion of a higher number of intercorrelations and hence of a stronger connectivity between sensorial and non-sensorial features for LTs than for NLTs. This hypothesis was motivated by previoUs reports of an uncrossed interaction between domain (LTs vs NLTs) and attribute type (sensorial vs FA) in, for example, a feature verification task (Laws, Humber, Ramsey, & McCarthy, 1995): while FA attributes are verified faster than sensorial attributes for NLTs, no difference is observed for LTs. We replicated and generalised this finding using several feature verification tasks on both written words and pictures (Experiment 3), including in conditions aimed at minimising the intervention of priming biases and strategic or mnemonic processes (Experiment 4). The whole set of results suggests that both privileged relations between features and categories, and the differential importance of intercorrelations between features as a function of category, modulate access to semantic features.

Implementation of aerial LiDAR technology to update highway feature inventory.

DOT National Transportation Integrated Search

2016-12-01

Highway assets, including traffic signs, traffic signals, light poles, and guardrails, are important components of : transportation networks. They guide, warn and protect drivers, and regulate traffic. To manage and maintain the : regular operation o...

Monitoring Blood Sugar: The Importance of Checking Blood Sugar Levels

MedlinePlus

... more portable sizes. Other features may include memory storage and the ability to record other information like ... for ketones , chemicals that show up in the urine (pee) and blood after the body breaks down ...

Selecting a digital camera for telemedicine.

PubMed

Patricoski, Chris; Ferguson, A Stewart

2009-06-01

The digital camera is an essential component of store-and-forward telemedicine (electronic consultation). There are numerous makes and models of digital cameras on the market, and selecting a suitable consumer-grade camera can be complicated. Evaluation of digital cameras includes investigating the features and analyzing image quality. Important features include the camera settings, ease of use, macro capabilities, method of image transfer, and power recharging. Consideration needs to be given to image quality, especially as it relates to color (skin tones) and detail. It is important to know the level of the photographer and the intended application. The goal is to match the characteristics of the camera with the telemedicine program requirements. In the end, selecting a digital camera is a combination of qualitative (subjective) and quantitative (objective) analysis. For the telemedicine program in Alaska in 2008, the camera evaluation and decision process resulted in a specific selection based on the criteria developed for our environment.

Varied Rates of Implementation of Patient-Centered Medical Home Features and Residents' Perceptions of Their Importance Based on Practice Experience.

PubMed

Eiff, M Patrice; Green, Larry A; Jones, Geoff; Devlaeminck, Alex Verdieck; Waller, Elaine; Dexter, Eve; Marino, Miguel; Carney, Patricia A

2017-03-01

Little is known about how the patient-centered medical home (PCMH) is being implemented in residency practices. We describe both the trends in implementation of PCMH features and the influence that working with PCMH features has on resident attitudes toward their importance in 14 family medicine residencies associated with the P4 Project. We assessed 24 residency continuity clinics annually between 2007-2011 on presence or absence of PCMH features. Annual resident surveys (n=690) assessed perceptions of importance of PCMH features using a 4-point scale (not at all important to very important). We used generalized estimating equations logistic regression to assess trends and ordinal-response proportional odds regression models to determine if resident ratings of importance were associated with working with those features during training. Implementation of electronic health record (EHR) features increased significantly from 2007-2011, such as email communication with patients (33% to 67%), preventive services registries (23% to 64%), chronic disease registries (63% to 82%), and population-based quality assurance (46% to 79%). Team-based care was the only process of care feature to change significantly (54% to 93%). Residents with any exposure to EHR-based features had higher odds of rating the features more important compared to those with no exposure. We observed consistently lower odds of the resident rating process of care features as more important with any exposure compared to no exposure. Residencies engaged in educational transformation were more successful in implementing EHR-based PCMH features, and exposure during training appears to positively influence resident ratings of importance, while exposure to process of care features are slower to implement with less influence on importance ratings.

Urban topography for flood modeling by fusion of OpenStreetMap, SRTM and local knowledge

NASA Astrophysics Data System (ADS)

Winsemius, Hessel; Donchyts, Gennadii; Eilander, Dirk; Chen, Jorik; Leskens, Anne; Coughlan, Erin; Mawanda, Shaban; Ward, Philip; Diaz Loaiza, Andres; Luo, Tianyi; Iceland, Charles

2016-04-01

Topography data is essential for understanding and modeling of urban flood hazard. Within urban areas, much of the topography is defined by highly localized man-made features such as roads, channels, ditches, culverts and buildings. This results in the requirement that urban flood models require high resolution topography, and water conveying connections within the topography are considered. In recent years, more and more topography information is collected through LIDAR surveys however there are still many cities in the world where high resolution topography data is not available. Furthermore, information on connectivity is required for flood modelling, even when LIDAR data are used. In this contribution, we demonstrate how high resolution terrain data can be synthesized using a fusion between features in OpenStreetMap (OSM) data (including roads, culverts, channels and buildings) and existing low resolution and noisy SRTM elevation data using the Google Earth Engine platform. Our method uses typical existing OSM properties to estimate heights and topology associated with the features, and uses these to correct noise and burn features on top of the existing low resolution SRTM elevation data. The method has been setup in the Google Earth Engine platform so that local stakeholders and mapping teams can on-the-fly propose, include and visualize the effect of additional features and properties of features, which are deemed important for topography and water conveyance. These features can be included in a workshop environment. We pilot our tool over Dar Es Salaam.

Application of musical timbre discrimination features to active sonar classification

NASA Astrophysics Data System (ADS)

Young, Victor W.; Hines, Paul C.; Pecknold, Sean

2005-04-01

In musical acoustics significant effort has been devoted to uncovering the physical basis of timbre perception. Most investigations into timbre rely on multidimensional scaling (MDS), in which different musical sounds are arranged as points in multidimensional space. The Euclidean distance between points corresponds to the perceptual distance between sounds and the multidimensional axes are linked to measurable properties of the sounds. MDS has identified numerous temporal and spectral features believed to be important to timbre perception. There is reason to believe that some of these features may have wider application in the disparate field of underwater acoustics, since anecdotal evidence suggests active sonar returns from metallic objects sound different than natural clutter returns when auralized by human operators. This is particularly encouraging since attempts to develop robust automatic classifiers capable of target-clutter discrimination over a wide range of operational conditions have met with limited success. Spectral features relevant to target-clutter discrimination are believed to include click-pitch and envelope irregularity; relevant temporal features are believed to include duration, sub-band attack/decay time, and time separation pitch. Preliminary results from an investigation into the role of these timbre features in target-clutter discrimination will be presented. [Work supported by NSERC and GDC.

A Thermodynamically General Theory for Convective Circulations and Vortices

NASA Astrophysics Data System (ADS)

Renno, N. O.

2007-12-01

Convective circulations and vortices are common features of atmospheres that absorb low-entropy-energy at higher temperatures than they reject high-entropy-energy to space. These circulations range from small to planetary-scale and play an important role in the vertical transport of heat, momentum, and tracer species. Thus, the development of theoretical models for convective phenomena is important to our understanding of many basic features of planetary atmospheres. A thermodynamically general theory for convective circulations and vortices is proposed. The theory includes irreversible processes and quantifies the pressure drop between the environment and any point in a convective updraft. The article's main result is that the proposed theory provides an expression for the pressure drop along streamlines or streamtubes that is a generalization of Bernoulli's equation to convective circulations. We speculate that the proposed theory not only explains the intensity, but also shed light on other basic features of convective circulations and vortices.

The future of primordial features with large-scale structure surveys

NASA Astrophysics Data System (ADS)

Chen, Xingang; Dvorkin, Cora; Huang, Zhiqi; Namjoo, Mohammad Hossein; Verde, Licia

2016-11-01

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopic and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.

The analysis of image feature robustness using cometcloud

PubMed Central

Qi, Xin; Kim, Hyunjoo; Xing, Fuyong; Parashar, Manish; Foran, David J.; Yang, Lin

2012-01-01

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval. PMID:23248759

The future of primordial features with large-scale structure surveys

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

Chen, Xingang; Namjoo, Mohammad Hossein; Dvorkin, Cora

2016-11-01

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopicmore » and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.« less

Optimal faces for gender and expression: a new technique for measuring dynamic templates used in face perception.

PubMed

Poirier, Frédéric J A M; Faubert, Jocelyn

2012-06-22

Facial expressions are important for human communications. Face perception studies often measure the impact of major degradation (e.g., noise, inversion, short presentations, masking, alterations) on natural expression recognition performance. Here, we introduce a novel face perception technique using rich and undegraded stimuli. Participants modified faces to create optimal representations of given expressions. Using sliders, participants adjusted 53 face components (including 37 dynamic) including head, eye, eyebrows, mouth, and nose shape and position. Data was collected from six participants and 10 conditions (six emotions + pain + gender + neutral). Some expressions had unique features (e.g., frown for anger, upward-curved mouth for happiness), whereas others had shared features (e.g., open eyes and mouth for surprise and fear). Happiness was different from other emotions. Surprise was different from other emotions except fear. Weighted sum morphing provides acceptable stimuli for gender-neutral and dynamic stimuli. Many features were correlated, including (1) head size with internal feature sizes as related to gender, (2) internal feature scaling, and (3) eyebrow height and eye openness as related to surprise and fear. These findings demonstrate the method's validity for measuring the optimal facial expressions, which we argue is a more direct measure of their internal representations.

Observations on morphology of immature Lucilia porphyrina (Diptera: Calliphoridae), a fly species of forensic importance.

PubMed

Klong-Klaew, Tunwadee; Sukontason, Kom; Sribanditmongkol, Pongruk; Moophayak, Kittikhun; Sanit, Sangob; Sukontason, Kabkaew L

2012-11-01

Lucilia porphyrina (Walker) is a blow fly of forensic importance, and shares its geographical distribution with a related forensically important species, Lucilia cuprina (Wiedemann). The immature stages of both species are similar in general appearance; therefore, correct identification should be given special consideration. This study highlighted the main features of L. porphyrina larvae, as observed under light microscopy and scanning electron microscopy. Particular attention is given to the anterior and posterior spiracles, cephalopharyngeal skeleton, and characteristics of the dorsal spines between the prothorax and mesothorax. In the third instar specifically, morphological information on L. porphyrina showed several features that are shared by L. cuprina, and therefore need certain identification to distinguish between them. Such key features are (1) greater posterior spiracle, (2) apparent inner projection between the middle and lower slits of the posterior spiracle, and (3) strongly sclerotized peritreme. The number of papillae on the anterior spiracle may be a supplement, five to nine and three to six in L. porphyrina and L. cuprina, respectively. The key for identifying third instar of forensically important flies in Thailand has been updated to include L. porphyrina.

Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions.

PubMed

Kolchinsky, A; Lourenço, A; Li, L; Rocha, L M

2013-01-01

Drug-drug interaction (DDI) is a major cause of morbidity and mortality. DDI research includes the study of different aspects of drug interactions, from in vitro pharmacology, which deals with drug interaction mechanisms, to pharmaco-epidemiology, which investigates the effects of DDI on drug efficacy and adverse drug reactions. Biomedical literature mining can aid both kinds of approaches by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper dimensionality reduction had a large impact on performance. Finally, the inclusion of NER features and dictionaries was found not to help classification.

Clinical decision support provided within physician order entry systems: a systematic review of features effective for changing clinician behavior.

PubMed

Kawamoto, Kensaku; Lobach, David F

2003-01-01

Computerized physician order entry (CPOE) systems represent an important tool for providing clinical decision support. In undertaking this systematic review, our objective was to identify the features of CPOE-based clinical decision support systems (CDSSs) most effective at modifying clinician behavior. For this review, two independent reviewers systematically identified randomized controlled trials that evaluated the effectiveness of CPOE-based CDSSs in changing clinician behavior. Furthermore, each included study was assessed for the presence of 14 CDSS features. We screened 10,023 citations and included 11 studies. Of the 10 studies comparing a CPOE-based CDSS intervention against a non-CDSS control group, 7 reported a significant desired change in professional practice. Moreover, meta-regression analysis revealed that automatic provision of the decision support was strongly associated with improved professional practice (adjusted odds ratio, 23.72; 95% confidence interval, 1.75-infiniti). Thus, we conclude that automatic provision of decision support is a critical feature of successful CPOE-based CDSS interventions.

Agriculture and Karst in Kentucky

USDA-ARS?s Scientific Manuscript database

This publication describes the unique hydrologic and environmental issues found in karst environments. The publication describes karst landscapes, the importance of karst, different types of karst features, and how water moves through karst landscapes. The publication includes details on methods for...

The Gene Scene.

ERIC Educational Resources Information Center

World Wildlife Fund, Washington, DC.

This document features a lesson plan in which genetic traits are identified and classified using a genetic wheel by playing several different games that introduce genetic diversity and highlight why it is important within populations. Samples of instruction and assessment are included. (KHR)

IMPERVIOUS SURFACE RESEARCH IN THE MID-ATLANTIC

EPA Science Inventory

Anthropogenic impervious surfaces have an important relationship with non-point source pollution (NPS) in urban watersheds. These human-created surfaces include such features as roads, parking lots, rooftops, sidewalks, and driveways. The amount of impervious surface area in a ...

Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

PubMed

Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

2013-03-01

Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Using different classification models in wheat grading utilizing visual features

NASA Astrophysics Data System (ADS)

Basati, Zahra; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-04-01

Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively

Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations

PubMed Central

Zheng, Jiaping; Yu, Hong

2016-01-01

Background Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients’ notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. Objective We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. Methods First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians’ agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Results Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen’s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P<.001). Rich learning features contributed to FOCUS’s performance substantially. Conclusions FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care. PMID:27903489

Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations.

PubMed

Chen, Jinying; Zheng, Jiaping; Yu, Hong

2016-11-30

Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients' notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians' agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen's kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P<.001). Rich learning features contributed to FOCUS's performance substantially. FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care. ©Jinying Chen, Jiaping Zheng, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 30.11.2016.

Recognition and classification of colon cells applying the ensemble of classifiers.

PubMed

Kruk, M; Osowski, S; Koktysz, R

2009-02-01

The paper presents the application of an ensemble of classifiers for the recognition of colon cells on the basis of the microscope colon image. The solved task include: segmentation of the individual cells from the image using the morphological operations, the preprocessing stages, leading to the extraction of features, selection of the most important features, and the classification stage applying the classifiers arranged in the form of ensemble. The paper presents and discusses the results concerning the recognition of four most important colon cell types: eosinophylic granulocyte, neutrophilic granulocyte, lymphocyte and plasmocyte. The proposed system is able to recognize the cells with the accuracy comparable to the human expert (around 5% of discrepancy of both results).

High speed micromachining with high power UV laser

NASA Astrophysics Data System (ADS)

Patel, Rajesh S.; Bovatsek, James M.

2013-03-01

Increasing demand for creating fine features with high accuracy in manufacturing of electronic mobile devices has fueled growth for lasers in manufacturing. High power, high repetition rate ultraviolet (UV) lasers provide an opportunity to implement a cost effective high quality, high throughput micromachining process in a 24/7 manufacturing environment. The energy available per pulse and the pulse repetition frequency (PRF) of diode pumped solid state (DPSS) nanosecond UV lasers have increased steadily over the years. Efficient use of the available energy from a laser is important to generate accurate fine features at a high speed with high quality. To achieve maximum material removal and minimal thermal damage for any laser micromachining application, use of the optimal process parameters including energy density or fluence (J/cm2), pulse width, and repetition rate is important. In this study we present a new high power, high PRF QuasarR 355-40 laser from Spectra-Physics with TimeShiftTM technology for unique software adjustable pulse width, pulse splitting, and pulse shaping capabilities. The benefits of these features for micromachining include improved throughput and quality. Specific example and results of silicon scribing are described to demonstrate the processing benefits of the Quasar's available power, PRF, and TimeShift technology.

A thermodynamically general theory for convective vortices

NASA Astrophysics Data System (ADS)

Renno, Nilton O.

2008-08-01

Convective vortices are common features of atmospheres that absorb lower-entropy-energy at higher temperatures than they reject higher-entropy-energy to space. These vortices range from small to large-scale and play an important role in the vertical transport of heat, momentum, and tracer species. Thus, the development of theoretical models for convective vortices is important to our understanding of some of the basic features of planetary atmospheres. The heat engine framework is a useful tool for studying convective vortices. However, current theories assume that convective vortices are reversible heat engines. Since there are questions about how reversible real atmospheric heat engines are, their usefulness for studying real atmospheric vortices is somewhat controversial. In order to reduce this problem, a theory for convective vortices that includes irreversible processes is proposed. The paper's main result is that the proposed theory provides an expression for the pressure drop along streamlines that includes the effects of irreversible processes. It is shown that a simplified version of this expression is a generalization of Bernoulli's equation to convective circulations. It is speculated that the proposed theory not only explains the intensity, but also sheds light on other basic features of convective vortices such as their physical appearance.

[Pathological features of myositis with myositis -specific autoantibodies].

PubMed

Shimizu, Jun; Mimori, Tsuneyo

2014-01-01

Myositis is a heterogeneous group of systemic autoimmune disorders characterized by inflammation of skeletal muscle. Historically, myositis has been defined using clinical features including muscle weakness, skin disease, internal organ involvement, and an association with cancer in adults. From a clinicopathologic approach, myositis has been classified into pathologically distinct subsets, polymyositis, dermatomyositis(DM), necrotizing autoimmune myositis, amyopathic DM, and non-specific myositis. Although the characteristic pathological changes are believed to be important in pathological mechanisms of each subset of myositis, in clinical practices, the percentages of the patients with typical pathological findings are usually not high. On the other hand, with the recent discovery of new myositis-specific autoantibodies (MSAs), it has been revealed that around 60% of patients with IIMs have been shown to have a anti-myositis-specific autoantibody, including anti-synthetase, anti-Mi-2, anti-MDA5, anti-TIF1 and anti-SRP antibodies. Because of striking association between unique MSAs and distinct clinical phenotypes, these antibodies are thought to be important not only for classifications of IIMs, but also as factors involved in the mechanism underlying their pathogenesis. This review reports recent progress in understanding of pathological features of myositis with MSAs.

How important is vehicle safety for older consumers in the vehicle purchase process?

PubMed

Koppel, Sjaan; Clark, Belinda; Hoareau, Effie; Charlton, Judith L; Newstead, Stuart V

2013-01-01

This study aimed to investigate the importance of vehicle safety to older consumers in the vehicle purchase process. Older (n = 102), middle-aged (n = 791), and younger (n = 109) participants throughout the eastern Australian states of Victoria, New South Wales, and Queensland who had recently purchased a new or used vehicle completed an online questionnaire about their vehicle purchase process. When asked to list the 3 most important considerations in the vehicle purchase process (in an open-ended format), older consumers were mostly likely to list price as their most important consideration (43%). Similarly, when presented with a list of vehicle factors (such as price, design, Australasian New Car Assessment Program [ANCAP] rating), older consumers were most likely to identify price as the most important vehicle factor (36%). When presented with a list of vehicle features (such as automatic transmission, braking, air bags), older consumers in the current study were most likely to identify an antilock braking system (41%) as the most important vehicle feature, and 50 percent of older consumers identified a safety-related vehicle feature as the highest priority vehicle feature (50%). When asked to list up to 3 factors that make a vehicle safe, older consumers in the current study were most likely to list braking systems (35%), air bags (22%), and the driver's behavior or skill (11%). When asked about the influence of safety in the new vehicle purchase process, one third of older consumers reported that all new vehicles are safe (33%) and almost half of the older consumers rated their vehicle as safer than average (49%). A logistic regression model was developed to predict the profile of older consumers more likely to assign a higher priority to safety features in the vehicle purchasing process. The model predicted that the importance of safety-related features was influenced by several variables, including older consumers' beliefs that they could protect themselves and their family from a crash, their traffic infringement history, and whether they had children. These findings are consistent with previous research that suggests that, though older consumers highlight the importance of safety features (i.e., seat belts, air bags, braking), they often downplay the role of safety in their vehicle purchasing process and are more likely to equate vehicle safety with the presence of specific vehicle safety features or technologies rather than the vehicle's crash safety/test results or crashworthiness. The findings from this study provide a foundation to support further research in this area that can be used by policy makers, manufacturers, and other stakeholders to better target the promotion and publicity of vehicle safety features to particular consumer groups (such as older consumers). Better targeted campaigns may help to emphasize the value of safety features and their role in reducing the risk of injury/death. If older consumers are better informed of the benefits of safety features when purchasing a vehicle, a further reduction in injuries and deaths related to motor vehicle crashes may be realized.

A Photographic Atlas of Rock Breakdown Features in Geomorphic Environments

NASA Technical Reports Server (NTRS)

Bourke, Mary C. (Editor); Brearley, J. Alexander; Haas, Randall; Viles, Heather A.

2007-01-01

A primary goal of geomorphological enquiry is to make genetic associations between process and form. In rock breakdown studies, the links between process, inheritance and lithology are not well constrained. In particular, there is a need to establish an understanding of feature persistence. That is, to determine the extent to which in situ rock breakdown (e.g., aeolian abrasion or salt weathering) masks signatures of earlier geomorphic transport processes (e.g., fluvial transport or crater ejecta). Equally important is the extent to which breakdown during geomorphic transport masks the imprint of past weathering. The use of rock features in this way raises the important question: Can features on the surface of a rock reliably indicate its geomorphic history? This has not been determined for rock surfaces on Earth or other planets. A first step towards constraining the links between process, inheritance, and morphology is to identify pristine features produced by different process regimes. The purpose of this atlas is to provide a comprehensive image collection of breakdown features commonly observed on boulders in different geomorphic environments. The atlas is intended as a tool for planetary geoscientists and their students to assist in identifying features found on rocks on planetary surfaces. In compiling this atlas, we have attempted to include features that have formed 'recently' and where the potential for modification by another geomorphic process is low. However, we acknowledge that this is, in fact, difficult to achieve when selecting rocks in their natural environment. We group breakdown features according to their formative environment and process. In selecting images for inclusion in the atlas we were mindful to cover a wide range of climatic zones. For example, in the weathering chapter, clast features are shown from locations such as the hyper-arid polar desert of Antarctica and the semi-arid canyons of central Australia. This is important as some features (e.g., alveoli) occur across climate regimes. We have drawn on the published geomorphological literature and our own field experience. We use, where possible, images of extrusive igneous rocks as the data returned from Mars, Venus and the Moon indicates that this is the predominant rock type. One of the purposes of this atlas is to expand the range of surface features that are known to indicate a particular geomorphic environment or process history. The surface features on boulders in some environments such as aeolian and weathering are well understood. In contrast, those in fluvial or ejecta environments are not. Therefore we have presented a comprehensive assemblage of features that are likely to be produced in each of the geomorphic environments. We hope that this atlas will trigger more research on diagnostic features, particularly their morphometry and detailed morphology, their persistence and rates of formation. In this first edition of the atlas we detail the features found on clasts in three geomorphic environments: aeolian, fluvial and weathering. Future editions of the atlas will include chapters on ejecta, micro-impacts, coastal, colluvial, glacial and structural features.

Why replication is important in landscape genetics: American black bear in the Rocky Mountains

USGS Publications Warehouse

Short, Bull R.A.; Cushman, S.A.; MacE, R.; Chilton, T.; Kendall, K.C.; Landguth, E.L.; Schwartz, Maurice L.; McKelvey, K.; Allendorf, F.W.; Luikart, G.

2011-01-01

We investigated how landscape features influence gene flow of black bears by testing the relative support for 36 alternative landscape resistance hypotheses, including isolation by distance (IBD) in each of 12 study areas in the north central U.S. Rocky Mountains. The study areas all contained the same basic elements, but differed in extent of forest fragmentation, altitude, variation in elevation and road coverage. In all but one of the study areas, isolation by landscape resistance was more supported than IBD suggesting gene flow is likely influenced by elevation, forest cover, and roads. However, the landscape features influencing gene flow varied among study areas. Using subsets of loci usually gave models with the very similar landscape features influencing gene flow as with all loci, suggesting the landscape features influencing gene flow were correctly identified. To test if the cause of the variability of supported landscape features in study areas resulted from landscape differences among study areas, we conducted a limiting factor analysis. We found that features were supported in landscape models only when the features were highly variable. This is perhaps not surprising but suggests an important cautionary note – that if landscape features are not found to influence gene flow, researchers should not automatically conclude that the features are unimportant to the species’ movement and gene flow. Failure to investigate multiple study areas that have a range of variability in landscape features could cause misleading inferences about which landscape features generally limit gene flow. This could lead to potentially erroneous identification of corridors and barriers if models are transferred between areas with different landscape characteristics.

Implications of Sponge Biodiversity Patterns for the Management of a Marine Reserve in Northern Australia

PubMed Central

Przeslawski, Rachel; Alvarez, Belinda; Kool, Johnathan; Bridge, Tom; Caley, M. Julian; Nichol, Scott

2015-01-01

Marine reserves are becoming progressively more important as anthropogenic impacts continue to increase, but we have little baseline information for most marine environments. In this study, we focus on the Oceanic Shoals Commonwealth Marine Reserve (CMR) in northern Australia, particularly the carbonate banks and terraces of the Sahul Shelf and Van Diemen Rise which have been designated a Key Ecological Feature (KEF). We use a species-level inventory compiled from three marine surveys to the CMR to address several questions relevant to marine management: 1) Are carbonate banks and other raised geomorphic features associated with biodiversity hotspots? 2) Can environmental (depth, substrate hardness, slope) or biogeographic (east vs west) variables help explain local and regional differences in community structure? 3) Do sponge communities differ among individual raised geomorphic features? Approximately 750 sponge specimens were collected in the Oceanic Shoals CMR and assigned to 348 species, of which only 18% included taxonomically described species. Between eastern and western areas of the CMR, there was no difference between sponge species richness or assemblages on raised geomorphic features. Among individual raised geomorphic features, sponge assemblages were significantly different, but species richness was not. Species richness showed no linear relationships with measured environmental factors, but sponge assemblages were weakly associated with several environmental variables including mean depth and mean backscatter (east and west) and mean slope (east only). These patterns of sponge diversity are applied to support the future management and monitoring of this region, particularly noting the importance of spatial scale in biodiversity assessments and associated management strategies. PMID:26606745

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

PubMed Central

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. Objective The aims were to describe how to: (i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii) automatically identify the features that best distinguish the groups. Methods The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described – simple or complex; presentation order – which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo18 were used,which included 200 healthy Brazilians of both genders. Results and Conclusion A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods. PMID:29213908

Feature: Post Traumatic Stres Disorder PTSD: NIH Research to Results

MedlinePlus

... including PTSD, persist for those affected by 2005's Hurricane Katrina. This is important because previously researchers found that mental disorders usually decrease and fade after about two years. Ongoing ... Hurricane Ike. Other research is exploring whether certain drugs ...

Hop, Skip and Jump: Animation Software.

ERIC Educational Resources Information Center

Eiser, Leslie

1986-01-01

Discusses the features of animation software packages, reviewing eight commercially available programs. Information provided for each program includes name, publisher, current computer(s) required, cost, documentation, input device, import/export capabilities, printing possibilities, what users can originate, types of image manipulation possible,…

Physical habitat structure of the lake shoreline and littoral zone -- How important is it?

EPA Science Inventory

The recent National Lakes Assessment (NLA) included the first national assessment of littoral and lakeshore physical habitat. It quantified water depth, surface characteristics, bank morphology, lake level fluctuations, substrate, fish concealment features, aquatic macrophytes, l...

Programmed Instruction Revisited.

ERIC Educational Resources Information Center

Skinner, B. F.

1986-01-01

Discusses the history and development of teaching machines, invented to restore the important features of personalized instruction as public school class size increased. Examines teaching and learning problems over the past 50 years, including motivation, attention, appreciation, discovery, and creativity in relation to programmed instruction.…

USEPA EPIC IMPERVIOUS SURFACE RESEARCH IN THE MID-ATLANTIC

EPA Science Inventory

Anthropogenic impervious surfaces have an important relationship with non-point source pollution (NPS) in urban watersheds. These human-created surfaces include such features as roads, parking lots, rooftops, sidewalks, and driveways. The amount of impervious surface area in a ...

Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer

PubMed Central

Kothari, Sonal; Phan, John H.; Young, Andrew N.; Wang, May D.

2016-01-01

Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an “optimal” diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis PMID:28163980

ibex: An open infrastructure software platform to facilitate collaborative work in radiomics

PubMed Central

Zhang, Lifei; Fried, David V.; Fave, Xenia J.; Hunter, Luke A.; Court, Laurence E.

2015-01-01

Purpose: Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (ibex), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions. Methods: The ibex software package was developed using the matlab and c/c++ programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand, ibex is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand, ibex provides an integrated development environment on top of matlab and c/c++, so users are not limited to its built-in functions. In the ibex developer studio, users can plug in, debug, and test new algorithms, extending ibex’s functionality. ibex also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the ibex workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions. Results: Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the ibex software to be intuitive, powerful, and easy to use. ibex can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone ibex and ibex’s source code can be downloaded. Conclusions: The authors successfully implemented ibex, an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation. PMID:25735289

IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.

PubMed

Zhang, Lifei; Fried, David V; Fave, Xenia J; Hunter, Luke A; Yang, Jinzhong; Court, Laurence E

2015-03-01

Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (IBEX), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions. The IBEX software package was developed using the MATLAB and c/c++ programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand, IBEX is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand, IBEX provides an integrated development environment on top of MATLAB and c/c++, so users are not limited to its built-in functions. In the IBEX developer studio, users can plug in, debug, and test new algorithms, extending IBEX's functionality. IBEX also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the IBEX workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions. Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the IBEX software to be intuitive, powerful, and easy to use. IBEX can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone IBEX and IBEX's source code can be downloaded. The authors successfully implemented IBEX, an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation.

Vehicle license plate recognition based on geometry restraints and multi-feature decision

NASA Astrophysics Data System (ADS)

Wu, Jianwei; Wang, Zongyue

2005-10-01

Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.

Nuclear hormone receptors in parasitic helminths

PubMed Central

Wu, Wenjie; LoVerde, Philip T

2010-01-01

Nuclear receptors (NRs) belong to a large protein superfamily that are important transcriptional modulators in metazoans. Parasitic helminths include parasitic worms from the Lophotrochozoa (Platyhelminths) and Ecdysozoa (Nematoda). NRs in parasitic helminths diverged into two different evolutionary lineages. NRs in parasitic Platyhelminths have orthologues in Deuterostomes, in arthropods or both with a feature of extensive gene loss and gene duplication within different gene groups. NRs in parasitic Nematoda follow the nematode evolutionary lineage with a feature of multiple duplication of SupNRs and gene loss. PMID:20600585

SEDPAK—A comprehensive operational system and data-processing package in APPLESOFT BASIC for a settling tube, sediment analyzer

NASA Astrophysics Data System (ADS)

Goldbery, R.; Tehori, O.

SEDPAK provides a comprehensive software package for operation of a settling tube and sand analyzer (2-0.063 mm) and includes data-processing programs for statistical and graphic output of results. The programs are menu-driven and written in APPLESOFT BASIC, conforming with APPLE 3.3 DOS. Data storage and retrieval from disc is an important feature of SEDPAK. Additional features of SEDPAK include condensation of raw settling data via standard size-calibration curves to yield statistical grain-size parameters, plots of grain-size frequency distributions and cumulative log/probability curves. The program also has a module for processing of grain-size frequency data from sieved samples. An addition feature of SEDPAK is the option for automatic data processing and graphic output of a sequential or nonsequential array of samples on one side of a disc.

Fast traffic sign recognition with a rotation invariant binary pattern based feature.

PubMed

Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

2015-01-19

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature

PubMed Central

Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

2015-01-01

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed. PMID:25608217

Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking.

PubMed

Yu, Jun; Yang, Xiaokang; Gao, Fei; Tao, Dacheng

2017-12-01

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.

Doing Local History.

ERIC Educational Resources Information Center

Ohrn, Deborah Gore, Ed.

1992-01-01

This theme issue contains articles about the importance of learning local history. The lead article includes historical information about three Iowa cities: Council Bluffs, Waterloo, and Jefferson. Other features in this issue are entitled: "Iowa Kids Talk,""Digging Into Local History,""Goldie's Top Ten News Stories";…

41 CFR 101-8.311 - Historic Preservation Programs.

Code of Federal Regulations, 2010 CFR

2010-07-01

...; (iii) Importance of the historic features of the property to the conduct of the program or activity.... (b) Obligation—(1) Accessibility. A recipient shall operate any program or activity involving... by handicapped persons. Methods of achieving accessibility include: (i) Making physical alterations...

The relationship study between image features and detection probability based on psychology experiments

NASA Astrophysics Data System (ADS)

Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

2011-04-01

Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment

PubMed Central

Martínez-Torteya, Antonio; Treviño, Víctor; Tamez-Peña, José G.

2015-01-01

The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is very important for treatment research and patient care purposes. Few biomarkers are currently considered in clinical settings, and their use is still optional. The objective of this work was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between AD, MCI, and healthy controls, which may impact future AD biomarkers. For this, Alzheimer's Disease Neuroimaging Initiative database was mined for case-control candidates. At least 652 baseline features extracted from MRI and PET analyses, biological samples, and clinical data up to February 2014 were used. A feature selection methodology that includes a genetic algorithm search coupled to a logistic regression classifier and forward and backward selection strategies was used to explore combinations of features. This generated diagnostic models with sizes ranging from 3 to 8, including well documented AD biomarkers, as well as unexplored image, biochemical, and clinical features. Accuracies of 0.85, 0.79, and 0.80 were achieved for HC-AD, HC-MCI, and MCI-AD classifications, respectively, when evaluated using a blind test set. In conclusion, a set of features provided additional and independent information to well-established AD biomarkers, aiding in the classification of MCI and AD. PMID:26106620

Interactions between hyporheic flow produced by stream meanders, bars, and dunes

USGS Publications Warehouse

Stonedahl, Susa H.; Harvey, Judson W.; Packman, Aaron I.

2013-01-01

Stream channel morphology from grain-scale roughness to large meanders drives hyporheic exchange flow. In practice, it is difficult to model hyporheic flow over the wide spectrum of topographic features typically found in rivers. As a result, many studies only characterize isolated exchange processes at a single spatial scale. In this work, we simulated hyporheic flows induced by a range of geomorphic features including meanders, bars and dunes in sand bed streams. Twenty cases were examined with 5 degrees of river meandering. Each meandering river model was run initially without any small topographic features. Models were run again after superimposing only bars and then only dunes, and then run a final time after including all scales of topographic features. This allowed us to investigate the relative importance and interactions between flows induced by different scales of topography. We found that dunes typically contributed more to hyporheic exchange than bars and meanders. Furthermore, our simulations show that the volume of water exchanged and the distributions of hyporheic residence times resulting from various scales of topographic features are close to, but not linearly additive. These findings can potentially be used to develop scaling laws for hyporheic flow that can be widely applied in streams and rivers.

The role of park conditions and features on park visitation and physical activity.

PubMed

Rung, Ariane L; Mowen, Andrew J; Broyles, Stephanie T; Gustat, Jeanette

2011-09-01

Neighborhood parks play an important role in promoting physical activity. We examined the effect of activity area, condition, and presence of supporting features on number of park users and park-based physical activity levels. 37 parks and 154 activity areas within parks were assessed during summer 2008 for their features and park-based physical activity. Outcomes included any park use, number of park users, mean and total energy expenditure. Independent variables included type and condition of activity area, supporting features, size of activity area, gender, and day of week. Multilevel models controlled for clustering of observations at activity area and park levels. Type of activity area was associated with number of park users, mean and total energy expenditure, with basketball courts having the highest number of users and total energy expenditure, and playgrounds having the highest mean energy expenditure. Condition of activity areas was positively associated with number of basketball court users and inversely associated with number of green space users and total green space energy expenditure. Various supporting features were both positively and negatively associated with each outcome. This study provides evidence regarding characteristics of parks that can contribute to achieving physical activity goals within recreational spaces.

Breast masses in mammography classification with local contour features.

PubMed

Li, Haixia; Meng, Xianjing; Wang, Tingwen; Tang, Yuchun; Yin, Yilong

2017-04-14

Mammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well. In this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass. The proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier. The performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.

Rough sets and Laplacian score based cost-sensitive feature selection

PubMed Central

Yu, Shenglong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. PMID:29912884

Rough sets and Laplacian score based cost-sensitive feature selection.

PubMed

Yu, Shenglong; Zhao, Hong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

Assessment of features for automatic CTG analysis based on expert annotation.

PubMed

Chudácek, Vacláv; Spilka, Jirí; Lhotská, Lenka; Janku, Petr; Koucký, Michal; Huptych, Michal; Bursa, Miroslav

2011-01-01

Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. Number of acceleration and deceleration, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

The uses of synchrotron radiation sources for elemental and chemical microanalysis

USGS Publications Warehouse

Chen, J.R.; Chao, E.C.T.; Minkin, J.A.; Back, J.M.; Jones, K.W.; Rivers, M.L.; Sutton, S.R.

1990-01-01

Synchrotron radiation sources offer important features for the analysis of a material. Among these features is the ability to determine both the elemental composition of the material and the chemical state of its elements. For microscopic analysis synchrotron X-ray fluorescence (SXRF) microprobes now offer spatial resolutions of 10 ??m with minimum detection limits in the 1-10 ppm range depending on the nature of the sample and the synchrotron source used. This paper describes the properties of synchrotron radiation and their importance for elemental analysis, existing synchrotron facilities and those under construction that are optimum for SXRF microanalysis, and a number of applications including the high energy excitation of the K lines of heavy elements, microtomography, and XANES and EXAFS spectroscopies. ?? 1990.

Walking and Walkability: Is Wayfinding a Missing Link? Implications for Public Health Practice.

PubMed

Vandenberg, Ann E; Hunter, Rebecca H; Anderson, Lynda A; Bryant, Lucinda L; Hooker, Steven P; Satariano, William A

2016-02-01

Research on walking and walkability has yet to focus on wayfinding, the interactive, problem-solving process by which people use environmental information to locate themselves and navigate through various settings. We reviewed the literature on outdoor pedestrian-oriented wayfinding to examine its relationship to walking and walkability, 2 areas of importance to physical activity promotion. Our findings document that wayfinding is cognitively demanding and can compete with other functions, including walking itself. Moreover, features of the environment can either facilitate or impede wayfinding, just as environmental features can influence walking. Although there is still much to be learned about wayfinding and walking behaviors, our review helps frame the issues and lays out the importance of this area of research and practice.

Assessment of polar climate change using satellite technology

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.

1988-01-01

Using results of selected studies, this paper highlights some of the problems that exist in the remote sensing of snow and ice, and demonstrates the importance of remote sensing for the study of snow and ice in determining the effect of temperature increase, due to the atmospheric CO2 increase, on the cryospheric features. Evidence obtained from NOAA, Nimbus, and other satellites, that may already indicate a global or at least a regional warming, includes an increase in permafrost temperature in northern Alaska and the retreat of many of the world's small glaciers in the last 100 years. It is emphasized that remote sensing is of major importance as the method of obtaining data for monitoring future changes in cryospheric features.

Feature extraction from multiple data sources using genetic programming

NASA Astrophysics Data System (ADS)

Szymanski, John J.; Brumby, Steven P.; Pope, Paul A.; Eads, Damian R.; Esch-Mosher, Diana M.; Galassi, Mark C.; Harvey, Neal R.; McCulloch, Hersey D.; Perkins, Simon J.; Porter, Reid B.; Theiler, James P.; Young, Aaron C.; Bloch, Jeffrey J.; David, Nancy A.

2002-08-01

Feature extraction from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. We use the GENetic Imagery Exploitation (GENIE) software for this purpose, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land cover features including towns, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

PubMed

Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

2014-12-01

Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

PubMed

Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang

2012-12-05

Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (P<0.05). Correlation analysis between feature importance and the statistical significance of metrics was investigated, and the results revealed a strong positive correlation between them. Overall, the current study demonstrated that major depressive disorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.

Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis.

PubMed

Garnavi, Rahil; Aldeen, Mohammad; Bailey, James

2012-11-01

This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimised selection and integration of features derived from textural, borderbased and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a boundaryseries model of the lesion border and analysing it in spatial and frequency domains, and the geometry features are derived from shape indexes. The optimised selection of features is achieved by using the Gain-Ratio method, which is shown to be computationally efficient for melanoma diagnosis application. Classification is done through the use of four classifiers; namely, Support Vector Machine, Random Forest, Logistic Model Tree and Hidden Naive Bayes. The proposed diagnostic system is applied on a set of 289 dermoscopy images (114 malignant, 175 benign) partitioned into train, validation and test image sets. The system achieves and accuracy of 91.26% and AUC value of 0.937, when 23 features are used. Other important findings include (i) the clear advantage gained in complementing texture with border and geometry features, compared to using texture information only, and (ii) higher contribution of texture features than border-based features in the optimised feature set.

Issues and Importance of "Good" Starting Points for Nonlinear Regression for Mathematical Modeling with Maple: Basic Model Fitting to Make Predictions with Oscillating Data

ERIC Educational Resources Information Center

Fox, William

2012-01-01

The purpose of our modeling effort is to predict future outcomes. We assume the data collected are both accurate and relatively precise. For our oscillating data, we examined several mathematical modeling forms for predictions. We also examined both ignoring the oscillations as an important feature and including the oscillations as an important…

Designing coastal conservation to deliver ecosystem and human well-being benefits.

PubMed

Annis, Gust M; Pearsall, Douglas R; Kahl, Katherine J; Washburn, Erika L; May, Christopher A; Franks Taylor, Rachael; Cole, James B; Ewert, David N; Game, Edward T; Doran, Patrick J

2017-01-01

Conservation scientists increasingly recognize that incorporating human values into conservation planning increases the chances for success by garnering broader project acceptance. However, methods for defining quantitative targets for the spatial representation of human well-being priorities are less developed. In this study we employ an approach for identifying regionally important human values and establishing specific spatial targets for their representation based on stakeholder outreach. Our primary objective was to develop a spatially-explicit conservation plan that identifies the most efficient locations for conservation actions to meet ecological goals while sustaining or enhancing human well-being values within the coastal and nearshore areas of the western Lake Erie basin (WLEB). We conducted an optimization analysis using 26 features representing ecological and human well-being priorities (13 of each), and included seven cost layers. The influence that including human well-being had on project results was tested by running five scenarios and setting targets for human well-being at different levels in each scenario. The most important areas for conservation to achieve multiple goals are clustered along the coast, reflecting a concentration of existing or potentially restorable coastal wetlands, coastal landbird stopover habitat and terrestrial biodiversity, as well as important recreational activities. Inland important areas tended to cluster around trails and high quality inland landbird stopover habitat. Most concentrated areas of importance also are centered on lands that are already conserved, reflecting the lower costs and higher benefits of enlarging these conserved areas rather than conserving isolated, dispersed areas. Including human well-being features in the analysis only influenced the solution at the highest target levels.

Designing coastal conservation to deliver ecosystem and human well-being benefits

PubMed Central

Pearsall, Douglas R.; Kahl, Katherine J.; Washburn, Erika L.; May, Christopher A.; Franks Taylor, Rachael; Cole, James B.; Ewert, David N.; Game, Edward T.; Doran, Patrick J.

2017-01-01

Conservation scientists increasingly recognize that incorporating human values into conservation planning increases the chances for success by garnering broader project acceptance. However, methods for defining quantitative targets for the spatial representation of human well-being priorities are less developed. In this study we employ an approach for identifying regionally important human values and establishing specific spatial targets for their representation based on stakeholder outreach. Our primary objective was to develop a spatially-explicit conservation plan that identifies the most efficient locations for conservation actions to meet ecological goals while sustaining or enhancing human well-being values within the coastal and nearshore areas of the western Lake Erie basin (WLEB). We conducted an optimization analysis using 26 features representing ecological and human well-being priorities (13 of each), and included seven cost layers. The influence that including human well-being had on project results was tested by running five scenarios and setting targets for human well-being at different levels in each scenario. The most important areas for conservation to achieve multiple goals are clustered along the coast, reflecting a concentration of existing or potentially restorable coastal wetlands, coastal landbird stopover habitat and terrestrial biodiversity, as well as important recreational activities. Inland important areas tended to cluster around trails and high quality inland landbird stopover habitat. Most concentrated areas of importance also are centered on lands that are already conserved, reflecting the lower costs and higher benefits of enlarging these conserved areas rather than conserving isolated, dispersed areas. Including human well-being features in the analysis only influenced the solution at the highest target levels. PMID:28241018

43 CFR 17.260 - Historic Preservation Programs.

Code of Federal Regulations, 2010 CFR

2010-10-01

...; (iii) Importance of the historic features of the property to the conduct of the program or activity... quality or special character. (b) Obligations. (1) A recipient shall operate any program or activity... usable by qualified handicapped persons. Methods of achieving accessibility include: (i) Making physical...

Development , Implementation and Evaluation of a Physics-Base Windblown Dust Emission Model

EPA Science Inventory

A physics-based windblown dust emission parametrization scheme is developed and implemented in the CMAQ modeling system. A distinct feature of the present model includes the incorporation of a newly developed, dynamic relation for the surface roughness length, which is important ...

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

Wu, Huixian; Wacker, Daniel; Mileni, Mauro

Opioid receptors mediate the actions of endogenous and exogenous opioids on many physiological processes, including the regulation of pain, respiratory drive, mood, and - in the case of {kappa}-opioid receptor ({kappa}-OR) - dysphoria and psychotomimesis. Here we report the crystal structure of the human {kappa}-OR in complex with the selective antagonist JDTic, arranged in parallel dimers, at 2.9 {angstrom} resolution. The structure reveals important features of the ligand-binding pocket that contribute to the high affinity and subtype selectivity of JDTic for the human {kappa}-OR. Modelling of other important {kappa}-OR-selective ligands, including the morphinan-derived antagonists norbinaltorphimine and 5'-guanidinonaltrindole, and the diterpenemore » agonist salvinorin A analogue RB-64, reveals both common and distinct features for binding these diverse chemotypes. Analysis of site-directed mutagenesis and ligand structure-activity relationships confirms the interactions observed in the crystal structure, thereby providing a molecular explanation for {kappa}-OR subtype selectivity, and essential insights for the design of compounds with new pharmacological properties targeting the human {kappa}-OR.« less

Assessment of chronic post-surgical pain after knee replacement: development of a core outcome set.

PubMed

Wylde, V; MacKichan, F; Bruce, J; Gooberman-Hill, R

2015-05-01

Approximately 20% of patients experience chronic post-surgical pain (CPSP) after total knee replacement (TKR). There is scope to improve assessment of CPSP after TKR, and this study aimed to develop a core outcome set. Eighty patients and 43 clinicians were recruited into a three-round modified Delphi study. In Round 1, participants were presented with 56 pain features identified from a systematic review, structured interviews with patients and focus groups with clinicians. Participants assigned importance ratings, using a 1-9 scale, to individual pain features; those features rated as most important were retained in subsequent rounds. Consensus that a pain feature should be included in the core outcome set was defined as the feature having a rating of 7-9 by ≥70% of both panels (patients and clinicians) and 1-3 by ≤15% of both panels or rated as 7-9 by ≥90% of one panel. Round 1 was completed by 71 patients and 39 clinicians, and Round 3 by 62 patients and 33 clinicians. The final consensus was that 33 pain features were important. These were grouped into an 8-item core outcome set comprising: pain intensity, pain interference with daily living, pain and physical functioning, temporal aspects of pain, pain description, emotional aspects of pain, use of pain medication, and improvement and satisfaction with pain relief. This core outcome set serves to guide assessment of CPSP after TKR. Consistency in assessment can promote standardized reporting and facilitate comparability between studies that address a common but understudied type of CPSP. © 2014 The Authors. European Journal of Pain published by John Wiley & Sons Ltd on behalf of European Pain Federation - EFIC®.

Logistics or patient care: which features do independent Finnish pharmacy owners prioritize in a strategic plan for future information technology systems?

PubMed

Westerling, Anna M; Haikala, Veikko E; Bell, J Simon; Airaksinen, Marja S

2010-01-01

To determine Finnish community pharmacy owners' requirements for the next generation of software systems. Descriptive, nonexperimental, cross-sectional study. Finland during December 2006. 308 independent pharmacy owners. Survey listing 126 features that could potentially be included in the new information technology (IT) system. The list was grouped into five categories: (1) drug information and patient counseling, (2) medication safety, (3) interprofessional collaboration, (4) pharmacy services, and (5) pharmacy internal processes. Perceived value of potential features for a new IT system. The survey was mailed to all independent pharmacy owners in Finland (n = 580; response rate 53% [n = 308]). Respondents gave priority to logistical functions and functions related to drug information and patient care. The highest rated individual features were tracking product expiry (rated as very or quite important by 100% of respondents), computerized drug-drug interaction screening (99%), an electronic version of the national pharmaceutical reference book (97%), and a checklist-type drug information database to assist patient counseling (95%). In addition to the high ranking for logistical features, Finnish pharmacy owners put a priority on support for cognitive pharmaceutical services in the next IT system. Although the importance of logistical functions is understandable, the owners demonstrated a commitment to strategic health policy goals when planning their business IT system.

Sparse generalized linear model with L0 approximation for feature selection and prediction with big omics data.

PubMed

Liu, Zhenqiu; Sun, Fengzhu; McGovern, Dermot P

2017-01-01

Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L 1 , SCAD and MC+. However, none of the existing algorithms optimizes L 0 , which penalizes the number of nonzero features directly. In this paper, we develop a novel sparse generalized linear model (GLM) with L 0 approximation for feature selection and prediction with big omics data. The proposed approach approximate the L 0 optimization directly. Even though the original L 0 problem is non-convex, the problem is approximated by sequential convex optimizations with the proposed algorithm. The proposed method is easy to implement with only several lines of code. Novel adaptive ridge algorithms ( L 0 ADRIDGE) for L 0 penalized GLM with ultra high dimensional big data are developed. The proposed approach outperforms the other cutting edge regularization methods including SCAD and MC+ in simulations. When it is applied to integrated analysis of mRNA, microRNA, and methylation data from TCGA ovarian cancer, multilevel gene signatures associated with suboptimal debulking are identified simultaneously. The biological significance and potential clinical importance of those genes are further explored. The developed Software L 0 ADRIDGE in MATLAB is available at https://github.com/liuzqx/L0adridge.

Latent fingerprint matching.

PubMed

Jain, Anil K; Feng, Jianjiang

2011-01-01

Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Poor quality of ridge impressions, small finger area, and large nonlinear distortion are the main difficulties in latent fingerprint matching compared to plain or rolled fingerprint matching. We propose a system for matching latent fingerprints found at crime scenes to rolled fingerprints enrolled in law enforcement databases. In addition to minutiae, we also use extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map, and skeleton. We tested our system by matching 258 latents in the NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining the NIST SD4, SD14, and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9 percent was improved to 74 percent when extended features were used. In order to evaluate the relative importance of each extended feature, these features were incrementally used in the order of their cost in marking by latent experts. The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy.

Integrating Multibeam Backscatter Angular Response, Mosaic and Bathymetry Data for Benthic Habitat Mapping

PubMed Central

Che Hasan, Rozaimi; Ierodiaconou, Daniel; Laurenson, Laurie; Schimel, Alexandre

2014-01-01

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management. PMID:24824155

A Cross-Sectional Investigation of the Importance of Park Features for Promoting Regular Physical Activity in Parks.

PubMed

Costigan, Sarah A; Veitch, Jenny; Crawford, David; Carver, Alison; Timperio, Anna

2017-11-02

Parks in the US and Australia are generally underutilised, and park visitors typically engage in low levels of physical activity (PA). Better understanding park features that may encourage visitors to be active is important. This study examined the perceived importance of park features for encouraging park-based PA and examined differences by sex, age, parental-status and participation in PA. Cross-sectional surveys were completed by local residents ( n = 2775) living near two parks (2013/2015). Demographic variables, park visitation and leisure-time PA were self-reported, respondents rated the importance of 20 park features for encouraging park-based PA in the next fortnight. Chi-square tests of independence examined differences in importance of park features for PA among sub-groups of local residents (sex, age, parental-status, PA). Park features ranked most important for park-based PA were: well maintained (96.2%), feel safe (95.4%), relaxing atmosphere (91.2%), easy to get to (91.7%), and shady trees (90.3%). All subgroups ranked 'well maintained' as most important. Natural and built environment features of parks are important for promoting adults' park-based PA, and should be considered in park (re)design.

Visual texture perception via graph-based semi-supervised learning

NASA Astrophysics Data System (ADS)

Zhang, Qin; Dong, Junyu; Zhong, Guoqiang

2018-04-01

Perceptual features, for example direction, contrast and repetitiveness, are important visual factors for human to perceive a texture. However, it needs to perform psychophysical experiment to quantify these perceptual features' scale, which requires a large amount of human labor and time. This paper focuses on the task of obtaining perceptual features' scale of textures by small number of textures with perceptual scales through a rating psychophysical experiment (what we call labeled textures) and a mass of unlabeled textures. This is the scenario that the semi-supervised learning is naturally suitable for. This is meaningful for texture perception research, and really helpful for the perceptual texture database expansion. A graph-based semi-supervised learning method called random multi-graphs, RMG for short, is proposed to deal with this task. We evaluate different kinds of features including LBP, Gabor, and a kind of unsupervised deep features extracted by a PCA-based deep network. The experimental results show that our method can achieve satisfactory effects no matter what kind of texture features are used.

Content management systems and E-commerce: a comparative case study

NASA Astrophysics Data System (ADS)

Al Rasheed, Amal A.; El-Masri, Samir D.

2011-12-01

The need for CMS's to create and edit e-commerce websites has increased with the growing importance of e-commerce. In this paper, the various features essential for e-commerce CMS's are explored. The aim of the paper was to find the best CMS solution for e-commerce which includes the best of both CMS and store management. Accordingly, we conducted a study on three popular open source CMS's for e-commerce: VirtueMart from Joomla!, Ubercart from Drupal, and Magento. We took into account features like hosting and installation, performance, support/community, content management, add on modules and functional features. We concluded with improvements that could be made in order to alleviate problems.

Development and Application of a Three-Dimensional Finite Element Vapor Intrusion Model

PubMed Central

Pennell, Kelly G.; Bozkurt, Ozgur; Suuberg, Eric M.

2010-01-01

Details of a three-dimensional finite element model of soil vapor intrusion, including the overall modeling process and the stepwise approach, are provided. The model is a quantitative modeling tool that can help guide vapor intrusion characterization efforts. It solves the soil gas continuity equation coupled with the chemical transport equation, allowing for both advective and diffusive transport. Three-dimensional pressure, velocity, and chemical concentration fields are produced from the model. Results from simulations involving common site features, such as impervious surfaces, porous foundation sub-base material, and adjacent structures are summarized herein. The results suggest that site-specific features are important to consider when characterizing vapor intrusion risks. More importantly, the results suggest that soil gas or subslab gas samples taken without proper regard for particular site features may not be suitable for evaluating vapor intrusion risks; rather, careful attention needs to be given to the many factors that affect chemical transport into and around buildings. PMID:19418819

Speech, Voice, and Communication.

PubMed

Johnson, Julia A

2017-01-01

Communication changes are an important feature of Parkinson's and include both motor and nonmotor features. This chapter will cover briefly the motor features affecting speech production and voice function before focusing on the nonmotor aspects. A description of the difficulties experienced by people with Parkinson's when trying to communicate effectively is presented along with some of the assessment tools and therapists' treatment options. The idea of clinical heterogeneity of PD and subtyping patients with different communication problems is explored and suggestions are made on how this may influence clinicians' treatment methods and choices so as to provide personalized therapy programmes. The importance of encouraging and supporting people to maintain social networks, employment, and leisure activities is stated as the key to achieving sustainability. Finally looking into the future, the emergence of new technologies is seen as providing further possibilities to support therapists in the goal of helping people with Parkinson's to maintain good communication skills throughout the course of the disease. © 2017 Elsevier Inc. All rights reserved.

Chemical features of Ganoderma polysaccharides with antioxidant, antitumor and antimicrobial activities.

PubMed

Ferreira, Isabel C F R; Heleno, Sandrina A; Reis, Filipa S; Stojkovic, Dejan; Queiroz, Maria João R P; Vasconcelos, M Helena; Sokovic, Marina

2015-06-01

Ganoderma genus comprises one of the most commonly studied species worldwide, Ganoderma lucidum. However, other Ganoderma species have been also reported as important sources of bioactive compounds. Polysaccharides are important contributors to the medicinal properties reported for Ganoderma species, as demonstrated by the numerous publications, including reviews, on this matter. Yet, what are the chemical features of Ganoderma polysaccharides that have bioactivity? In the present manuscript, the chemical features of Ganoderma polysaccharides with reported antioxidant, antitumor and antimicrobial activities (the most studied worldwide) are analyzed in detail. The composition of sugars (homo- versus hetero-glucans and other polysaccharides), type of glycosidic linkages, branching patterns, and linkage to proteins are discussed. Methods for extraction, isolation and identification are evaluated and, finally, the bioactivity of polysaccharidic extracts and purified compounds are discussed. The integration of data allows deduction of structure-activity relationships and gives clues to the chemical aspects involved in Ganoderma bioactivity. Copyright © 2014 Elsevier Ltd. All rights reserved.

Limb/pelvis hypoplasia/aplasia with skull defect (Schinzel phocomelia): distinctive features and prenatal detection.

PubMed

Olney, R S; Hoyme, H E; Roche, F; Ferguson, K; Hintz, S; Madan, A

2001-11-01

Schinzel phocomelia syndrome is characterized by limb/pelvis hypoplasia/aplasia: specifically, intercalary limb deficiencies and absent or hypoplastic pelvic bones. The phenotype is similar to that described in a related multiple malformation syndrome known as Al-Awadi/Raas-Rothschild syndrome. The additional important feature of large parietooccipital skull defects without meningocele, encephalocele, or other brain malformation has thus far been reported only in children with Schinzel phocomelia syndrome. We recently evaluated a boy affected with Schinzel phocomelia born to nonconsanguineous healthy parents of Mexican origin. A third-trimester fetal ultrasound scan showed severe limb deficiencies and an absent pelvis. The infant died shortly after birth. Dysmorphology examination, radiographs, and autopsy revealed quadrilateral intercalary limb deficiencies with preaxial toe polydactyly; an absent pelvis and a 7 x 3-cm skull defect; and extraskeletal anomalies including microtia, telecanthus, micropenis with cryptorchidism, renal cysts, stenosis of the colon, and a cleft alveolar ridge. A normal 46,XY karyotype was demonstrated, and autosomal recessive inheritance was presumed on the basis of previously reported families. This case report emphasizes the importance of recognizing severe pelvic and skull deficiencies (either post- or prenatally) in differentiating infants with Schinzel phocomelia from other multiple malformation syndromes that feature intercalary limb defects, including thalidomide embryopathy and Roberts-SC phocomelia. Copyright 2001 Wiley-Liss, Inc.

Why sauropods had long necks; and why giraffes have short necks.

PubMed

Taylor, Michael P; Wedel, Mathew J

2013-01-01

The necks of the sauropod dinosaurs reached 15 m in length: six times longer than that of the world record giraffe and five times longer than those of all other terrestrial animals. Several anatomical features enabled this extreme elongation, including: absolutely large body size and quadrupedal stance providing a stable platform for a long neck; a small, light head that did not orally process food; cervical vertebrae that were both numerous and individually elongate; an efficient air-sac-based respiratory system; and distinctive cervical architecture. Relevant features of sauropod cervical vertebrae include: pneumatic chambers that enabled the bone to be positioned in a mechanically efficient way within the envelope; and muscular attachments of varying importance to the neural spines, epipophyses and cervical ribs. Other long-necked tetrapods lacked important features of sauropods, preventing the evolution of longer necks: for example, giraffes have relatively small torsos and large, heavy heads, share the usual mammalian constraint of only seven cervical vertebrae, and lack an air-sac system and pneumatic bones. Among non-sauropods, their saurischian relatives the theropod dinosaurs seem to have been best placed to evolve long necks, and indeed their necks probably surpassed those of giraffes. But 150 million years of evolution did not suffice for them to exceed a relatively modest 2.5 m.

Why sauropods had long necks; and why giraffes have short necks

PubMed Central

Wedel, Mathew J.

2013-01-01

The necks of the sauropod dinosaurs reached 15 m in length: six times longer than that of the world record giraffe and five times longer than those of all other terrestrial animals. Several anatomical features enabled this extreme elongation, including: absolutely large body size and quadrupedal stance providing a stable platform for a long neck; a small, light head that did not orally process food; cervical vertebrae that were both numerous and individually elongate; an efficient air-sac-based respiratory system; and distinctive cervical architecture. Relevant features of sauropod cervical vertebrae include: pneumatic chambers that enabled the bone to be positioned in a mechanically efficient way within the envelope; and muscular attachments of varying importance to the neural spines, epipophyses and cervical ribs. Other long-necked tetrapods lacked important features of sauropods, preventing the evolution of longer necks: for example, giraffes have relatively small torsos and large, heavy heads, share the usual mammalian constraint of only seven cervical vertebrae, and lack an air-sac system and pneumatic bones. Among non-sauropods, their saurischian relatives the theropod dinosaurs seem to have been best placed to evolve long necks, and indeed their necks probably surpassed those of giraffes. But 150 million years of evolution did not suffice for them to exceed a relatively modest 2.5 m. PMID:23638372

Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST‐Indel)

PubMed Central

Douville, Christopher; Masica, David L.; Stenson, Peter D.; Cooper, David N.; Gygax, Derek M.; Kim, Rick; Ryan, Michael

2015-01-01

ABSTRACT Insertion/deletion variants (indels) alter protein sequence and length, yet are highly prevalent in healthy populations, presenting a challenge to bioinformatics classifiers. Commonly used features—DNA and protein sequence conservation, indel length, and occurrence in repeat regions—are useful for inference of protein damage. However, these features can cause false positives when predicting the impact of indels on disease. Existing methods for indel classification suffer from low specificities, severely limiting clinical utility. Here, we further develop our variant effect scoring tool (VEST) to include the classification of in‐frame and frameshift indels (VEST‐indel) as pathogenic or benign. We apply 24 features, including a new “PubMed” feature, to estimate a gene's importance in human disease. When compared with four existing indel classifiers, our method achieves a drastically reduced false‐positive rate, improving specificity by as much as 90%. This approach of estimating gene importance might be generally applicable to missense and other bioinformatics pathogenicity predictors, which often fail to achieve high specificity. Finally, we tested all possible meta‐predictors that can be obtained from combining the four different indel classifiers using Boolean conjunctions and disjunctions, and derived a meta‐predictor with improved performance over any individual method. PMID:26442818

A Virtual Microscope for Academic Medical Education: The Pate Project

PubMed Central

Hundt, Christian; Schmitt, Volker H; Schömer, Elmar; Kirkpatrick, C James

2015-01-01

Background Whole-slide imaging (WSI) has become more prominent and continues to gain in importance in student teaching. Applications with different scope have been developed. Many of these applications have either technical or design shortcomings. Objective To design a survey to determine student expectations of WSI applications for teaching histological and pathological diagnosis. To develop a new WSI application based on the findings of the survey. Methods A total of 216 students were questioned about their experiences and expectations of WSI applications, as well as favorable and undesired features. The survey included 14 multiple choice and two essay questions. Based on the survey, we developed a new WSI application called Pate utilizing open source technologies. Results The survey sample included 216 students—62.0% (134) women and 36.1% (78) men. Out of 216 students, 4 (1.9%) did not disclose their gender. The best-known preexisting WSI applications included Mainzer Histo Maps (199/216, 92.1%), Histoweb Tübingen (16/216, 7.4%), and Histonet Ulm (8/216, 3.7%). Desired features for the students were latitude in the slides (190/216, 88.0%), histological (191/216, 88.4%) and pathological (186/216, 86.1%) annotations, points of interest (181/216, 83.8%), background information (146/216, 67.6%), and auxiliary informational texts (113/216, 52.3%). By contrast, a discussion forum was far less important (9/216, 4.2%) for the students. Conclusions The survey revealed that the students appreciate a rich feature set, including WSI functionality, points of interest, auxiliary informational texts, and annotations. The development of Pate was significantly influenced by the findings of the survey. Although Pate currently has some issues with the Zoomify file format, it could be shown that Web technologies are capable of providing a high-performance WSI experience, as well as a rich feature set. PMID:25963527

The role of the reef-dune system in coastal protection in Puerto Morelos (Mexico)

NASA Astrophysics Data System (ADS)

Franklin, Gemma L.; Torres-Freyermuth, Alec; Medellin, Gabriela; Allende-Arandia, María Eugenia; Appendini, Christian M.

2018-04-01

Reefs and sand dunes are critical morphological features providing natural coastal protection. Reefs dissipate around 90 % of the incident wave energy through wave breaking, whereas sand dunes provide the final natural barrier against coastal flooding. The storm impact on coastal areas with these features depends on the relative elevation of the extreme water levels with respect to the sand dune morphology. However, despite the importance of barrier reefs and dunes in coastal protection, poor management practices have degraded these ecosystems, increasing their vulnerability to coastal flooding. The present study aims to theoretically investigate the role of the reef-dune system in coastal protection under current climatic conditions at Puerto Morelos, located in the Mexican Caribbean Sea, using a widely validated nonlinear non-hydrostatic numerical model (SWASH). Wave hindcast information, tidal level, and a measured beach profile of the reef-dune system in Puerto Morelos are employed to estimate extreme runup and the storm impact scale for current and theoretical scenarios. The numerical results show the importance of including the storm surge when predicting extreme water levels and also show that ecosystem degradation has important implications for coastal protection against storms with return periods of less than 10 years. The latter highlights the importance of conservation of the system as a mitigation measure to decrease coastal vulnerability and infrastructure losses in coastal areas in the short to medium term. Furthermore, the results are used to evaluate the applicability of runup parameterisations for beaches to reef environments. Numerical analysis of runup dynamics suggests that runup parameterisations for reef environments can be improved by including the fore reef slope. Therefore, future research to develop runup parameterisations incorporating reef geometry features (e.g. reef crest elevation, reef lagoon width, fore reef slope) is warranted.

One-carbon metabolism and nucleotide biosynthesis as attractive targets for anticancer therapy

PubMed Central

Shuvalov, Oleg; Petukhov, Alexey; Daks, Alexandra; Fedorova, Olga; Vasileva, Elena; Barlev, Nickolai A.

2017-01-01

Cancer-related metabolism has recently emerged as one of the “hallmarks of cancer”. It has several important features, including altered metabolism of glucose and glutamine. Importantly, altered cancer metabolism connects different biochemical pathways into the one fine-tuned metabolic network, which stimulates high proliferation rates and plasticity to malignant cells. Among the keystones of cancer metabolism are one-carbon metabolism and nucleotide biosynthesis, which provide building blocks to anabolic reactions. Accordingly, the importance of these metabolic pathways for anticancer therapy has well been documented by more than fifty years of clinical use of specific metabolic inhibitors – methotrexate and nucleotides analogs. In this review we discuss one-carbon metabolism and nucleotide biosynthesis as common and specific features of many, if not all, tumors. The key enzymes involved in these pathways also represent promising anti-cancer therapeutic targets. We review different aspects of these metabolic pathways including their biochemistry, compartmentalization and expression of the key enzymes and their regulation at different levels. We also discuss the effects of known inhibitors of these pathways as well as the recent data on other enzymes of the same pathways as perspective pharmacological targets. PMID:28177894

LROC Observations of Geologic Features in the Marius Hills

NASA Astrophysics Data System (ADS)

Lawrence, S.; Stopar, J. D.; Hawke, R. B.; Denevi, B. W.; Robinson, M. S.; Giguere, T.; Jolliff, B. L.

2009-12-01

Lunar volcanic cones, domes, and their associated geologic features are important objects of study for the LROC science team because they represent possible volcanic endmembers that may yield important insights into the history of lunar volcanism and are potential sources of lunar resources. Several hundred domes, cones, and associated volcanic features are currently targeted for high-resolution LROC Narrow Angle Camera [NAC] imagery[1]. The Marius Hills, located in Oceanus Procellarum (centered at ~13.4°N, -55.4°W), represent the largest concentration of these volcanic features on the Moon including sinuous rilles, volcanic cones, domes, and depressions [e.g., 2-7]. The Marius region is thus a high priority for future human lunar exploration, as signified by its inclusion in the Project Constellation list of notional future human lunar exploration sites [8], and will be an intense focus of interest for LROC science investigations. Previous studies of the Marius Hills have utilized telescopic, Lunar Orbiter, Apollo, and Clementine imagery to study the morphology and composition of the volcanic features in the region. Complementary LROC studies of the Marius region will focus on high-resolution NAC images of specific features for studies of morphology (including flow fronts, dome/cone structure, and possible layering) and topography (using stereo imagery). Preliminary studies of the new high-resolution images of the Marius Hills region reveal small-scale features in the sinuous rilles including possible outcrops of bedrock and lobate lava flows from the domes. The observed Marius Hills are characterized by rough surface textures, including the presence of large boulders at the summits (~3-5m diameter), which is consistent with the radar-derived conclusions of [9]. Future investigations will involve analysis of LROC stereo photoclinometric products and coordinating NAC images with the multispectral images collected by the LROC WAC, especially the ultraviolet data, to enable measurements of color variations within and amongst deposits and provide possible compositional insights, including the location of possibly related pyroclastic deposits. References: [1] J. D. Stopar et al. (2009), LRO Science Targeting Meeting, Abs. 6039 [2] Greeley R (1971) Moon, 3, 289-314 [3] Guest J. E. (1971) Geol. and Phys. of the Moon, p. 41-53. [4] McCauley J. F. (1967) USGS Geologic Atlas of the Moon, Sheet I-491 [5] Weitz C. M. and Head J. W. (1999) JGR, 104, 18933-18956 [6] Heather D. J. et al. (2003) JGR, doi:10.1029/2002JE001938 [7] Whitford-Stark, J. L., and J. W. Head (1977) Proc. LSC 8th, 2705-2724 [8] Gruener J. and Joosten B. K. (2009) LRO Science Targeting Meeting, Abs. 6036 [9] Campbell B. A. et al. (2009) JGR, doi:10.1029/2008JE003253.

Effects of landscape features on waterbird use of rice fields

USGS Publications Warehouse

King, S.; Elphick, C.S.; Guadagnin, D.; Taft, O.; Amano, T.

2010-01-01

Literature is reviewed to determine the effects of landscape features on waterbird use of fields in regions where rice (Oryza sativa) is grown. Rice-growing landscapes often consist of diverse land uses and land cover, including rice fields, irrigation ditches, other agricultural fields, grasslands, forests and natural wetlands. Numerous studies indicate that local management practices, such as water depth and timing of flooding and drawdown, can strongly influence waterbird use of a given rice field. However, the effects of size and distribution of rice fields and associated habitats at a landscape scale have received less attention. Even fewer studies have focused on local and landscape effects simultaneously. Habitat connectivity, area of rice, distance to natural wetlands, and presence and distance to unsuitable habitat can be important parameters influencing bird use of rice fields. However, responses to a given landscape vary with landscape structure, scale of analysis, among taxa and within taxa among seasons. A lack of multi-scale studies, particularly those extending beyond simple presence and abundance of a given species, and a lack of direct tests comparing the relative importance of landscape features with in-field management activities limits understanding of the importance of landscape in these systems and hampers waterbird conservation and management.

An Optimal Mean Based Block Robust Feature Extraction Method to Identify Colorectal Cancer Genes with Integrated Data.

PubMed

Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Liu, Hui

2017-08-17

It is urgent to diagnose colorectal cancer in the early stage. Some feature genes which are important to colorectal cancer development have been identified. However, for the early stage of colorectal cancer, less is known about the identity of specific cancer genes that are associated with advanced clinical stage. In this paper, we conducted a feature extraction method named Optimal Mean based Block Robust Feature Extraction method (OMBRFE) to identify feature genes associated with advanced colorectal cancer in clinical stage by using the integrated colorectal cancer data. Firstly, based on the optimal mean and L 2,1 -norm, a novel feature extraction method called Optimal Mean based Robust Feature Extraction method (OMRFE) is proposed to identify feature genes. Then the OMBRFE method which introduces the block ideology into OMRFE method is put forward to process the colorectal cancer integrated data which includes multiple genomic data: copy number alterations, somatic mutations, methylation expression alteration, as well as gene expression changes. Experimental results demonstrate that the OMBRFE is more effective than previous methods in identifying the feature genes. Moreover, genes identified by OMBRFE are verified to be closely associated with advanced colorectal cancer in clinical stage.

The Imagination of Early Childhood Education.

ERIC Educational Resources Information Center

Morgan, Harry

This book examines historical features from antiquity through present times that are important to early childhood scholars. Chapter 1 presents the history of education, including discussions of educational practices from the seventeenth through the twentieth centuries in Europe and the United States, recent efforts to merge preschool and…

GAISE 2016 Promotes Statistical Literacy

ERIC Educational Resources Information Center

Schield, Milo

2017-01-01

In the 2005 Guidelines for Assessment and Instruction in Statistics Education (GAISE), statistical literacy featured as a primary goal. The 2016 revision eliminated statistical literacy as a stated goal. Although this looks like a rejection, this paper argues that by including multivariate thinking and--more importantly--confounding as recommended…

The User Interface: How Does Your Product Look and Feel?

ERIC Educational Resources Information Center

Strukhoff, Roger

1987-01-01

Discusses the importance of user cordial interfaces to the successful marketing of optical data disk products, and describes features of several online systems. The topics discussed include full text searching, indexed searching, menu driven interfaces, natural language interfaces, computer graphics, and possible future developments. (CLB)

Small white matter lesion detection in cerebral small vessel disease

NASA Astrophysics Data System (ADS)

Ghafoorian, Mohsen; Karssemeijer, Nico; van Uden, Inge; de Leeuw, Frank E.; Heskes, Tom; Marchiori, Elena; Platel, Bram

2015-03-01

Cerebral small vessel disease (SVD) is a common finding on magnetic resonance images of elderly people. White matter lesions (WML) are important markers for not only the small vessel disease, but also neuro-degenerative diseases including multiple sclerosis, Alzheimer's disease and vascular dementia. Volumetric measurements such as the "total lesion load", have been studied and related to these diseases. With respect to SVD we conjecture that small lesions are important, as they have been observed to grow over time and they form the majority of lesions in number. To study these small lesions they need to be annotated, which is a complex and time-consuming task. Existing (semi) automatic methods have been aimed at volumetric measurements and large lesions, and are not suitable for the detection of small lesions. In this research we established a supervised voxel classification CAD system, optimized and trained to exclusively detect small WMLs. To achieve this, several preprocessing steps were taken, which included a robust standardization of subject intensities to reduce inter-subject intensity variability as much as possible. A number of features that were found to be well identifying small lesions were calculated including multimodal intensities, tissue probabilities, several features for accurate location description, a number of second order derivative features as well as multi-scale annular filter for blobness detection. Only small lesions were used to learn the target concept via Adaboost using random forests as its basic classifiers. Finally the results were evaluated using Free-response receiver operating characteristic.

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

Statistical universals reveal the structures and functions of human music.

PubMed

Savage, Patrick E; Brown, Steven; Sakai, Emi; Currie, Thomas E

2015-07-21

Music has been called "the universal language of mankind." Although contemporary theories of music evolution often invoke various musical universals, the existence of such universals has been disputed for decades and has never been empirically demonstrated. Here we combine a music-classification scheme with statistical analyses, including phylogenetic comparative methods, to examine a well-sampled global set of 304 music recordings. Our analyses reveal no absolute universals but strong support for many statistical universals that are consistent across all nine geographic regions sampled. These universals include 18 musical features that are common individually as well as a network of 10 features that are commonly associated with one another. They span not only features related to pitch and rhythm that are often cited as putative universals but also rarely cited domains including performance style and social context. These cross-cultural structural regularities of human music may relate to roles in facilitating group coordination and cohesion, as exemplified by the universal tendency to sing, play percussion instruments, and dance to simple, repetitive music in groups. Our findings highlight the need for scientists studying music evolution to expand the range of musical cultures and musical features under consideration. The statistical universals we identified represent important candidates for future investigation.

Statistical universals reveal the structures and functions of human music

PubMed Central

Savage, Patrick E.; Brown, Steven; Sakai, Emi; Currie, Thomas E.

2015-01-01

Music has been called “the universal language of mankind.” Although contemporary theories of music evolution often invoke various musical universals, the existence of such universals has been disputed for decades and has never been empirically demonstrated. Here we combine a music-classification scheme with statistical analyses, including phylogenetic comparative methods, to examine a well-sampled global set of 304 music recordings. Our analyses reveal no absolute universals but strong support for many statistical universals that are consistent across all nine geographic regions sampled. These universals include 18 musical features that are common individually as well as a network of 10 features that are commonly associated with one another. They span not only features related to pitch and rhythm that are often cited as putative universals but also rarely cited domains including performance style and social context. These cross-cultural structural regularities of human music may relate to roles in facilitating group coordination and cohesion, as exemplified by the universal tendency to sing, play percussion instruments, and dance to simple, repetitive music in groups. Our findings highlight the need for scientists studying music evolution to expand the range of musical cultures and musical features under consideration. The statistical universals we identified represent important candidates for future investigation. PMID:26124105

Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis

PubMed Central

Zheng, Lu-Lu; Niu, Shen; Hao, Pei; Feng, KaiYan; Cai, Yu-Dong; Li, Yixue

2011-01-01

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations. PMID:22174779

Automatic evaluation of intrapartum fetal heart rate recordings: a comprehensive analysis of useful features.

PubMed

Chudáček, V; Spilka, J; Janků, P; Koucký, M; Lhotská, L; Huptych, M

2011-08-01

Cardiotocography is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO), used routinely since the 1960s by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. We assess the features on a large data set (552 records) and unlike in other published papers we use three-class expert evaluation of the records instead of the pH values. We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. The number of accelerations and decelerations, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

PubMed

Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

2018-04-02

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Polymer-Nanoparticle Composites: From Synthesis to Modern Applications

PubMed Central

Hanemann, Thomas; Szabó, Dorothée Vinga

2010-01-01

The addition of inorganic spherical nanoparticles to polymers allows the modification of the polymers physical properties as well as the implementation of new features in the polymer matrix. This review article covers considerations on special features of inorganic nanoparticles, the most important synthesis methods for ceramic nanoparticles and nanocomposites, nanoparticle surface modification, and composite formation, including drawbacks. Classical nanocomposite properties, as thermomechanical, dielectric, conductive, magnetic, as well as optical properties, will be summarized. Finally, typical existing and potential applications will be shown with the focus on new and innovative applications, like in energy storage systems.

Intestinal microbiome-gut-brain axis and irritable bowel syndrome.

PubMed

Moser, Gabriele; Fournier, Camille; Peter, Johannes

2018-03-01

Psychological comorbidity is highly present in irritable bowel syndrome (IBS). Recent research points to a role of intestinal microbiota in visceral hypersensitivity, anxiety, and depression. Increased disease reactivity to psychological stress has been described too. A few clinical studies have attempted to identify features of dysbiosis in IBS. While animal studies revealed strong associations between stress and gut microbiota, studies in humans are rare. This review covers the most important studies on intestinal microbial correlates of psychological and clinical features in IBS, including stress, anxiety, and depression.

Automated detection of diabetic retinopathy on digital fundus images.

PubMed

Sinthanayothin, C; Boyce, J F; Williamson, T H; Cook, H L; Mensah, E; Lal, S; Usher, D

2002-02-01

The aim was to develop an automated screening system to analyse digital colour retinal images for important features of non-proliferative diabetic retinopathy (NPDR). High performance pre-processing of the colour images was performed. Previously described automated image analysis systems were used to detect major landmarks of the retinal image (optic disc, blood vessels and fovea). Recursive region growing segmentation algorithms combined with the use of a new technique, termed a 'Moat Operator', were used to automatically detect features of NPDR. These features included haemorrhages and microaneurysms (HMA), which were treated as one group, and hard exudates as another group. Sensitivity and specificity data were calculated by comparison with an experienced fundoscopist. The algorithm for exudate recognition was applied to 30 retinal images of which 21 contained exudates and nine were without pathology. The sensitivity and specificity for exudate detection were 88.5% and 99.7%, respectively, when compared with the ophthalmologist. HMA were present in 14 retinal images. The algorithm achieved a sensitivity of 77.5% and specificity of 88.7% for detection of HMA. Fully automated computer algorithms were able to detect hard exudates and HMA. This paper presents encouraging results in automatic identification of important features of NPDR.

Posteroanterior versus anteroposterior lumbar spine radiology

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

Tsuno, M.M.; Shu, G.J.

The posteroanterior view of the lumbar spine has important features including radiation protection and image quality; these have been studied by various investigators. Investigators have shown that sensitive tissues receive less radiation dosage in the posteroanterior view of the spine for scoliosis screening and intracranial tomography without altering the image quality. This paper emphasizes the importance of the radiation safety aspect of the posteroanterior view and shows the improvement in shape distortion in the lumbar vertebrae.

Spondyloenchondrodysplasia: a rare cause of short stature.

PubMed

Yeşiltepe-Mutlu, Gül; Ozsu, Elif; Cizmecioğlu, Filiz Mine; Alanay, Yasemin; Hatun, Sükrü

2011-01-01

Skeletal dysplasias (osteochondrodysplasias) are a group of diseases that must be included in the differential diagnosis of disproportionate short stature. History, clinical and radiologic findings and consanguinity are important features to be considered when a specific diagnosis is investigated. Spondyloenchondrodysplasia is a very rare skeletal dysplasia characterized with enchondromas in the long bones and platyspondyly. Manifestation of the disorder may include neurological involvement (spasticity, intracranial calcifications and mental retardation) and immune dysfunction. Herein, we report a 12-year-old boy who admitted to our clinic with short stature, who was born to consanguineous parents. He presented clinical (significant widening of wrists, ankles and knees) and radiologic (enchondromatous lesions in the metaphysis of long bones) features of spondyloenchondrodysplasia but did not yet have neurologic or immunologic involvement.

Combustion process science and technology

NASA Technical Reports Server (NTRS)

Hale, Robert R.

1989-01-01

An important and substantial area of technical work in which noncontact temperature measurement (NCTM) is desired is that involving combustion process research. In the planning for this workshop, it was hoped that W. Serignano would provide a briefing regarding the experimental requirements for thermal measurements to support such research. The particular features of thermal measurement requirements included those describing the timeline for combustion experiments, the requirements for thermal control and diagnostics of temperature and other related thermal measurements and the criticality to the involved science to parametric features of measurement capability including precision, repeatability, stability, and resolution. In addition, it was hoped that definitions could be provided which characterize the needs for concurrent imaging as it relates to science observations during the conduct of experimentation.

Toward an Efficient Icing CFD Process Using an Interactive Software Toolkit: Smagglce 2D

NASA Technical Reports Server (NTRS)

Vickerman, Mary B.; Choo, Yung K.; Schilling, Herbert W.; Baez, Marivell; Braun, Donald C.; Cotton, Barbara J.

2001-01-01

Two-dimensional CID analysis for iced airfoils can be a labor-intensive task. The software toolkit SmaggIce 2D is being developed to help streamline the CID process and provide the unique features needed for icing. When complete, it will include a combination of partially automated and fully interactive tools for all aspects of the tasks leading up to the flow analysis: geometry preparation, domain decomposition. block boundary demoralization. gridding, and linking with a flow solver. It also includes tools to perform ice shape characterization, an important aid in determining the relationship between ice characteristics and their effects on aerodynamic performance. Completed tools, work-in-progress, and planned features of the software toolkit are presented here.

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting.

PubMed

Tong, Tong; Ledig, Christian; Guerrero, Ricardo; Schuh, Andreas; Koikkalainen, Juha; Tolonen, Antti; Rhodius, Hanneke; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W; Soininen, Hilkka; Remes, Anne M; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; Baroni, Marta; Lötjönen, Jyrki; Flier, Wiesje van der; Rueckert, Daniel

2017-01-01

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.

Computers as Teaching Tools: Some Examples and Guidelines.

ERIC Educational Resources Information Center

Beins, Bernard C.

The use of computers in the classroom has been touted as an important innovation in education. This article features some recently developed software for use in teaching psychology and different approaches to classroom computer use. Uses of software packages for psychological research designs are included as are applications and limitations of…

Specific Features of Entrepreneurial Departments Management in Russian Companies

ERIC Educational Resources Information Center

Troshina, Elena P.; Mantulenko, Valentina V.; Shaposhnikov, Vladislav A.; Anopchenko, Tatiana Y.

2016-01-01

The topic is considered to be relevant due to the fact that entrepreneurship is necessary for the businesses survival and development worldwide, including Russia, and today some Russian companies are adapting their economies to the more developed economic standards. The entrepreneurial format adoption is an important stage of development for…

Invasibility of mature and 15-year-old deciduous forests by exotic plants

Treesearch

Cynthia D. Huebner; Patrick C. Tobin

2006-01-01

High species richness, resource availability and disturbance are community characteristics associated with forest invasibility. We categorized commonly measured community variables, including species composition, topography, and landscape features, within both mature and 15-year-old clearcuts in West Virginia, USA. We evaluated the importance of each variable for...

Computer Technology and Its Impact on Recreation and Sport Programs.

ERIC Educational Resources Information Center

Ross, Craig M.

This paper describes several types of computer programs that can be useful to sports and recreation programs. Computerized tournament scheduling software is helpful to recreation and parks staff working with tournaments of 50 teams/individuals or more. Important features include team capacity, league formation, scheduling conflicts, scheduling…

Burned in: Fueling the Fire to Teach

ERIC Educational Resources Information Center

Friedman, Audrey A.; Reynolds, Luke

2011-01-01

Almost half of new teachers leave the profession within their first year. New teachers need support, mentoring, encouragement, and, most importantly, hope in order to survive the challenges of their first years of teaching. "Burned In" features essays from today's most visionary educators, including Jim Burke, Peter Elbow, James Loewen, Gregory…

Proceedings, Conference on the Computing Environment for Mathematical Software

NASA Technical Reports Server (NTRS)

1981-01-01

Recent advances in software and hardware technology which make it economical to create computing environments appropriate for specialized applications are addressed. Topics included software tools, FORTRAN standards activity, and features of languages, operating systems, and hardware that are important for the development, testing, and maintenance of mathematical software.

Habitat fragmentation and interspecific competition: Implications for lynx conservation [Chapter 4

Treesearch

Steven W. Buskirk

2000-01-01

Habitat fragmentation and interspecific competition are two important forces that potentially affect lynx populations. Fragmentation operates by various mechanisms, including direct habitat loss, vehicle collisions and behavioral disturbance from roads, and changes in landscape features such as edges. Competition takes two forms: Exploitation competition involves...

Data, Data Everywhere but Not a Byte to Read: Managing Monitoring Information.

ERIC Educational Resources Information Center

Stafford, Susan G.

1993-01-01

Describes the Forest Science Data Bank that contains 2,400 data sets from over 350 existing ecological studies. Database features described include involvement of the scientific community; database documentation; data quality assurance; security; data access and retrieval; and data import/export flexibility. Appendices present the Quantitative…

Two Crystallographic Laboratory and Computational Exercises for Undergraduates.

ERIC Educational Resources Information Center

Lessinger, Leslie

1988-01-01

Describes two introductory exercises designed to teach the fundamental ideas and methods of crystallography, and to convey some important features of inorganic and organic crystal structures to students in an advanced laboratory course. Exercises include "The Crystal Structure of NiO" and "The Crystal Structure of Beta-Fumaric Acid." (CW)

Web OPAC Interfaces: An Overview.

ERIC Educational Resources Information Center

Babu, B. Ramesh; O'Brien, Ann

2000-01-01

Discussion of Web-based online public access catalogs (OPACs) focuses on a review of six Web OPAC interfaces in use in academic libraries in the United Kingdom. Presents a checklist and guidelines of important features and functions that are currently available, including search strategies, access points, display, links, and layout. (Author/LRW)

Paroxysmal atrial fibrillation prediction method with shorter HRV sequences.

PubMed

Boon, K H; Khalil-Hani, M; Malarvili, M B; Sia, C W

2016-10-01

This paper proposes a method that predicts the onset of paroxysmal atrial fibrillation (PAF), using heart rate variability (HRV) segments that are shorter than those applied in existing methods, while maintaining good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to stabilize (electrically) and prevent the onset of atrial arrhythmias with different pacing techniques. We investigate the effect of HRV features extracted from different lengths of HRV segments prior to PAF onset with the proposed PAF prediction method. The pre-processing stage of the predictor includes QRS detection, HRV quantification and ectopic beat correction. Time-domain, frequency-domain, non-linear and bispectrum features are then extracted from the quantified HRV. In the feature selection, the HRV feature set and classifier parameters are optimized simultaneously using an optimization procedure based on genetic algorithm (GA). Both full feature set and statistically significant feature subset are optimized by GA respectively. For the statistically significant feature subset, Mann-Whitney U test is used to filter non-statistical significance features that cannot pass the statistical test at 20% significant level. The final stage of our predictor is the classifier that is based on support vector machine (SVM). A 10-fold cross-validation is applied in performance evaluation, and the proposed method achieves 79.3% prediction accuracy using 15-minutes HRV segment. This accuracy is comparable to that achieved by existing methods that use 30-minutes HRV segments, most of which achieves accuracy of around 80%. More importantly, our method significantly outperforms those that applied segments shorter than 30 minutes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Land mine detection using multispectral image fusion

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

Clark, G.A.; Sengupta, S.K.; Aimonetti, W.D.

1995-03-29

Our system fuses information contained in registered images from multiple sensors to reduce the effects of clutter and improve the ability to detect surface and buried land mines. The sensor suite currently consists of a camera that acquires images in six bands (400nm, 500nm, 600nm, 700nm, 800nm and 900nm). Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a varietymore » of physical properties that are more separable in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, etc.) and some artifacts. We use a supervised learning pattern recognition approach to detecting the metal and plastic land mines. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in a two step process to classify a subimage. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the spectral bands add value to the detection system. The most important features from the various sensors are fused using a supervised learning pattern classifier (the probabilistic neural network). We present results of experiments to detect land mines from real data collected from an airborne platform, and evaluate the usefulness of fusing feature information from multiple spectral bands.« less

A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix.

PubMed

Saito, Akira; Numata, Yasushi; Hamada, Takuya; Horisawa, Tomoyoshi; Cosatto, Eric; Graf, Hans-Peter; Kuroda, Masahiko; Yamamoto, Yoichiro

2016-01-01

Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features. CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.

Familial endocrine myxolentiginosis.

PubMed

Panossian, D H; Marais, G E; Marais, H J

1995-11-01

We present an unusual case of a left atrial myxoma as a feature of a familial mesoectodermal disorder and review the literature. The new term "familial endocrine myxolentiginosis" is proposed, which is descriptive of the major clinical components of the syndrome. Myriad features of this disorder include (1) cardiac myxomas; (2) cutaneous myxomas; (3) multiple lentigines or blue nevi, particularly of the head and neck; (4) bilateral primary pigmented nodular adrenocortical hyperplasia; (5) unusual testicular tumors; (6) pituitary tumors; (7) myxoid fibroadenomas of the breast; (8) myxomatous disorder of the stroma of the breast; (9) ductal adenoma of the breast; and (10) psammomatous melanotic schwannoma. A tentative diagnosis is suggested by identifying two features and a definitive diagnosis is made by three or more features. The clinical and pathologic features of cardiac myxoma in familial endocrine myxolentiginosis are identical to those of familial cardiac myxoma: age < 40 years, atypical locations, multicentric origins, and recurrent presentations. A Venn diagram classification for cardiac myxomas is proposed. We include photographic, echocardiographic, biopsy, and adrenal computerized tomography documentation in our patient. Recognition of this disorder is important because of its clinical, surgical, and genetic implications. The availability of transesophageal echocardiographic technology should allow early diagnosis of this underdiagnosed entity. Clinicians should consider this entity in the differential diagnosis of their patients with any one of these manifestations.

EDITORIAL: Measurement techniques for multiphase flows Measurement techniques for multiphase flows

NASA Astrophysics Data System (ADS)

Okamoto, Koji; Murai, Yuichi

2009-11-01

Research on multiphase flows is very important for industrial applications, including power stations, vehicles, engines, food processing and so on. Multiphase flows originally have nonlinear features because of multiphase systems. The interaction between the phases plays a very interesting role in the flows. The nonlinear interaction causes the multiphase flows to be very complicated. Therefore techniques for measuring multiphase flows are very useful in helping to understand the nonlinear phenomena. The state-of-the-art measurement techniques were presented and discussed at the sixth International Symposium on Measurement Techniques for Multiphase Flows (ISMTMF2008) held in Okinawa, Japan, on 15-17 December 2008. This special feature of Measurement Science and Technology includes selected papers from ISMTMF2008. Okinawa has a long history as the Ryukyus Kingdom. China, Japan and many western Pacific countries have had cultural and economic exchanges through Okinawa for over 1000 years. Much technical and scientific information was exchanged at the symposium in Okinawa. The proceedings of ISMTMF2008 apart from these special featured papers were published in Journal of Physics: Conference Series vol. 147 (2009). We would like to express special thanks to all the contributors to the symposium and this special feature. This special feature will be a milestone in measurement techniques for multiphase flows.

Complex, multi-scale small intestinal topography replicated in cellular growth substrates fabricated via chemical vapor deposition of Parylene C.

PubMed

Koppes, Abigail N; Kamath, Megha; Pfluger, Courtney A; Burkey, Daniel D; Dokmeci, Mehmet; Wang, Lin; Carrier, Rebecca L

2016-08-22

Native small intestine possesses distinct multi-scale structures (e.g., crypts, villi) not included in traditional 2D intestinal culture models for drug delivery and regenerative medicine. The known impact of structure on cell function motivates exploration of the influence of intestinal topography on the phenotype of cultured epithelial cells, but the irregular, macro- to submicron-scale features of native intestine are challenging to precisely replicate in cellular growth substrates. Herein, we utilized chemical vapor deposition of Parylene C on decellularized porcine small intestine to create polymeric intestinal replicas containing biomimetic irregular, multi-scale structures. These replicas were used as molds for polydimethylsiloxane (PDMS) growth substrates with macro to submicron intestinal topographical features. Resultant PDMS replicas exhibit multiscale resolution including macro- to micro-scale folds, crypt and villus structures, and submicron-scale features of the underlying basement membrane. After 10 d of human epithelial colorectal cell culture on PDMS substrates, the inclusion of biomimetic topographical features enhanced alkaline phosphatase expression 2.3-fold compared to flat controls, suggesting biomimetic topography is important in induced epithelial differentiation. This work presents a facile, inexpensive method for precisely replicating complex hierarchal features of native tissue, towards a new model for regenerative medicine and drug delivery for intestinal disorders and diseases.

Trivalent methylated arsenical-induced phosphatidylserine exposure and apoptosis in platelets may lead to increased thrombus formation

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

Bae, Ok-Nam; Lim, Kyung-Min; AMOREPACIFIC CO/R and D Center, Gyeonggi-do 446-729

2009-09-01

Trivalent methylated metabolites of arsenic, monomethylarsonous acid (MMA{sup III}) and dimethylarsinous acid (DMA{sup III}), have been found highly reactive and toxic in various cells and in vivo animal models, suggesting their roles in the arsenic-associated toxicity. However, their effects on cardiovascular system including blood cells, one of the most important targets for arsenic toxicity, remain poorly understood. Here we found that MMA{sup III} and DMA{sup III} could induce procoagulant activity and apoptosis in platelets, which play key roles in the development of various cardiovascular diseases (CVDs) through excessive thrombus formation. In freshly isolated human platelets, treatment of MMA{sup III} resultedmore » in phosphatidylserine (PS) exposure, a hallmark of procoagulant activation, accompanied by distinctive apoptotic features including mitochondrial membrane potential disruption, cytochrome c release, and caspase-3 activation. These procoagulant activation and apoptotic features were found to be mediated by the depletion of protein thiol and intracellular ATP, and flippase inhibition by MMA{sup III}, while the intracellular calcium increase or reactive oxygen species generation was not involved. Importantly, increased platelet procoagulant activity by MMA{sup III} resulted in enhanced blood coagulation and excessive thrombus formation in a rat in vivo venous thrombosis model. DMA{sup III} also induced PS-exposure with apoptotic features mediated by protein thiol depletion, which resulted in enhanced thrombin generation. In summary, we believe that this study provides an important evidence for the role of trivalent methylated arsenic metabolites in arsenic-associated CVDs, giving a novel insight into the role of platelet apoptosis in toxicant-induced cardiovascular toxicity.« less

Does Translational Symmetry Matter on the Micro Scale? Fibroblastic and Osteoblastic Interactions with the Topographically Distinct Poly(ε-caprolactone)/Hydroxyapatite Thin Films

PubMed Central

2015-01-01

Material composition and topography of the cell-contacting material interface are important considerations in the design of biomaterials at the nano and micro scales. This study is one of the first to have assessed the osteoblastic response to micropatterned polymer–ceramic composite surfaces. In particular, the effect of topographic variations of composite poly(ε-caprolactone)/hydroxyapatite (PCL/HAp) films on viability, proliferation, migration and osteogenesis of fibroblastic and osteoblastic MC3T3-E1 cells was evaluated. To that end, three different micropatterned PCL/HAp films were compared: flat and textured, the latter of which included films comprising periodically arranged and randomly distributed oval topographic features 10 μm in diameter, 20 μm in separation and 10 μm in height, comparable to the dimensions of MC3T3-E1 cells. PCL/HAp films were fabricated by the combination of a bottom-up, soft chemical synthesis of the ceramic, nanoparticulate phase and a top-down, photolithographic technique for imprinting fine, microscale features on them. X-ray diffraction analysis indicated an isotropic orientation of both the polymeric chains and HAp crystallites in the composite samples. Biocompatibility tests indicated no significant decrease in their viability when grown on PCL/HAp films. Fibroblast proliferation and migration onto PCL/HAp films proceeded slower than on the control borosilicate glass, with the flat composite film fostering more cell migration activity than the films containing topographic features. The gene expression of seven analyzed osteogenic markers, including procollagen type I, osteocalcin, osteopontin, alkaline phosphatase, and the transcription factors Runx2 and TGFβ-1, was, however, consistently upregulated in cells grown on PCL/HAp films comprising periodically ordered topographic features, suggesting that the higher levels of symmetry of the topographic ordering impose a moderate mechanochemical stress on the adherent cells and thus promote a more favorable osteogenic response. The obtained results suggest that topography can be a more important determinant of the cell/surface interaction than the surface chemistry and/or stiffness as well as that the regularity of the distribution of topographic features can be a more important variable than the topographic features per se. PMID:25014232

Apollo 15 clastic materials and their relationship to local geologic features

NASA Technical Reports Server (NTRS)

Fruchter, J. S.; Stoeser, J. W.; Lindstrom, M. M.; Goles, G. G.

1973-01-01

Ninety sub-samples of Apollo 15 materials have been analyzed by instrumental neutron activation analysis techniques for as many as 21 elements. Soil and soil breccia compositions show considerable variation from station to station although at any given station the soils and soil breccias were compositionally very similar to one another. Mixing model calculations show that the station-to-station variations can be related to important local geologic features. These features include the Apennine Front, Hadley Rille and the ray from the craters Aristillus or Autolycus. Compositional similarities between soils and soil breccias at the Apollo 15 site indicate that the breccias and soils are related in some fundamental way, although the exact nature of this relationship is not yet fully understood.

Anatomic features involved in technical complexity of partial nephrectomy.

PubMed

Hou, Weibin; Yan, Weigang; Ji, Zhigang

2015-01-01

Nephrometry score systems, including RENAL nephrometry, preoperative aspects and dimensions used for an anatomical classification system, C-index, diameter-axial-polar nephrometry, contact surface area score, calculating resected and ischemized volume, renal tumor invasion index, surgical approach renal ranking score, zonal NePhRO score, and renal pelvic score, have been reviewed. Moreover, salient anatomic features like the perinephric fat and vascular variants also have been discussed. We then extract 7 anatomic characteristics, namely tumor size, spatial location, adjacency, exophytic/endophytic extension, vascular variants, pelvic anatomy, and perinephric fat as important features for partial nephrectomy. For novice surgeons, comprehensive and adequate anatomic consideration may help them in their early clinical practice. Copyright © 2015 Elsevier Inc. All rights reserved.

Assessing the performance of quantitative image features on early stage prediction of treatment effectiveness for ovary cancer patients: a preliminary investigation

NASA Astrophysics Data System (ADS)

Zargari, Abolfazl; Du, Yue; Thai, Theresa C.; Gunderson, Camille C.; Moore, Kathleen; Mannel, Robert S.; Liu, Hong; Zheng, Bin; Qiu, Yuchen

2018-02-01

The objective of this study is to investigate the performance of global and local features to better estimate the characteristics of highly heterogeneous metastatic tumours, for accurately predicting the treatment effectiveness of the advanced stage ovarian cancer patients. In order to achieve this , a quantitative image analysis scheme was developed to estimate a total of 103 features from three different groups including shape and density, Wavelet, and Gray Level Difference Method (GLDM) features. Shape and density features are global features, which are directly applied on the entire target image; wavelet and GLDM features are local features, which are applied on the divided blocks of the target image. To assess the performance, the new scheme was applied on a retrospective dataset containing 120 recurrent and high grade ovary cancer patients. The results indicate that the three best performed features are skewness, root-mean-square (rms) and mean of local GLDM texture, indicating the importance of integrating local features. In addition, the averaged predicting performance are comparable among the three different categories. This investigation concluded that the local features contains at least as copious tumour heterogeneity information as the global features, which may be meaningful on improving the predicting performance of the quantitative image markers for the diagnosis and prognosis of ovary cancer patients.

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.

Mspire-Simulator: LC-MS shotgun proteomic simulator for creating realistic gold standard data.

PubMed

Noyce, Andrew B; Smith, Rob; Dalgleish, James; Taylor, Ryan M; Erb, K C; Okuda, Nozomu; Prince, John T

2013-12-06

The most important step in any quantitative proteomic pipeline is feature detection (aka peak picking). However, generating quality hand-annotated data sets to validate the algorithms, especially for lower abundance peaks, is nearly impossible. An alternative for creating gold standard data is to simulate it with features closely mimicking real data. We present Mspire-Simulator, a free, open-source shotgun proteomic simulator that goes beyond previous simulation attempts by generating LC-MS features with realistic m/z and intensity variance along with other noise components. It also includes machine-learned models for retention time and peak intensity prediction and a genetic algorithm to custom fit model parameters for experimental data sets. We show that these methods are applicable to data from three different mass spectrometers, including two fundamentally different types, and show visually and analytically that simulated peaks are nearly indistinguishable from actual data. Researchers can use simulated data to rigorously test quantitation software, and proteomic researchers may benefit from overlaying simulated data on actual data sets.

Exploring the limits of identifying sub-pixel thermal features using ASTER TIR data

USGS Publications Warehouse

Vaughan, R.G.; Keszthelyi, L.P.; Davies, A.G.; Schneider, D.J.; Jaworowski, C.; Heasler, H.

2010-01-01

Understanding the characteristics of volcanic thermal emissions and how they change with time is important for forecasting and monitoring volcanic activity and potential hazards. Satellite instruments view volcanic thermal features across the globe at various temporal and spatial resolutions. Thermal features that may be a precursor to a major eruption, or indicative of important changes in an on-going eruption can be subtle, making them challenging to reliably identify with satellite instruments. The goal of this study was to explore the limits of the types and magnitudes of thermal anomalies that could be detected using satellite thermal infrared (TIR) data. Specifically, the characterization of sub-pixel thermal features with a wide range of temperatures is considered using ASTER multispectral TIR data. First, theoretical calculations were made to define a "thermal mixing detection threshold" for ASTER, which quantifies the limits of ASTER's ability to resolve sub-pixel thermal mixing over a range of hot target temperatures and % pixel areas. Then, ASTER TIR data were used to model sub-pixel thermal features at the Yellowstone National Park geothermal area (hot spring pools with temperatures from 40 to 90 ??C) and at Mount Erebus Volcano, Antarctica (an active lava lake with temperatures from 200 to 800 ??C). Finally, various sources of uncertainty in sub-pixel thermal calculations were quantified for these empirical measurements, including pixel resampling, atmospheric correction, and background temperature and emissivity assumptions.

Identifying sports videos using replay, text, and camera motion features

NASA Astrophysics Data System (ADS)

Kobla, Vikrant; DeMenthon, Daniel; Doermann, David S.

1999-12-01

Automated classification of digital video is emerging as an important piece of the puzzle in the design of content management systems for digital libraries. The ability to classify videos into various classes such as sports, news, movies, or documentaries, increases the efficiency of indexing, browsing, and retrieval of video in large databases. In this paper, we discuss the extraction of features that enable identification of sports videos directly from the compressed domain of MPEG video. These features include detecting the presence of action replays, determining the amount of scene text in vide, and calculating various statistics on camera and/or object motion. The features are derived from the macroblock, motion,and bit-rate information that is readily accessible from MPEG video with very minimal decoding, leading to substantial gains in processing speeds. Full-decoding of selective frames is required only for text analysis. A decision tree classifier built using these features is able to identify sports clips with an accuracy of about 93 percent.

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

PubMed Central

Guo, Hao; Zhang, Fan; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were selected to participate in an experiment. The proposed method achieved satisfactory classification accuracy, with an average of 91.60%. The abnormal brain regions included the bilateral precuneus, right parahippocampal gyrus\\hippocampus, right posterior cingulate gyrus, and other regions that are known to be important in Alzheimer's disease. Machine learning classification combining multiple features of a hyper-network of functional magnetic resonance imaging data in Alzheimer's disease obtains better classification performance. PMID:29209156

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

Tom, N.; Lawson, M.; Yu, Y. H.

WEC-Sim is a midfidelity numerical tool for modeling wave energy conversion devices. The code uses the MATLAB SimMechanics package to solve multibody dynamics and models wave interactions using hydrodynamic coefficients derived from frequency-domain boundary-element methods. This paper presents the new modeling features introduced in the latest release of WEC-Sim. The first feature discussed conversion of the fluid memory kernel to a state-space form. This enhancement offers a substantial computational benefit after the hydrodynamic body-to-body coefficients are introduced and the number of interactions increases exponentially with each additional body. Additional features include the ability to calculate the wave-excitation forces based onmore » the instantaneous incident wave angle, allowing the device to weathervane, as well as import a user-defined wave elevation time series. A review of the hydrodynamic theory for each feature is provided and the successful implementation is verified using test cases.« less

CT of abdominal tuberculosis

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

Epstein, B.M.; Mann, J.H.

1982-11-01

Intraabdominal tuberculosis (TB) presents with a wide variety of clinical and radiologic features. Besides the reported computed tomographic (CT) finding of high-density ascites in tuberculous peritonitis, this report describes additional CT features highly suggestive of abdominal tuberculosis in eight cases: (1) irregular soft-tissue densities in the omental area; (2) low-density masses surrounded by thick solid rims; (3) a disorganized appearance of soft-tissue densities, fluid, and bowel loops forming a poorly defined mass; (4) low-density lymph nodes with a multilocular appearance after intravenous contrast administration; and (5) possibly high-density ascites. The differential diagnosis of these features include lymphoma, various forms ofmore » peritonitis, peritoneal carcinomatosis, and peritoneal mesothelioma. It is important that the CT features of intraabdominal tuberculosis be recognized in order that laparotomy be avoided and less invasive procedures (e.g., laparoscopy, biopsy, or a trial of antituberculous therapy) be instituted.« less

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

Szymanski, J. J.; Brumby, Steven P.; Pope, P. A.

Feature extration from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. The tool used is the GENetic Imagery Exploitation (GENIE) software, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniquesmore » to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land-cover features including towns, grasslands, wild fire burn scars, and several types of forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.« less

Radiological features for the approach in trans-sphenoidal pituitary surgery.

PubMed

Twigg, Victoria; Carr, Simon D; Balakumar, Ramkishan; Sinha, Saurabh; Mirza, Showkat

2017-08-01

In order to perform trans-sphenoidal endoscopic pituitary surgery safely and efficiently it is important to identify anatomical and pituitary disease features on the pre-operative CT and MRI scans; thereby minimising the risk to surrounding structures and optimising outcomes. We aim to create a checklist to streamline pre-operative planning. We retrospectively reviewed pre-operative CT and MRI scans of 100 adults undergoing trans-sphenoidal endoscopic pituitary surgery. Radiological findings and their incidence included deviated nasal septum (62%), concha bullosa (32%), bony dehiscence of the carotid arteries (18%), sphenoid septation overlying the internal carotid artery (24% at the sella) and low lying CSF (32%). The mean distance of the sphenoid ostium to the skull base was 10 mm (range 2.7-17.6 mm). We also describe the 'teddy bear' sign which when present on an axial CT indicates the carotid arteries will be identifiable intra-operatively. There are significant variations in the anatomical and pituitary disease features between patients. We describe a number of features on pre-operative scans and have devised a checklist including a new 'teddy bear' sign to aid the surgeon in the anatomical assessment of patients undergoing trans-sphenoidal pituitary surgery.

Infrastructure features outperform environmental variables explaining rabbit abundance around motorways.

PubMed

Planillo, Aimara; Malo, Juan E

2018-01-01

Human disturbance is widespread across landscapes in the form of roads that alter wildlife populations. Knowing which road features are responsible for the species response and their relevance in comparison with environmental variables will provide useful information for effective conservation measures. We sampled relative abundance of European rabbits, a very widespread species, in motorway verges at regional scale, in an area with large variability in environmental and infrastructure conditions. Environmental variables included vegetation structure, plant productivity, distance to water sources, and altitude. Infrastructure characteristics were the type of vegetation in verges, verge width, traffic volume, and the presence of embankments. We performed a variance partitioning analysis to determine the relative importance of two sets of variables on rabbit abundance. Additionally, we identified the most important variables and their effects model averaging after model selection by AICc on hypothesis-based models. As a group, infrastructure features explained four times more variability in rabbit abundance than environmental variables, being the effects of the former critical in motorway stretches located in altered landscapes with no available habitat for rabbits, such as agricultural fields. Model selection and Akaike weights showed that verge width and traffic volume are the most important variables explaining rabbit abundance index, with positive and negative effects, respectively. In the light of these results, the response of species to the infrastructure can be modulated through the modification of motorway features, being some of them manageable in the design phase. The identification of such features leads to suggestions for improvement through low-cost corrective measures and conservation plans. As a general indication, keeping motorway verges less than 10 m wide will prevent high densities of rabbits and avoid the unwanted effects that rabbit populations can generate in some areas.

Discriminative analysis of lip motion features for speaker identification and speech-reading.

PubMed

Cetingül, H Ertan; Yemez, Yücel; Erzin, Engin; Tekalp, A Murat

2006-10-01

There have been several studies that jointly use audio, lip intensity, and lip geometry information for speaker identification and speech-reading applications. This paper proposes using explicit lip motion information, instead of or in addition to lip intensity and/or geometry information, for speaker identification and speech-reading within a unified feature selection and discrimination analysis framework, and addresses two important issues: 1) Is using explicit lip motion information useful, and, 2) if so, what are the best lip motion features for these two applications? The best lip motion features for speaker identification are considered to be those that result in the highest discrimination of individual speakers in a population, whereas for speech-reading, the best features are those providing the highest phoneme/word/phrase recognition rate. Several lip motion feature candidates have been considered including dense motion features within a bounding box about the lip, lip contour motion features, and combination of these with lip shape features. Furthermore, a novel two-stage, spatial, and temporal discrimination analysis is introduced to select the best lip motion features for speaker identification and speech-reading applications. Experimental results using an hidden-Markov-model-based recognition system indicate that using explicit lip motion information provides additional performance gains in both applications, and lip motion features prove more valuable in the case of speech-reading application.

Biased estimation of forest log characteristics using intersect diameters

Treesearch

Lisa J. Bate; Torolf R. Torgersen; Michael J. Wisdom; Edward O. Garton

2009-01-01

Logs are an important structural feature of forest ecosystems, and their abundance affects many resources and forest processes, including fire regimes, soil productivity, silviculture, carbon cycling, and wildlife habitat. Consequently, logs are often sampled to estimate their frequency, percent cover, volume, and weight. The line-intersect method (LIM) is one of the...

Public Policy in Gifted Education

ERIC Educational Resources Information Center

Gallagher, James J., Ed; Reis, Sally M., Ed.

2004-01-01

Raising some of the most challenging questions in the field, this call-to-arms focuses on the important services gifted programs provide, the potential crisis gifted educators face, and what must be done to keep the gifted child movement alive and well. Key features include: (1) James J. Gallagher's unflinching account of the issues that continue…

Using VRML for Teaching and Training in Industry.

ERIC Educational Resources Information Center

Dorner, Ralph; Schafer, Arno; Elcacho, Colette; Luckas, Volker

This paper shows how World Wide Web-based technology using VRML (Virtual Reality Modeling Language) can be applied in an industrial education and training context. Following an introduction to the importance of lifelong learning and training in industry, the state of the art of VRML is discussed, including its features and integration into the Web…

The EDUTECH Report. The Education Technology Newsletter for Faculty and Administrators, 1994-1995.

ERIC Educational Resources Information Center

EDUTECH Report, 1995

1995-01-01

This newsletter examines education technology issues of concern to school faculty and administrators. Regular features in each issue include educational technology news, a book review, and a question and answer column. The cover articles during this volume year are: "The Decision-Making Process: as Important as the Decision";…

The State of the Art in Information Handling. Operation PEP/Executive Information Systems.

ERIC Educational Resources Information Center

Summers, J. K.; Sullivan, J. E.

This document explains recent developments in computer science and information systems of interest to the educational manager. A brief history of computers is included, together with an examination of modern computers' capabilities. Various features of card, tape, and disk information storage systems are presented. The importance of time-sharing…

Mental Health Concerns in Williams Syndrome: Intervention Considerations and Illustrations from Case Examples

ERIC Educational Resources Information Center

Phillips, Kristin D.; Klein-Tasman, Bonita P.

2009-01-01

The refinement of the Williams syndrome phenotype has frequently included the study of behavioral and temperamental features common to individuals with this disorder. Within this line of research, the importance of evaluating incidence of psychopathology has been increasingly recognized, with studies consistently identifying an increased risk for…

Supervision of Marriage and Family Counselors. ERIC Digest.

ERIC Educational Resources Information Center

Cryder, Annette Petro; And Others

This digest focuses on issues of importance to the supervision of marriage and family counselors. A brief overview notes that the defining hallmark of marriage and family supervision has been a systemic orientation. Other distinguishing features include a reliance on live forms of supervision, and the viewing of ethical issues within larger…

Visible and near-IR spectral reflectance of geologically important materials: A short review

NASA Technical Reports Server (NTRS)

Singer, R. B.

1982-01-01

Examples of reflectance spectra are presented and discussed for various mineral groups including pyroxenes, olivene, phylosilicates, amphiboles, feldspars, oxides and hydroxides, carbonates, and mixtures of minerals. The physical sources of some spectral features are also reviewed such as charge transfer and conduction bands, crystal field absorptions, and vibrational absorptions.

Setting the Record Straight. The Truth About Fad Diets.

ERIC Educational Resources Information Center

Wheat Foods Council, Parker, CO.

The Setting the Record Straight information packet presents facts to set the record straight about nutrition and debunk fad diets. The kit features materials designed to communicate the importance of balanced eating. Materials include: a time line of fad diets; four reproducible fad diet book review handouts that show the misleading claims rampant…

Broadcast Journalism; An Introduction to News Writing.

ERIC Educational Resources Information Center

Hall, Mark W.

The important features of writing news for radio and television are covered in this book. Ways to write colorful, accurate, and timely stories are explained with the emphasis on the differences between broadcast and newspaper stories. Other subjects treated are sources of news (including explanations of how the Associated Press copy works and how…

Reconsidering the Education of Gifted Young Children with the Reggio Emilia Approach

ERIC Educational Resources Information Center

Lai, Yuan

2009-01-01

While conceptualizations of giftedness have been broadened to include many forms of giftedness, a reconceptualization of gifted programs has not followed. The paper argues that the Reggio Emilia approach to early childhood education, combining important features of the fields of early childhood education and gifted education, is a good fit for…

Cellular Responses Evoked by Different Surface Characteristics of Intraosseous Titanium Implants

PubMed Central

Feller, Liviu; Jadwat, Yusuf; Khammissa, Razia A. G.; Meyerov, Robin; Lemmer, Johan

2015-01-01

The properties of biomaterials, including their surface microstructural topography and their surface chemistry or surface energy/wettability, affect cellular responses such as cell adhesion, proliferation, and migration. The nanotopography of moderately rough implant surfaces enhances the production of biological mediators in the peri-implant microenvironment with consequent recruitment of differentiating osteogenic cells to the implant surface and stimulates osteogenic maturation. Implant surfaces with moderately rough topography and with high surface energy promote osteogenesis, increase the ratio of bone-to-implant contact, and increase the bonding strength of the bone to the implant at the interface. Certain features of implant surface chemistry are also important in enhancing peri-implant bone wound healing. It is the purpose of this paper to review some of the more important features of titanium implant surfaces which have an impact on osseointegration. PMID:25767803

Classifying transcription factor targets and discovering relevant biological features

PubMed Central

Holloway, Dustin T; Kon, Mark; DeLisi, Charles

2008-01-01

Background An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties. Principal Findings (1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4). (2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression. (3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties. (4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter. Conclusion Postprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite. Reviewers This article was reviewed by Igor Jouline, Todd Mockler(nominated by Valerian Dolja), and Sandor Pongor. PMID:18513408

Dust storms on Mars: Considerations and simulations

NASA Technical Reports Server (NTRS)

Greeley, R.; White, B. R.; Pollack, J. B.; Iverson, J. D.; Leach, R. N.

1977-01-01

Aeolian processes are important in modifying the surface of Mars at present, and appear to have been significant in the geological past. Aeolian activity includes local and global dust storms, the formation of erosional features such as yardangs and depositional features such as sand dunes, and the erosion of rock and soil. As a means of understanding aeolian processes on Mars, an investigation is in progress that includes laboratory simulations, field studies of earth analogs, and interpretation of spacecraft data. This report describes the Martian Surface Wind Tunnel, an experimental facility established at NASA-Ames Research Center, and presents some results of the general investigation. Experiments dealing with wind speeds and other conditions required for the initiation of particle movement on Mars are described and considerations are given to the resulting effectiveness of aeolian erosion.

TRICCS: A proposed teleoperator/robot integrated command and control system for space applications

NASA Technical Reports Server (NTRS)

Will, R. W.

1985-01-01

Robotic systems will play an increasingly important role in space operations. An integrated command and control system based on the requirements of space-related applications and incorporating features necessary for the evolution of advanced goal-directed robotic systems is described. These features include: interaction with a world model or domain knowledge base, sensor feedback, multiple-arm capability and concurrent operations. The system makes maximum use of manual interaction at all levels for debug, monitoring, and operational reliability. It is shown that the robotic command and control system may most advantageously be implemented as packages and tasks in Ada.

Chondritic Meteorites: Nebular and Parent-Body Formation Processes

NASA Technical Reports Server (NTRS)

Rubin, Alan E.; Lindstrom, David (Technical Monitor)

2002-01-01

It is important to identify features in chondrites that formed as a result of parent-body modification in order to disentangle nebular and asteroidal processes. However, this task is difficult because unmetamorphosed chondritic meteorites are mixtures of diverse components including various types of chondrules, chondrule fragments, refractory and mafic inclusions, metal-sulfide grains and fine-grained matrix material. Shocked chondrites can contain melt pockets, silicate-darkened material, metal veins, silicate melt veins, and impact-melt-rock clasts. This grant paid for several studies that went far in helping to distinguish primitive nebular features from those produced during asteroidal modification processes.

New Optical Transforms For Statistical Image Recognition

NASA Astrophysics Data System (ADS)

Lee, Sing H.

1983-12-01

In optical implementation of statistical image recognition, new optical transforms on large images for real-time recognition are of special interest. Several important linear transformations frequently used in statistical pattern recognition have now been optically implemented, including the Karhunen-Loeve transform (KLT), the Fukunaga-Koontz transform (FKT) and the least-squares linear mapping technique (LSLMT).1-3 The KLT performs principle components analysis on one class of patterns for feature extraction. The FKT performs feature extraction for separating two classes of patterns. The LSLMT separates multiple classes of patterns by maximizing the interclass differences and minimizing the intraclass variations.

A real-time TV logo tracking method using template matching

NASA Astrophysics Data System (ADS)

Li, Zhi; Sang, Xinzhu; Yan, Binbin; Leng, Junmin

2012-11-01

A fast and accurate TV Logo detection method is presented based on real-time image filtering, noise eliminating and recognition of image features including edge and gray level information. It is important to accurately extract the optical template using the time averaging method from the sample video stream, and then different templates are used to match different logos in separated video streams with different resolution based on the topology features of logos. 12 video streams with different logos are used to verify the proposed method, and the experimental result demonstrates that the achieved accuracy can be up to 99%.

Autoimmune pernicious anaemia as a cause of collapse, heart failure and marked panyctopaenia in a young patient.

PubMed

Carey, Justin; Hack, Ebru

2012-05-08

A 35-year-old woman with a history of vitiligo, hypothyroidism and amenorrhoea presented with collapse and clinical features of cardiac failure. Laboratory investigations revealed pancytopaenia, the cause of which was found to be vitamin B12 deficiency due to pernicious anaemia. Treatment with intramuscular hydroxycobalamin was commenced and the patient improved steadily with concomitant improvement in her haematological indices. Clinical features of pernicious anaemia which can include marked pancytopaenia, diagnostic approach, associated conditions and approach to treatment are discussed. The importance of surveillance for gastrointestinal malignancy is emphasised.

Autoimmune pernicious anaemia as a cause of collapse, heart failure and marked panyctopaenia in a young patient

PubMed Central

Carey, Justin; Hack, Ebru

2012-01-01

A 35-year-old woman with a history of vitiligo, hypothyroidism and amenorrhoea presented with collapse and clinical features of cardiac failure. Laboratory investigations revealed pancytopaenia, the cause of which was found to be vitamin B12 deficiency due to pernicious anaemia. Treatment with intramuscular hydroxycobalamin was commenced and the patient improved steadily with concomitant improvement in her haematological indices. Clinical features of pernicious anaemia which can include marked pancytopaenia, diagnostic approach, associated conditions and approach to treatment are discussed. The importance of surveillance for gastrointestinal malignancy is emphasised. PMID:22605831

Crop identification of SAR data using digital textural analysis

NASA Technical Reports Server (NTRS)

Nuesch, D. R.

1983-01-01

After preprocessing SEASAT SAR data which included slant to ground range transformation, registration to LANDSAT MSS data and appropriate filtering of the raw SAR data to minimize coherent speckle, textural features were developed based upon the spatial gray level dependence method (SGLDM) to compute entropy and inertia as textural measures. It is indicated that the consideration of texture features are very important in SAR data analysis. The SEASAT SAR data are useful for the improvement of field boundary definitions and for an earlier season estimate of corn and soybean area location than is supported by LANDSAT alone.

The external morphology of adult female Egrasilus labracis as shown using hexamethyldisilazane treated, uncoated specimens for scanning electron microscopy.

PubMed

Murray, Harry M; Hill, Stephen J; Ang, Keng P

2016-07-01

The description and application of a modified Scanning Electron Microscope preparation technique using hexamethyldisilazane for small parasitic copepods was demonstrated though a high resolution depiction of individuals of Ergasilus labracis sampled from three spined stickleback (Gasterosteus aculeatus) in Bay D'Espoir, Newfoundland during summer 2015 and from archival samples retrieved from Atlantic salmon par (Salmo salar) stored at the Atlantic reference centre, St. Andrews, New Brunswick. The specimens were very well preserved showing high quality detail of important features and verifying those previously described using light microscopy by Hogans. Additionally the technique allowed excellent in situ demonstrations of mouth parts, swimming legs, and unusual and previously undescribed features of the second antenna including prominent striations and pore-like structures found to define the claw. It is thought that this technique will become a quick and efficient tool for describing important taxonomic features of small parasitic copepods like E. labracis or other similar small aquatic organisms. Microsc. Res. Tech. 79:657-663, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Efficient iris recognition by characterizing key local variations.

PubMed

Ma, Li; Tan, Tieniu; Wang, Yunhong; Zhang, Dexin

2004-06-01

Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.

Processing technology for high efficiency silicon solar cells

NASA Technical Reports Server (NTRS)

Spitzer, M. B.; Keavney, C. J.

1985-01-01

Recent advances in silicon solar cell processing have led to attainment of conversion efficiency approaching 20%. The basic cell design is investigated and features of greatest importance to achievement of 20% efficiency are indicated. Experiments to separately optimize high efficiency design features in test structures are discussed. The integration of these features in a high efficiency cell is examined. Ion implantation has been used to achieve optimal concentrations of emitter dopant and junction depth. The optimization reflects the trade-off between high sheet conductivity, necessary for high fill factor, and heavy doping effects, which must be minimized for high open circuit voltage. A second important aspect of the design experiments is the development of a passivation process to minimize front surface recombination velocity. The manner in which a thin SiO2 layer may be used for this purpose is indicated without increasing reflection losses, if the antireflection coating is properly designed. Details are presented of processing intended to reduce recombination at the contact/Si interface. Data on cell performance (including CZ and ribbon) and analysis of loss mechanisms are also presented.

A Features Selection for Crops Classification

NASA Astrophysics Data System (ADS)

Liu, Yifan; Shao, Luyi; Yin, Qiang; Hong, Wen

2016-08-01

The components of the polarimetric target decomposition reflect the differences of target since they linked with the scattering properties of the target and can be imported into SVM as the classification features. The result of decomposition usually concentrate on part of the components. Selecting a combination of components can reduce the features that importing into the SVM. The features reduction can lead to less calculation and targeted classification of one target when we classify a multi-class area. In this research, we import different combinations of features into the SVM and find a better combination for classification with a data of AGRISAR.

Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness.

PubMed

Balcarras, Matthew; Ardid, Salva; Kaping, Daniel; Everling, Stefan; Womelsdorf, Thilo

2016-02-01

Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.

Human red blood cell recognition enhancement with three-dimensional morphological features obtained by digital holographic imaging

NASA Astrophysics Data System (ADS)

Jaferzadeh, Keyvan; Moon, Inkyu

2016-12-01

The classification of erythrocytes plays an important role in the field of hematological diagnosis, specifically blood disorders. Since the biconcave shape of red blood cell (RBC) is altered during the different stages of hematological disorders, we believe that the three-dimensional (3-D) morphological features of erythrocyte provide better classification results than conventional two-dimensional (2-D) features. Therefore, we introduce a set of 3-D features related to the morphological and chemical properties of RBC profile and try to evaluate the discrimination power of these features against 2-D features with a neural network classifier. The 3-D features include erythrocyte surface area, volume, average cell thickness, sphericity index, sphericity coefficient and functionality factor, MCH and MCHSD, and two newly introduced features extracted from the ring section of RBC at the single-cell level. In contrast, the 2-D features are RBC projected surface area, perimeter, radius, elongation, and projected surface area to perimeter ratio. All features are obtained from images visualized by off-axis digital holographic microscopy with a numerical reconstruction algorithm, and four categories of biconcave (doughnut shape), flat-disc, stomatocyte, and echinospherocyte RBCs are interested. Our experimental results demonstrate that the 3-D features can be more useful in RBC classification than the 2-D features. Finally, we choose the best feature set of the 2-D and 3-D features by sequential forward feature selection technique, which yields better discrimination results. We believe that the final feature set evaluated with a neural network classification strategy can improve the RBC classification accuracy.

Hallucinations: A Systematic Review of Points of Similarity and Difference Across Diagnostic Classes

PubMed Central

Waters, Flavie; Fernyhough, Charles

2017-01-01

Hallucinations constitute one of the 5 symptom domains of psychotic disorders in DSM-5, suggesting diagnostic significance for that group of disorders. Although specific featural properties of hallucinations (negative voices, talking in the third person, and location in external space) are no longer highlighted in DSM, there is likely a residual assumption that hallucinations in schizophrenia can be identified based on these candidate features. We investigated whether certain featural properties of hallucinations are specifically indicative of schizophrenia by conducting a systematic review of studies showing direct comparisons of the featural and clinical characteristics of (auditory and visual) hallucinations among 2 or more population groups (one of which included schizophrenia). A total of 43 articles were reviewed, which included hallucinations in 4 major groups (nonclinical groups, drug- and alcohol-related conditions, medical and neurological conditions, and psychiatric disorders). The results showed that no single hallucination feature or characteristic uniquely indicated a diagnosis of schizophrenia, with the sole exception of an age of onset in late adolescence. Among the 21 features of hallucinations in schizophrenia considered here, 95% were shared with other psychiatric disorders, 85% with medical/neurological conditions, 66% with drugs and alcohol conditions, and 52% with the nonclinical groups. Additional differences rendered the nonclinical groups somewhat distinctive from clinical disorders. Overall, when considering hallucinations, it is inadvisable to give weight to the presence of any featural properties alone in making a schizophrenia diagnosis. It is more important to focus instead on the co-occurrence of other symptoms and the value of hallucinations as an indicator of vulnerability. PMID:27872259

Accuracy of computed tomographic features in differentiating intestinal tuberculosis from Crohn's disease: a systematic review with meta-analysis.

PubMed

Kedia, Saurabh; Sharma, Raju; Sreenivas, Vishnubhatla; Madhusudhan, Kumble Seetharama; Sharma, Vishal; Bopanna, Sawan; Pratap Mouli, Venigalla; Dhingra, Rajan; Yadav, Dawesh Prakash; Makharia, Govind; Ahuja, Vineet

2017-04-01

Abdominal computed tomography (CT) can noninvasively image the entire gastrointestinal tract and assess extraintestinal features that are important in differentiating Crohn's disease (CD) and intestinal tuberculosis (ITB). The present meta-analysis pooled the results of all studies on the role of CT abdomen in differentiating between CD and ITB. We searched PubMed and Embase for all publications in English that analyzed the features differentiating between CD and ITB on abdominal CT. The features included comb sign, necrotic lymph nodes, asymmetric bowel wall thickening, skip lesions, fibrofatty proliferation, mural stratification, ileocaecal area, long segment, and left colonic involvements. Sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (DOR) were calculated for all the features. Symmetric receiver operating characteristic curve was plotted for features present in >3 studies. Heterogeneity and publication bias was assessed and sensitivity analysis was performed by excluding studies that compared features on conventional abdominal CT instead of CT enterography (CTE). We included 6 studies (4 CTE, 1 conventional abdominal CT, and 1 CTE+conventional abdominal CT) involving 417 and 195 patients with CD and ITB, respectively. Necrotic lymph nodes had the highest diagnostic accuracy (sensitivity, 23%; specificity, 100%; DOR, 30.2) for ITB diagnosis, and comb sign (sensitivity, 82%; specificity, 81%; DOR, 21.5) followed by skip lesions (sensitivity, 86%; specificity, 74%; DOR, 16.5) had the highest diagnostic accuracy for CD diagnosis. On sensitivity analysis, the diagnostic accuracy of other features excluding asymmetric bowel wall thickening remained similar. Necrotic lymph nodes and comb sign on abdominal CT had the best diagnostic accuracy in differentiating CD and ITB.

Setting conservation targets for sandy beach ecosystems

NASA Astrophysics Data System (ADS)

Harris, Linda; Nel, Ronel; Holness, Stephen; Sink, Kerry; Schoeman, David

2014-10-01

Representative and adequate reserve networks are key to conserving biodiversity. This begs the question, how much of which features need to be placed in protected areas? Setting specifically-derived conservation targets for most ecosystems is common practice; however, this has never been done for sandy beaches. The aims of this paper, therefore, are to propose a methodology for setting conservation targets for sandy beach ecosystems; and to pilot the proposed method using data describing biodiversity patterns and processes from microtidal beaches in South Africa. First, a classification scheme of valued features of beaches is constructed, including: biodiversity features; unique features; and important processes. Second, methodologies for setting targets for each feature under different data-availability scenarios are described. From this framework, targets are set for features characteristic of microtidal beaches in South Africa, as follows. 1) Targets for dune vegetation types were adopted from a previous assessment, and ranged 19-100%. 2) Targets for beach morphodynamic types (habitats) were set using species-area relationships (SARs). These SARs were derived from species richness data from 142 sampling events around the South African coast (extrapolated to total theoretical species richness estimates using previously-established species-accumulation curve relationships), plotted against the area of the beach (calculated from Google Earth imagery). The species-accumulation factor (z) was 0.22, suggesting a baseline habitat target of 27% is required to protect 75% of the species. This baseline target was modified by heuristic principles, based on habitat rarity and threat status, with final values ranging 27-40%. 3) Species targets were fixed at 20%, modified using heuristic principles based on endemism, threat status, and whether or not beaches play an important role in the species' life history, with targets ranging 20-100%. 4) Targets for processes and 5) important assemblages were set at 50%, following other studies. 6) Finally, a target for an outstanding feature (the Alexandria dunefield) was set at 80% because of its national, international and ecological importance. The greatest shortfall in the current target-setting process is in the lack of empirical models describing the key beach processes, from which robust ecological thresholds can be derived. As for many other studies, our results illustrate that the conservation target of 10% for coastal and marine systems proposed by the Convention on Biological Diversity is too low to conserve sandy beaches and their biota.

Parkinson's Disease Subtypes in the Oxford Parkinson Disease Centre (OPDC) Discovery Cohort.

PubMed

Lawton, Michael; Baig, Fahd; Rolinski, Michal; Ruffman, Claudio; Nithi, Kannan; May, Margaret T; Ben-Shlomo, Yoav; Hu, Michele T M

2015-01-01

Within Parkinson's there is a spectrum of clinical features at presentation which may represent sub-types of the disease. However there is no widely accepted consensus of how best to group patients. Use a data-driven approach to unravel any heterogeneity in the Parkinson's phenotype in a well-characterised, population-based incidence cohort. 769 consecutive patients, with mean disease duration of 1.3 years, were assessed using a broad range of motor, cognitive and non-motor metrics. Multiple imputation was carried out using the chained equations approach to deal with missing data. We used an exploratory and then a confirmatory factor analysis to determine suitable domains to include within our cluster analysis. K-means cluster analysis of the factor scores and all the variables not loading into a factor was used to determine phenotypic subgroups. Our factor analysis found three important factors that were characterised by: psychological well-being features; non-tremor motor features, such as posture and rigidity; and cognitive features. Our subsequent five cluster model identified groups characterised by (1) mild motor and non-motor disease (25.4%), (2) poor posture and cognition (23.3%), (3) severe tremor (20.8%), (4) poor psychological well-being, RBD and sleep (18.9%), and (5) severe motor and non-motor disease with poor psychological well-being (11.7%). Our approach identified several Parkinson's phenotypic sub-groups driven by largely dopaminergic-resistant features (RBD, impaired cognition and posture, poor psychological well-being) that, in addition to dopaminergic-responsive motor features may be important for studying the aetiology, progression, and medication response of early Parkinson's.

The National Geographic Names Data Base: Phase II instructions

USGS Publications Warehouse

Orth, Donald J.; Payne, Roger L.

1987-01-01

not recorded on topographic maps be added. The systematic collection of names from other sources, including maps, charts, and texts, is termed Phase II. In addition, specific types of features not compiled during Phase I are encoded and added to the data base. Other names of importance to researchers and users, such as historical and variant names, are also included. The rules and procedures for Phase II research, compilation, and encoding are contained in this publication.

Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features

PubMed Central

Xia, Junfeng; Yue, Zhenyu; Di, Yunqiang; Zhu, Xiaolei; Zheng, Chun-Hou

2016-01-01

The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spots in protein interfaces. PMID:26934646

Personalized features for attention detection in children with Attention Deficit Hyperactivity Disorder.

PubMed

Fahimi, Fatemeh; Guan, Cuntai; Wooi Boon Goh; Kai Keng Ang; Choon Guan Lim; Tih Shih Lee

2017-07-01

Measuring attention from electroencephalogram (EEG) has found applications in the treatment of Attention Deficit Hyperactivity Disorder (ADHD). It is of great interest to understand what features in EEG are most representative of attention. Intensive research has been done in the past and it has been proven that frequency band powers and their ratios are effective features in detecting attention. However, there are still unanswered questions, like, what features in EEG are most discriminative between attentive and non-attentive states? Are these features common among all subjects or are they subject-specific and must be optimized for each subject? Using Mutual Information (MI) to perform subject-specific feature selection on a large data set including 120 ADHD children, we found that besides theta beta ratio (TBR) which is commonly used in attention detection and neurofeedback, the relative beta power and theta/(alpha+beta) (TBAR) are also equally significant and informative for attention detection. Interestingly, we found that the relative theta power (which is also commonly used) may not have sufficient discriminative information itself (it is informative only for 3.26% of ADHD children). We have also demonstrated that although these features (relative beta power, TBR and TBAR) are the most important measures to detect attention on average, different subjects have different set of most discriminative features.

Geospatial Analytics in Retail Site Selection and Sales Prediction.

PubMed

Ting, Choo-Yee; Ho, Chiung Ching; Yee, Hui Jia; Matsah, Wan Razali

2018-03-01

Studies have shown that certain features from geography, demography, trade area, and environment can play a vital role in retail site selection, largely due to the impact they asserted on retail performance. Although the relevant features could be elicited by domain experts, determining the optimal feature set can be intractable and labor-intensive exercise. The challenges center around (1) how to determine features that are important to a particular retail business and (2) how to estimate retail sales performance given a new location? The challenges become apparent when the features vary across time. In this light, this study proposed a nonintervening approach by employing feature selection algorithms and subsequently sales prediction through similarity-based methods. The results of prediction were validated by domain experts. In this study, data sets from different sources were transformed and aggregated before an analytics data set that is ready for analysis purpose could be obtained. The data sets included data about feature location, population count, property type, education status, and monthly sales from 96 branches of a telecommunication company in Malaysia. The finding suggested that (1) optimal retail performance can only be achieved through fulfillment of specific location features together with the surrounding trade area characteristics and (2) similarity-based method can provide solution to retail sales prediction.

Avitourism and Australian Important Bird and Biodiversity Areas.

PubMed

Steven, Rochelle; Morrison, Clare; Arthur, J Michael; Castley, J Guy

2015-01-01

Formal protected areas will not provide adequate protection to conserve all biodiversity, and are not always designated using systematic or strategic criteria. Using a systematic process, the Important Bird and Biodiversity Area (IBA) network was designed to highlight areas of conservation significance for birds (i.e. IBA trigger species), and more recently general biodiversity. Land use activities that take place in IBAs are diverse, including consumptive and non-consumptive activities. Avitourism in Australia, generally a non-consumptive activity, is reliant on the IBA network and the birds IBAs aim to protect. However, companies tend not to mention IBAs in their marketing. Furthermore, avitourism, like other nature-based tourism has the potential to be both a threatening process as well as a conservation tool. We aimed to assess the current use of IBAs among Australian-based avitour companies' marketing, giving some indication of which IBAs are visited by avitourists on organised tours. We reviewed online avitour itineraries, recorded sites featuring in descriptions of avitours and which IBA trigger species are used to sell those tours. Of the 209 avitours reviewed, Queensland is the most featured state (n = 59 tours), and 73% feature at least one IBA. Daintree (n = 22) and Bruny Island (n = 17) IBAs are the most popular, nationally. Trigger species represent 34% (n = 254 out of 747) of species used in avitour descriptions. The most popular trigger species' are wetland species including; Brolga (n = 37), Black-necked Stork (n = 30) and Magpie Goose (n = 27). Opportunities exist to increase collaboration between avitour companies and IBA stakeholders. Our results can provide guidance for managing sustainability of the avitourism industry at sites that feature heavily in avitour descriptions and enhance potential cooperation between avitour companies, IBA stakeholders and bird conservation organisations.

Avitourism and Australian Important Bird and Biodiversity Areas

PubMed Central

Steven, Rochelle; Morrison, Clare; Arthur, J. Michael; Castley, J. Guy

2015-01-01

Formal protected areas will not provide adequate protection to conserve all biodiversity, and are not always designated using systematic or strategic criteria. Using a systematic process, the Important Bird and Biodiversity Area (IBA) network was designed to highlight areas of conservation significance for birds (i.e. IBA trigger species), and more recently general biodiversity. Land use activities that take place in IBAs are diverse, including consumptive and non-consumptive activities. Avitourism in Australia, generally a non-consumptive activity, is reliant on the IBA network and the birds IBAs aim to protect. However, companies tend not to mention IBAs in their marketing. Furthermore, avitourism, like other nature-based tourism has the potential to be both a threatening process as well as a conservation tool. We aimed to assess the current use of IBAs among Australian-based avitour companies’ marketing, giving some indication of which IBAs are visited by avitourists on organised tours. We reviewed online avitour itineraries, recorded sites featuring in descriptions of avitours and which IBA trigger species are used to sell those tours. Of the 209 avitours reviewed, Queensland is the most featured state (n = 59 tours), and 73% feature at least one IBA. Daintree (n = 22) and Bruny Island (n = 17) IBAs are the most popular, nationally. Trigger species represent 34% (n = 254 out of 747) of species used in avitour descriptions. The most popular trigger species’ are wetland species including; Brolga (n = 37), Black-necked Stork (n = 30) and Magpie Goose (n = 27). Opportunities exist to increase collaboration between avitour companies and IBA stakeholders. Our results can provide guidance for managing sustainability of the avitourism industry at sites that feature heavily in avitour descriptions and enhance potential cooperation between avitour companies, IBA stakeholders and bird conservation organisations. PMID:26701779

Computer-aided-diagnosis (CAD) for colposcopy

NASA Astrophysics Data System (ADS)

Lange, Holger; Ferris, Daron G.

2005-04-01

Uterine cervical cancer is the second most common cancer among women worldwide. Colposcopy is a diagnostic method, whereby a physician (colposcopist) visually inspects the lower genital tract (cervix, vulva and vagina), with special emphasis on the subjective appearance of metaplastic epithelium comprising the transformation zone on the cervix. Cervical cancer precursor lesions and invasive cancer exhibit certain distinctly abnormal morphologic features. Lesion characteristics such as margin; color or opacity; blood vessel caliber, intercapillary spacing and distribution; and contour are considered by colposcopists to derive a clinical diagnosis. Clinicians and academia have suggested and shown proof of concept that automated image analysis of cervical imagery can be used for cervical cancer screening and diagnosis, having the potential to have a direct impact on improving women"s health care and reducing associated costs. STI Medical Systems is developing a Computer-Aided-Diagnosis (CAD) system for colposcopy -- ColpoCAD. At the heart of ColpoCAD is a complex multi-sensor, multi-data and multi-feature image analysis system. A functional description is presented of the envisioned ColpoCAD system, broken down into: Modality Data Management System, Image Enhancement, Feature Extraction, Reference Database, and Diagnosis and directed Biopsies. The system design and development process of the image analysis system is outlined. The system design provides a modular and open architecture built on feature based processing. The core feature set includes the visual features used by colposcopists. This feature set can be extended to include new features introduced by new instrument technologies, like fluorescence and impedance, and any other plausible feature that can be extracted from the cervical data. Preliminary results of our research on detecting the three most important features: blood vessel structures, acetowhite regions and lesion margins are shown. As this is a new and very complex field in medical image processing, the hope is that this paper can provide a framework and basis to encourage and facilitate collaboration and discussion between industry, academia, and medical practitioners.

SU-F-R-14: PET Based Radiomics to Predict Outcomes in Patients with Hodgkin Lymphoma

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

Lee, J; Aristophanous, M; Akhtari, M

Purpose: To identify PET-based radiomics features associated with high refractory/relapsed disease risk for Hodgkin lymphoma patients. Methods: A total of 251 Hodgkin lymphoma patients including 19 primary refractory and 9 relapsed patients were investigated. All patients underwent an initial pre-treatment diagnostic FDG PET/CT scan. All cancerous lymph node regions (ROIs) were delineated by an experienced physician based on thresholding each volume of disease in the anatomical regions to SUV>2.5. We extracted 122 image features and evaluated the effect of ROI selection (the largest ROI, the ROI with highest mean SUV, merged ROI, and a single anatomic region [e.g. mediastinum]) onmore » classification accuracy. Random forest was used as a classifier and ROC analysis was used to assess the relationship between selected features and patient’s outcome status. Results: Each patient had between 1 and 9 separate ROIs, with much intra-patient variability in PET features. The best model, which used features from a single anatomic region (the mediastinal ROI, only volumes>5cc: 169 patients with 12 primary refractory) had a classification accuracy of 80.5% for primary refractory disease. The top five features, based on Gini index, consist of shape features (max 3D-diameter and volume) and texture features (correlation and information measure of correlation1&2). In the ROC analysis, sensitivity and specificity of the best model were 0.92 and 0.80, respectively. The area under the ROC (AUC) and the accuracy were 0.86 and 0.86, respectively. The classification accuracy was less than 60% for other ROI models or when ROIs less than 5cc were included. Conclusion: This study showed that PET-based radiomics features from the mediastinal lymph region are associated with primary refractory disease and therefore may play an important role in predicting outcomes in Hodgkin lymphoma patients. These features could be additive beyond baseline tumor and clinical characteristics, and may warrant more aggressive treatment.« less

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

PubMed

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216. © 2017 International Society for Magnetic Resonance in Medicine.

MHC Class I Immune Proteins Are Critical for Hippocampus-Dependent Memory and Gate NMDAR-Dependent Hippocampal Long-Term Depression

ERIC Educational Resources Information Center

Nelson, P. Austin; Sage, Jennifer R.; Wood, Suzanne C.; Davenport, Christopher M.; Anagnostaras, Stephan G.; Boulanger, Lisa M.

2013-01-01

Memory impairment is a common feature of conditions that involve changes in inflammatory signaling in the brain, including traumatic brain injury, infection, neurodegenerative disorders, and normal aging. However, the causal importance of inflammatory mediators in cognitive impairments in these conditions remains unclear. Here we show that…

A Cost-Effective Optical Device for the Characterization of Liquid Crystals

ERIC Educational Resources Information Center

Millier, Brian; Aleman Milán, Gianna

2014-01-01

The design and construction of an apparatus to measure the optical birefringence of a liquid crystal is described. The instrument also includes temperature control and monitoring circuitry to allow for the measurement of the nematic-to-isotropic phase transition temperature. An important feature of this design is that the students are able to…

A Fruiting Body Tissue Method for Efficient Agrobacterium-Mediated Transformation of Agaricus bisporus

PubMed Central

Chen, Xi; Stone, Michelle; Schlagnhaufer, Carl; Romaine, C. Peter

2000-01-01

We describe a modified Agrobacterium-mediated method for the efficient transformation of Agaricus bisporus. Salient features of this procedure include cocultivation of Agrobacterium and fruiting body gill tissue and use of a vector with a homologous promoter. This method offers new prospects for the genetic manipulation of this commercially important mushroom species. PMID:11010906

The Diagnosis and Understanding of Apraxia of Speech: Why Including Neurodegenerative Etiologies May Be Important

ERIC Educational Resources Information Center

Duffy, Joseph R.; Josephs, Keith A.

2012-01-01

Purpose: To discuss apraxia of speech (AOS) as it occurs in neurodegenerative disease (progressive AOS [PAOS]) and how its careful study may contribute to general concepts of AOS and help refine its diagnostic criteria. Method: The article summarizes our current understanding of the clinical features and neuroanatomical and pathologic correlates…

Controversies in the Classroom: A Radical Teacher Reader. Teaching for Social Justice Series

ERIC Educational Resources Information Center

Entin, Joseph, Ed.; Rosen, Robert C., Ed.; Vogt, Leonard, Ed.

2008-01-01

"Controversies in the Classroom" features the most important and exciting writing from the past 15 years of "Radical Teacher" magazine. This is a must-read for all teachers who are committed to creative pedagogy and social justice. Contributors include: Bernadette Anand, Nancy Barnes, Lilia I. Bartolome, Bill Bigelow, Lawrence Blum, Marjorie Feld,…

Building a Reference Resolution System Using Human Language Processing for Inspiration

ERIC Educational Resources Information Center

Watters, Shana Kay

2010-01-01

For over 30 years, reference resolution, the process of determining what a noun phrase including a pronoun refers to in written and spoken language, has been an important and on-going area of research. Most existing pronominal reference resolution algorithms and systems are designed to use syntactic information and surface features (e.g. number…

Can Dynamic Visualizations with Variable Control Enhance the Acquisition of Intuitive Knowledge?

ERIC Educational Resources Information Center

Wichmann, Astrid; Timpe, Sebastian

2015-01-01

An important feature of inquiry learning is to take part in science practices including exploring variables and testing hypotheses. Computer-based dynamic visualizations have the potential to open up various exploration possibilities depending on the level of learner control. It is assumed that variable control, e.g., by changing parameters of a…

Developing an Effective Tool for Teaching Teens about Workplace Safety

ERIC Educational Resources Information Center

Miara, Christine; Gallagher, Susan; Bush, Diane; Dewer, Robin

2003-01-01

Paid employment is an important feature of adolescent life. Too often, it has negative health consequences, including more than 200,000 workplace injuries to 14 to 17 year olds every year. Training teens about occupational safety is part of an overall strategy to address this problem. When the project described in this article began, there were…

The Dani of West Irian. An Ethnographic Companion to the Film "Dead Birds". Module 2.

ERIC Educational Resources Information Center

Heider, Karl G.

The purpose of this study guide is to provide students of anthropology with an ethnographic accompaniment to Robert Gardiner's film about the Dani tribesmen, "Dead Birds." The first section offers an ethnographic profile of Dani culture, describing its most important features. These include: 1) their material culture, covering matters…

Contrasting Patterns in Solitary and Eusocial Bees While Responding to Landscape Features in the Brazilian Cerrado: a Multiscaled Perspective.

PubMed

Silva, D P; Nogueira, D S; De Marco, P

2017-06-01

Landscape structure is an important determinant of biological fluxes and species composition, but species do not respond equally to landscape features or spatial extents. Evaluating "multi-scale" responses of species to landscape structure is an important framework to be considered, allowing insights about habitat requirements for different groups. We evaluated the response of Brazilian Cerrado's bees (eusocial vs. solitary ones) to both the amount and isolation of remnant vegetation in eight nested multiple-local scales. Response variables included abundance, observed, and estimated species richness, and beta diversity (split into nestedness and turnover resultant dissimilarities). Eusocial species' abundance responded to landscape structure at narrow scales of fragment isolation (250 m of radius from sampling sites), while solitary species' abundance responded to broader scales to fragment area (2000 m). Eusocial species nestedness also responded to landscape features in broader scales (1500 m), especially to increasing fragment isolation. However, all the remaining response variables did not respond to any other landscape variables in any spatial scale considered. Such contrasting responses of the abundances of eusocial vs. solitary species are related to the inherent life-history traits of each group. Important attributes in this context are different requirements on food resources, population features, and flight abilities. Species-specific dispersal abilities may be the main determinants of the nested patterns found for eusocial species at 1500 m. Considering these results, we suggest that different bee groups are considered separately in further landscape analyses, especially in other Brazilian biomes, for a better understanding of landscape effects on these organisms.

Predicting Chemically Induced Duodenal Ulcer and Adrenal Necrosis with Classification Trees

NASA Astrophysics Data System (ADS)

Giampaolo, Casimiro; Gray, Andrew T.; Olshen, Richard A.; Szabo, Sandor

1991-07-01

Binary tree-structured statistical classification algorithms and properties of 56 model alkyl nucleophiles were brought to bear on two problems of experimental pharmacology and toxicology. Each rat of a learning sample of 745 was administered one compound and autopsied to determine the presence of duodenal ulcer or adrenal hemorrhagic necrosis. The cited statistical classification schemes were then applied to these outcomes and 67 features of the compounds to ascertain those characteristics that are associated with biologic activity. For predicting duodenal ulceration, dipole moment, melting point, and solubility in octanol are particularly important, while for predicting adrenal necrosis, important features include the number of sulfhydryl groups and double bonds. These methods may constitute inexpensive but powerful ways to screen untested compounds for possible organ-specific toxicity. Mechanisms for the etiology and pathogenesis of the duodenal and adrenal lesions are suggested, as are additional avenues for drug design.

Redefining cerebral malaria by including malaria retinopathy.

PubMed

Beare, Nicholas A V; Lewallen, Susan; Taylor, Terrie E; Molyneux, Malcolm E

2011-03-01

Accurate diagnosis of cerebral malaria (CM) is important for patient management, epidemiological and end point surveillance, and enrolling patients with CM in studies of pathogenesis or therapeutic trials. In malaria-endemic areas, where asymptomatic Plasmodium falciparum parasitemia is common, a positive blood film in a comatose individual does not prove that the coma is due to malaria. A retinopathy consisting of two unique features - patchy retinal whitening and focal changes of vessel color - is highly specific for encephalopathy of malarial etiology. White-centered retinal hemorrhages are a common but less specific feature. Either indirect or direct ophthalmoscopy can be used to identify the changes, and both procedures can be learned and practiced by nonspecialist clinicians. In view of its important contributions to both clinical care and research, examination of the retina should become a routine component of the assessment of a comatose child or adult when CM is a possible diagnosis.

Redefining cerebral malaria by including malaria retinopathy

PubMed Central

Beare, Nicholas AV; Lewallen, Susan; Taylor, Terrie E; Molyneux, Malcolm E

2011-01-01

Accurate diagnosis of cerebral malaria (CM) is important for patient management, epidemiological and end point surveillance, and enrolling patients with CM in studies of pathogenesis or therapeutic trials. In malaria-endemic areas, where asymptomatic Plasmodium falciparum parasitemia is common, a positive blood film in a comatose individual does not prove that the coma is due to malaria. A retinopathy consisting of two unique features – patchy retinal whitening and focal changes of vessel color – is highly specific for encephalopathy of malarial etiology. White-centered retinal hemorrhages are a common but less specific feature. Either indirect or direct ophthalmoscopy can be used to identify the changes, and both procedures can be learned and practiced by nonspecialist clinicians. In view of its important contributions to both clinical care and research, examination of the retina should become a routine component of the assessment of a comatose child or adult when CM is a possible diagnosis. PMID:21449844

Imaging of gout: an overview.

PubMed

Dalbeth, Nicola; Doyle, Anthony J

2012-12-01

The diverse clinical states and sites of pathology in gout provide challenges when considering the features apparent on imaging. Ideally, an imaging modality should capture all aspects of disease including monosodium urate crystal deposition, acute inflammation, tophus, tissue remodelling and complications of disease. The modalities used in gout include conventional radiography, ultrasonography, magnetic resonance imaging, computed tomography and dual-energy computed tomography. This review discusses the role of each of these imaging modalities in gout, focussing on the imaging characteristics, role in gout diagnosis and role for disease monitoring. Ultrasonography and dual-energy computed tomography are particularly promising methods for both non-invasive diagnosis and monitoring of disease. The observation that ultrasonographic appearances of monosodium urate crystal deposition can be observed in patients with hyperuricaemia but no other clinical features of gout raises important questions about disease definitions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Defining greed.

PubMed

Seuntjens, Terri G; Zeelenberg, Marcel; Breugelmans, Seger M; van de Ven, Niels

2015-08-01

Although greed is both hailed as the motor of economic growth and blamed as the cause of economic crises, very little is known about its psychological underpinnings. Five studies explored lay conceptualizations of greed among U.S. and Dutch participants using a prototype analysis. Study 1 identified features related to greed. Study 2 determined the importance of these features; the most important features were classified as central (e.g., self-interested, never satisfied), whereas less important features were classified as peripheral (e.g., ambition, addiction). Subsequently, we found that, compared to peripheral features, participants recalled central features better (Study 3), faster (Study 4), and these central features were more present in real-life episodes of greed (Study 5). These findings provide a better understanding of the elements that make up the experience of greed and provide insights into how greed can be manipulated and measured in future research. © 2014 The British Psychological Society.

A computer simulation of Skylab dynamics and attitude control for performance verification and operational support

NASA Technical Reports Server (NTRS)

Buchanan, H.; Nixon, D.; Joyce, R.

1974-01-01

A simulation of the Skylab attitude and pointing control system (APCS) is outlined and discussed. Implementation is via a large hybrid computer and includes those factors affecting system momentum management, propellant consumption, and overall vehicle performance. The important features of the flight system are discussed; the mathematical models necessary for this treatment are outlined; and the decisions involved in implementation are discussed. A brief summary of the goals and capabilities of this tool is also included.

Better physical activity classification using smartphone acceleration sensor.

PubMed

Arif, Muhammad; Bilal, Mohsin; Kattan, Ahmed; Ahamed, S Iqbal

2014-09-01

Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted from the acceleration data recorded by smartphone during different physical activities. Time and space complexity of the whole framework is done by optimal feature subset selection and pruning of instances. Classification results of six physical activities are reported in this paper. Using simple time domain features, 99 % classification accuracy is achieved. Furthermore, attributes subset selection is used to remove the redundant features and to minimize the time complexity of the algorithm. A subset of 30 features produced more than 98 % classification accuracy for the six physical activities.

Scalable topographies to support proliferation and Oct4 expression by human induced pluripotent stem cells

PubMed Central

Reimer, Andreas; Vasilevich, Aliaksei; Hulshof, Frits; Viswanathan, Priyalakshmi; van Blitterswijk, Clemens A.; de Boer, Jan; Watt, Fiona M.

2016-01-01

It is well established that topographical features modulate cell behaviour, including cell morphology, proliferation and differentiation. To define the effects of topography on human induced pluripotent stem cells (iPSC), we plated cells on a topographical library containing over 1000 different features in medium lacking animal products (xeno-free). Using high content imaging, we determined the effect of each topography on cell proliferation and expression of the pluripotency marker Oct4 24 h after seeding. Features that maintained Oct4 expression also supported proliferation and cell-cell adhesion at 24 h, and by 4 days colonies of Oct4-positive, Sox2-positive cells had formed. Computational analysis revealed that small feature size was the most important determinant of pluripotency, followed by high wave number and high feature density. Using this information we correctly predicted whether any given topography within our library would support the pluripotent state at 24 h. This approach not only facilitates the design of substrates for optimal human iPSC expansion, but also, potentially, identification of topographies with other desirable characteristics, such as promoting differentiation. PMID:26757610

Scalable topographies to support proliferation and Oct4 expression by human induced pluripotent stem cells.

PubMed

Reimer, Andreas; Vasilevich, Aliaksei; Hulshof, Frits; Viswanathan, Priyalakshmi; van Blitterswijk, Clemens A; de Boer, Jan; Watt, Fiona M

2016-01-13

It is well established that topographical features modulate cell behaviour, including cell morphology, proliferation and differentiation. To define the effects of topography on human induced pluripotent stem cells (iPSC), we plated cells on a topographical library containing over 1000 different features in medium lacking animal products (xeno-free). Using high content imaging, we determined the effect of each topography on cell proliferation and expression of the pluripotency marker Oct4 24 h after seeding. Features that maintained Oct4 expression also supported proliferation and cell-cell adhesion at 24 h, and by 4 days colonies of Oct4-positive, Sox2-positive cells had formed. Computational analysis revealed that small feature size was the most important determinant of pluripotency, followed by high wave number and high feature density. Using this information we correctly predicted whether any given topography within our library would support the pluripotent state at 24 h. This approach not only facilitates the design of substrates for optimal human iPSC expansion, but also, potentially, identification of topographies with other desirable characteristics, such as promoting differentiation.

Inherited Fe and Ti electron transition spectroscopic features in altered ultramafic-carbonatite intrusives

NASA Astrophysics Data System (ADS)

Shavers, E. J.; Ghulam, A.; Encarnacion, J. P.

2016-12-01

Spectroscopic reflectance in the visible to short-wave infrared region is an important tool for remote geologic mapping and is applied at scales from satellite to field measurements. Remote geologic mapping is challenging in regions subject to significant surficial weathering. Here we identify absorption features found in altered volcanic pipes and dikes in the Avon Volcanic District, Missouri, that are inherited from the original ultramafic and carbonatite lithology. Alteration ranges from small degree hydrothermal alteration to extensive laterization. The absorption features are three broad minima centered near 690, 890, and 1100 nm. Features in this region are recognized to be caused by ferric and ferrous Fe minerals including olivine, carbonates, chlorite, and goethite all of which are found among the Avon pipes and dikes that are in various stages of alteration. Iron-related intervalence charge transfer and crystal field perturbations of ions are the principal causes of the spectroscopic features in the visible to near-infrared region yet spectra are also distorted by factors like texture and the presence of opaque minerals known to reduce overall reflectance. In the Avon samples, Fe oxide content can reach >15 wt% leading to prominent absorption features even in the less altered ultramafics with reflectance curve maxima as low as 5%. The exaggerated minima allow the altered intrusive rocks to stand out among other weathered lithologies that will often have clay features in the region yet have lower iron concentration. The absorption feature centered near 690 nm is particularly noteworthy. Broad mineral-related absorption features centered at this wavelength are rare but have been linked to Ti3+ in octahedral coordination. The reduced form of Ti is not common in surface lithologies. Titanium-rich andradite has Ti3+ in the octahedral position, is resistant to weathering, is found among the Avon lithologies including ultramafic, carbonatite, and carbonated breccia, and is identified here as the cause of the 690 nm absorption feature. The Ti3+ absorption feature centered near 690 nm and strong Fe absorption features at 890 and 1100 nm may be useful indicators of rare intrusive lithologies in remote geologic mapping.

Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

PubMed

Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

2014-01-01

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

Tunable intraparticle frameworks for creating complex heterostructured nanoparticle libraries

NASA Astrophysics Data System (ADS)

Fenton, Julie L.; Steimle, Benjamin C.; Schaak, Raymond E.

2018-05-01

Complex heterostructured nanoparticles with precisely defined materials and interfaces are important for many applications. However, rationally incorporating such features into nanoparticles with rigorous morphology control remains a synthetic bottleneck. We define a modular divergent synthesis strategy that progressively transforms simple nanoparticle synthons into increasingly sophisticated products. We introduce a series of tunable interfaces into zero-, one-, and two-dimensional copper sulfide nanoparticles using cation exchange reactions. Subsequent manipulation of these intraparticle frameworks yielded a library of 47 distinct heterostructured metal sulfide derivatives, including particles that contain asymmetric, patchy, porous, and sculpted nanoarchitectures. This generalizable mix-and-match strategy provides predictable retrosynthetic pathways to complex nanoparticle features that are otherwise inaccessible.

Roth 401(k): asking the right questions.

PubMed

Joyner, James F

2006-01-01

Roth 401(k) provisions are a newly available feature of 401(k) plans. Roth 401(k) provisions are after-tax savings that generally are tax-free at the time of distribution. Questions arise for plan sponsors about whether the new feature is beneficial, and to whom, and what needs to be done if the plan sponsor decides to offer this provision to its employees. This article tries to answer some of those common questions, including a simple computational analysis to try to answer the important question of how much an employee-participant genuinely benefits from this savings approach. Some practical issues of implementation are touched on, and some unanswered questions are identified.

Fetal anterior abdominal wall defects: prenatal imaging by magnetic resonance imaging.

PubMed

Victoria, Teresa; Andronikou, Savvas; Bowen, Diana; Laje, Pablo; Weiss, Dana A; Johnson, Ann M; Peranteau, William H; Canning, Douglas A; Adzick, N Scott

2018-04-01

Abdominal wall defects range from the mild umbilical cord hernia to the highly complex limb-body wall syndrome. The most common defects are gastroschisis and omphalocele, and the rarer ones include the exstrophy complex, pentalogy of Cantrell and limb-body wall syndrome. Although all have a common feature of viscera herniation through a defect in the anterior body wall, their imaging features and, more important, postnatal management, differ widely. Correct diagnosis of each entity is imperative in order to achieve appropriate and accurate prenatal counseling and postnatal management. In this paper, we discuss fetal abdominal wall defects and present diagnostic pearls to aid with diagnosis.

[Advances of portable electrocardiogram monitor design].

PubMed

Ding, Shenping; Wang, Yinghai; Wu, Weirong; Deng, Lingli; Lu, Jidong

2014-06-01

Portable electrocardiogram monitor is an important equipment in the clinical diagnosis of cardiovascular diseases due to its portable, real-time features. It has a broad application and development prospects in China. In the present review, previous researches on the portable electrocardiogram monitors have been arranged, analyzed and summarized. According to the characteristics of the electrocardiogram (ECG), this paper discusses the ergonomic design of the portable electrocardiogram monitor, including hardware and software. The circuit components and software modules were parsed from the ECG features and system functions. Finally, the development trend and reference are provided for the portable electrocardiogram monitors and for the subsequent research and product design.

[Hypersomnia: a diagnostic problem].

PubMed

Kranenburg, Guido; Teunissen, Laurien L

2014-01-01

Hypersomnia is a frequently occurring problem. When taking a medical history it is important to distinguish between fatigue and sleepiness. We present a 14-year-old girl with narcolepsy and a 59-year-old man with idiopathic hypersomnia. Features that are typical of narcolepsy are cataplexy and weight gain. Features that are typical of both narcolepsy and idiopathic hypersomnia are daytime naps, insomnia, sleep paralysis and hypnagogic hallucinations. Additional testing in patients with hypersomnia should include a polysomnography in order to exclude other sleeping disorders, and a mean sleep latency test. Practice shows that both patients with narcolepsy and those with idiopathic hypersomnia benefit from treatment with stimulating drugs such as modafinil.

Redd Site Selection and Spawning Habitat Use by Fall Chinook Salmon: The Importance of Geomorphic Features in Large Rivers

PubMed

Geist; Dauble

1998-09-01

/ Knowledge of the three-dimensional connectivity between rivers and groundwater within the hyporheic zone can be used to improve the definition of fall chinook salmon (Oncorhynchus tshawytscha) spawning habitat. Information exists on the microhabitat characteristics that define suitable salmon spawning habitat. However, traditional spawning habitat models that use these characteristics to predict available spawning habitat are restricted because they can not account for the heterogeneous nature of rivers. We present a conceptual spawning habitat model for fall chinook salmon that describes how geomorphic features of river channels create hydraulic processes, including hyporheic flows, that influence where salmon spawn in unconstrained reaches of large mainstem alluvial rivers. Two case studies based on empirical data from fall chinook salmon spawning areas in the Hanford Reach of the Columbia River are presented to illustrate important aspects of our conceptual model. We suggest that traditional habitat models and our conceptual model be combined to predict the limits of suitable fall chinook salmon spawning habitat. This approach can incorporate quantitative measures of river channel morphology, including general descriptors of geomorphic features at different spatial scales, in order to understand the processes influencing redd site selection and spawning habitat use. This information is needed in order to protect existing salmon spawning habitat in large rivers, as well as to recover habitat already lost.KEY WORDS: Hyporheic zone; Geomorphology; Spawning habitat; Large rivers; Fall chinook salmon; Habitat management

Hallucinations: A Systematic Review of Points of Similarity and Difference Across Diagnostic Classes.

PubMed

Waters, Flavie; Fernyhough, Charles

2017-01-01

Hallucinations constitute one of the 5 symptom domains of psychotic disorders in DSM-5, suggesting diagnostic significance for that group of disorders. Although specific featural properties of hallucinations (negative voices, talking in the third person, and location in external space) are no longer highlighted in DSM, there is likely a residual assumption that hallucinations in schizophrenia can be identified based on these candidate features. We investigated whether certain featural properties of hallucinations are specifically indicative of schizophrenia by conducting a systematic review of studies showing direct comparisons of the featural and clinical characteristics of (auditory and visual) hallucinations among 2 or more population groups (one of which included schizophrenia). A total of 43 articles were reviewed, which included hallucinations in 4 major groups (nonclinical groups, drug- and alcohol-related conditions, medical and neurological conditions, and psychiatric disorders). The results showed that no single hallucination feature or characteristic uniquely indicated a diagnosis of schizophrenia, with the sole exception of an age of onset in late adolescence. Among the 21 features of hallucinations in schizophrenia considered here, 95% were shared with other psychiatric disorders, 85% with medical/neurological conditions, 66% with drugs and alcohol conditions, and 52% with the nonclinical groups. Additional differences rendered the nonclinical groups somewhat distinctive from clinical disorders. Overall, when considering hallucinations, it is inadvisable to give weight to the presence of any featural properties alone in making a schizophrenia diagnosis. It is more important to focus instead on the co-occurrence of other symptoms and the value of hallucinations as an indicator of vulnerability. © The Author 2016. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification.

PubMed

Qin, Jiaolong; Wei, Maobin; Liu, Haiyan; Chen, Jianhuai; Yan, Rui; Hua, Lingling; Zhao, Ke; Yao, Zhijian; Lu, Qing

2014-12-01

Previous studies had explored the diagnostic and prognostic value of the structural neuroimaging data of MDD and treated the whole brain voxels, the fractional anisotropy and the structural connectivity as classification features. To our best knowledge, no study examined the potential diagnostic value of the hubs of anatomical brain networks in MDD. The purpose of the current study was to provide an exploratory examination of the potential diagnostic and prognostic values of hubs of white matter brain networks in MDD discrimination and the corresponding impaired hub pattern via a multi-pattern analysis. We constructed white matter brain networks from 29 depressions and 30 healthy controls based on diffusion tensor imaging data, calculated nodal measures and identified hubs. Using these measures as features, two types of feature architectures were established, one only included hubs (HUB) and the other contained both hubs and non hubs. The support vector machine classifiers with Gaussian radial basis kernel were used after the feature selection. Moreover, the relative contribution of the features was estimated by means of the consensus features. Our results presented that the hubs (including the bilateral dorsolateral part of superior frontal gyrus, the left middle frontal gyrus, the bilateral middle temporal gyrus, and the bilateral inferior temporal gyrus) played an important role in distinguishing the depressions from healthy controls with the best accuracy of 83.05%. Moreover, most of the HUB consensus features located in the frontal-parieto circuit. These findings provided evidence that the hubs could be served as valuable potential diagnostic measure for MDD, and the hub-concentrated lesion distribution of MDD was primarily anchored within the frontal-parieto circuit. Copyright © 2014 Elsevier Inc. All rights reserved.

Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution

PubMed Central

Rule, Michael E.; Vargas-Irwin, Carlos; Donoghue, John P.; Truccolo, Wilson

2015-01-01

Understanding the sources of variability in single-neuron spiking responses is an important open problem for the theory of neural coding. This variability is thought to result primarily from spontaneous collective dynamics in neuronal networks. Here, we investigate how well collective dynamics reflected in motor cortex local field potentials (LFPs) can account for spiking variability during motor behavior. Neural activity was recorded via microelectrode arrays implanted in ventral and dorsal premotor and primary motor cortices of non-human primates performing naturalistic 3-D reaching and grasping actions. Point process models were used to quantify how well LFP features accounted for spiking variability not explained by the measured 3-D reach and grasp kinematics. LFP features included the instantaneous magnitude, phase and analytic-signal components of narrow band-pass filtered (δ,θ,α,β) LFPs, and analytic signal and amplitude envelope features in higher-frequency bands. Multiband LFP features predicted single-neuron spiking (1ms resolution) with substantial accuracy as assessed via ROC analysis. Notably, however, models including both LFP and kinematics features displayed marginal improvement over kinematics-only models. Furthermore, the small predictive information added by LFP features to kinematic models was redundant to information available in fast-timescale (<100 ms) spiking history. Overall, information in multiband LFP features, although predictive of single-neuron spiking during movement execution, was redundant to information available in movement parameters and spiking history. Our findings suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs primarily relate to sensorimotor processes directly controlling movement output, adding little explanatory power to variability not accounted by movement parameters. PMID:26157365

Walking and child pedestrian injury: a systematic review of built environment correlates of safe walking.

PubMed

Rothman, Linda; Buliung, Ron; Macarthur, Colin; To, Teresa; Howard, Andrew

2014-02-01

The child active transportation literature has focused on walking, with little attention to risk associated with increased traffic exposure. This paper reviews the literature related to built environment correlates of walking and pedestrian injury in children together, to broaden the current conceptualization of walkability to include injury prevention. Two independent searches were conducted focused on walking in children and child pedestrian injury within nine electronic databases until March, 2012. Studies were included which: 1) were quantitative 2) set in motorized countries 3) were either urban or suburban 4) investigated specific built environment risk factors 5) had outcomes of either walking in children and/or child pedestrian roadway collisions (ages 0-12). Built environment features were categorized according to those related to density, land use diversity or roadway design. Results were cross-tabulated to identify how built environment features associate with walking and injury. Fifty walking and 35 child pedestrian injury studies were identified. Only traffic calming and presence of playgrounds/recreation areas were consistently associated with more walking and less pedestrian injury. Several built environment features were associated with more walking, but with increased injury. Many features had inconsistent results or had not been investigated for either outcome. The findings emphasise the importance of incorporating safety into the conversation about creating more walkable cities.

Working towards integrated community care for older people: empowering organisational features from a professional perspective.

PubMed

Janssen, Bienke M; Snoeren, Miranda W C; Van Regenmortel, Tine; Abma, Tineke A

2015-01-01

Although multi-disciplinary cooperation between professionals is a prerequisite to provide integrated care in the community, this seems hard to realise in practice. Yet, little is known about the experiences of professionals who implement it nor about the organisational features professionals identify as empowering during this cooperation process. Therefore, a case study of a multi-disciplinary geriatric team was performed. The data-collection included observations of meetings, in-depth interviews and focus groups with professionals (N = 12). Data were analysed inductively and related to the three organisational levels within the model of organisational empowerment of Peterson and Zimmerman. Signs of empowering organisational features on the intraorganisational level were mutual trust and clear working routines. On the interorganisational level important features included improved linkages between participating organisations and increased insight into each other's tasks. Tensions occurred relating to the inter- and the extraorganisational level. Professionals felt that the commitment of the management of involved organisations should be improved just as the capacity of the team to influence (local) policy. It is recommended that policymakers should not determine the nature of professional cooperation in advance, but to leave that to the local context as well as to the judgement of involved professionals. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data.

PubMed

Minhas, Fayyaz Ul Amir Afsar; Asif, Amina; Arif, Muhammad

2016-12-01

Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map. CAFÉ-Map is a locally linear feature ranking framework that allows recognition of important features in any given region of the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFÉ-Map on a number of toy and real world biomedical data sets. Our comparative study with a number of published methods shows that CAFÉ-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFÉ-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFÉ-Map provides a more in-depth analysis of feature ranking at the level of individual examples. CAFÉ-Map Python code is available at: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap . The CAFÉ-Map package supports parallelization and sparse data and provides example scripts for classification. This code can be used to reconstruct the results given in this paper. Copyright © 2016 Elsevier Ltd. All rights reserved.

The myositis autoantibody phenotypes of the juvenile idiopathic inflammatory myopathies.

PubMed

Rider, Lisa G; Shah, Mona; Mamyrova, Gulnara; Huber, Adam M; Rice, Madeline Murguia; Targoff, Ira N; Miller, Frederick W

2013-07-01

The juvenile idiopathic inflammatory myopathies (JIIM) are systemic autoimmune diseases characterized by skeletal muscle weakness, characteristic rashes, and other systemic features. In follow-up to our study defining the major clinical subgroup phenotypes of JIIM, we compared demographics, clinical features, laboratory measures, and outcomes among myositis-specific autoantibody (MSA) subgroups, as well as with published data on adult idiopathic inflammatory myopathy patients enrolled in a separate natural history study. In the present study, of 430 patients enrolled in a nationwide registry study who had serum tested for myositis autoantibodies, 374 had either a single specific MSA (n = 253) or no identified MSA (n = 121) and were the subject of the present report. Following univariate analysis, we used random forest classification and exact logistic regression modeling to compare autoantibody subgroups. Anti-p155/140 autoantibodies were the most frequent subgroup, present in 32% of patients with juvenile dermatomyositis (JDM) or overlap myositis with JDM, followed by anti-MJ autoantibodies, which were seen in 20% of JIIM patients, primarily in JDM. Other MSAs, including anti-synthetase, anti-signal recognition particle (SRP), and anti-Mi-2, were present in only 10% of JIIM patients. Features that characterized the anti-p155/140 autoantibody subgroup included Gottron papules, malar rash, "shawl-sign" rash, photosensitivity, cuticular overgrowth, lowest creatine kinase (CK) levels, and a predominantly chronic illness course. The features that differed for patients with anti-MJ antibodies included muscle cramps, dysphonia, intermediate CK levels, a high frequency of hospitalization, and a monocyclic disease course. Patients with anti-synthetase antibodies had higher frequencies of interstitial lung disease, arthralgia, and "mechanic's hands," and had an older age at diagnosis. The anti-SRP group, which had exclusively juvenile polymyositis, was characterized by high frequencies of black race, severe onset, distal weakness, falling episodes, Raynaud phenomenon, cardiac involvement, high CK levels, chronic disease course, frequent hospitalization, and wheelchair use. Characteristic features of the anti-Mi-2 subgroup included Hispanic ethnicity, classic dermatomyositis and malar rashes, high CK levels, and very low mortality. Finally, the most common features of patients without any currently defined MSA or myositis-associated autoantibodies included linear extensor erythema, arthralgia, and a monocyclic disease course. Several demographic and clinical features were shared between juvenile and adult idiopathic inflammatory myopathy subgroups, but with several important differences. We conclude that juvenile myositis is a heterogeneous group of illnesses with distinct autoantibody phenotypes defined by varying clinical and demographic characteristics, laboratory features, and outcomes.

The Myositis Autoantibody Phenotypes of the Juvenile Idiopathic Inflammatory Myopathies

PubMed Central

Shah, Mona; Mamyrova, Gulnara; Huber, Adam M.; Rice, Madeline Murguia; Targoff, Ira N.; Miller, Frederick W.

2013-01-01

Abstract The juvenile idiopathic inflammatory myopathies (JIIM) are systemic autoimmune diseases characterized by skeletal muscle weakness, characteristic rashes, and other systemic features. In follow-up to our study defining the major clinical subgroup phenotypes of JIIM, we compared demographics, clinical features, laboratory measures, and outcomes among myositis-specific autoantibody (MSA) subgroups, as well as with published data on adult idiopathic inflammatory myopathy patients enrolled in a separate natural history study. In the present study, of 430 patients enrolled in a nationwide registry study who had serum tested for myositis autoantibodies, 374 had either a single specific MSA (n = 253) or no identified MSA (n = 121) and were the subject of the present report. Following univariate analysis, we used random forest classification and exact logistic regression modeling to compare autoantibody subgroups. Anti-p155/140 autoantibodies were the most frequent subgroup, present in 32% of patients with juvenile dermatomyositis (JDM) or overlap myositis with JDM, followed by anti-MJ autoantibodies, which were seen in 20% of JIIM patients, primarily in JDM. Other MSAs, including anti-synthetase, anti-signal recognition particle (SRP), and anti-Mi-2, were present in only 10% of JIIM patients. Features that characterized the anti-p155/140 autoantibody subgroup included Gottron papules, malar rash, “shawl-sign” rash, photosensitivity, cuticular overgrowth, lowest creatine kinase (CK) levels, and a predominantly chronic illness course. The features that differed for patients with anti-MJ antibodies included muscle cramps, dysphonia, intermediate CK levels, a high frequency of hospitalization, and a monocyclic disease course. Patients with anti-synthetase antibodies had higher frequencies of interstitial lung disease, arthralgia, and “mechanic’s hands,” and had an older age at diagnosis. The anti-SRP group, which had exclusively juvenile polymyositis, was characterized by high frequencies of black race, severe onset, distal weakness, falling episodes, Raynaud phenomenon, cardiac involvement, high CK levels, chronic disease course, frequent hospitalization, and wheelchair use. Characteristic features of the anti-Mi-2 subgroup included Hispanic ethnicity, classic dermatomyositis and malar rashes, high CK levels, and very low mortality. Finally, the most common features of patients without any currently defined MSA or myositis-associated autoantibodies included linear extensor erythema, arthralgia, and a monocyclic disease course. Several demographic and clinical features were shared between juvenile and adult idiopathic inflammatory myopathy subgroups, but with several important differences. We conclude that juvenile myositis is a heterogeneous group of illnesses with distinct autoantibody phenotypes defined by varying clinical and demographic characteristics, laboratory features, and outcomes. PMID:23877355

Anthropometric typology of male and female rowers using k-means clustering.

PubMed

Forjasz, Justyna

2011-06-01

The aim of this paper is to present the morphological features of rowers. The objective is to establish the type of body build best suited to the present requirements of this sports discipline through the determination of the most important morphological features in rowing with regard to the type of racing boat. The subjects of this study included competitors who practise rowing and were members of the Junior National Team. The considered variables included a group of 32 anthropometric measurements of body composition determined using the BIA method among male and female athletes, while also including rowing boat categories. In order to determine the analysed structures of male and female rowers, an observation analysis was taken into consideration and performed by the k-means clustering method. In the group of male and female rowers using long paddles, higher mean values for the analysed features were observed, with the exception of fat-free mass, and water content in both genders, and trunk length and horizontal reach in women who achieved higher means in the short-paddle group. On the men's team, both groups differed significantly in body mass, longitudinal features, horizontal reach, hand width and body circumferences, while on the women's, they differed in body mass, width and length of the chest, body circumferences and fat content. The method of grouping used in this paper confirmed morphological differences in the competitors with regard to the type of racing boat.

Anthropometric Typology of Male and Female Rowers Using K-Means Clustering

PubMed Central

Forjasz, Justyna

2011-01-01

The aim of this paper is to present the morphological features of rowers. The objective is to establish the type of body build best suited to the present requirements of this sports discipline through the determination of the most important morphological features in rowing with regard to the type of racing boat. The subjects of this study included competitors who practise rowing and were members of the Junior National Team. The considered variables included a group of 32 anthropometric measurements of body composition determined using the BIA method among male and female athletes, while also including rowing boat categories. In order to determine the analysed structures of male and female rowers, an observation analysis was taken into consideration and performed by the k-means clustering method. In the group of male and female rowers using long paddles, higher mean values for the analysed features were observed, with the exception of fat-free mass, and water content in both genders, and trunk length and horizontal reach in women who achieved higher means in the short-paddle group. On the men’s team, both groups differed significantly in body mass, longitudinal features, horizontal reach, hand width and body circumferences, while on the women’s, they differed in body mass, width and length of the chest, body circumferences and fat content. The method of grouping used in this paper confirmed morphological differences in the competitors with regard to the type of racing boat. PMID:23486287

Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis. Application of machine learning principles.

PubMed

Ataer-Cansizoglu, E; Kalpathy-Cramer, J; You, S; Keck, K; Erdogmus, D; Chiang, M F

2015-01-01

Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support systems and educational systems in ROP, and these methods may be applicable to other diseases where inter-expert variability is observed. The experiments were carried out on a dataset of 34 retinal images, each with diagnoses provided independently by 22 experts. Analysis was performed using concepts of Mutual Information (MI) and Kernel Density Estimation. A large set of structural features (a total of 66) were extracted from retinal images. Feature selection was utilized to identify the most important features that correlated to actual clinical decisions by the 22 study experts. The best three features for each observer were selected by an exhaustive search on all possible feature subsets and considering joint MI as a relevance criterion. We also compared our results with the results of Cohen's Kappa [36] as an inter-rater reliability measure. The results demonstrate that a group of observers (17 among 22) decide consistently with each other. Mean and second central moment of arteriolar tortuosity is among the reasons of disagreement between this group and the rest of the observers, meaning that the group of experts consider amount of tortuosity as well as the variation of tortuosity in the image. Given a set of image-based features, the proposed analysis method can identify critical image-based features that lead to expert agreement and disagreement in diagnosis of ROP. Although tree-based features and various statistics such as central moment are not popular in the literature, our results suggest that they are important for diagnosis.

Characterizing Smartphone Engagement for Schizophrenia: Results of a Naturalist Mobile Health Study.

PubMed

Torous, John; Staples, Patrick; Slaters, Linda; Adams, Jared; Sandoval, Luis; Onnela, J P; Keshavan, Matcheri

2017-08-04

Despite growing interest in smartphone apps for schizophrenia, little is known about how these apps are utilized in the real world. Understanding how app users are engaging with these tools outside of the confines of traditional clinical studies offers an important information on who is most likely to use apps and what type of data they are willing to share. The Schizophrenia and Related Disorders Alliance of America, in partnership with Self Care Catalyst, has created a smartphone app for schizophrenia that is free and publically available on both Apple iTunes and Google Android Play stores. We analyzed user engagement data from this app across its medication tracking, mood tracking, and symptom tracking features from August 16 th 2015 to January 1 st 2017 using the R programming language. We included all registered app users in our analysis with reported ages less than 100. We analyzed a total of 43,451 mood, medication and symptom entries from 622 registered users, and excluded a single patient with a reported age of 114. Seventy one percent of the 622 users tried the mood-tracking feature at least once, 49% the symptom tracking feature, and 36% the medication-tracking feature. The mean number of uses of the mood feature was two, the symptom feature 10, and the medication feature 14. However, a small subset of users were very engaged with the app and the top 10 users for each feature accounted for 35% or greater of all entries for that feature. We find that user engagement follows a power law distribution for each feature, and this fit was largely invariant when stratifying for age or gender. Engagement with this app for schizophrenia was overall low, but similar to prior naturalistic studies for mental health app use in other diseases. The low rate of engagement in naturalistic settings, compared to higher rates of use in clinical studies, suggests the importance of clinical involvement as one factor in driving engagement for mental health apps. Power law relationships suggest strongly skewed user engagement, with a small subset of users accounting for the majority of substantial engagements. There is a need for further research on app engagement in schizophrenia.

Automated classification of immunostaining patterns in breast tissue from the human protein atlas.

PubMed

Swamidoss, Issac Niwas; Kårsnäs, Andreas; Uhlmann, Virginie; Ponnusamy, Palanisamy; Kampf, Caroline; Simonsson, Martin; Wählby, Carolina; Strand, Robin

2013-01-01

The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.

Research on driver fatigue detection

NASA Astrophysics Data System (ADS)

Zhang, Ting; Chen, Zhong; Ouyang, Chao

2018-03-01

Driver fatigue is one of the main causes of frequent traffic accidents. In this case, driver fatigue detection system has very important significance in avoiding traffic accidents. This paper presents a real-time method based on fusion of multiple facial features, including eye closure, yawn and head movement. The eye state is classified as being open or closed by a linear SVM classifier trained using HOG features of the detected eye. The mouth state is determined according to the width-height ratio of the mouth. The head movement is detected by head pitch angle calculated by facial landmark. The driver's fatigue state can be reasoned by the model trained by above features. According to experimental results, drive fatigue detection obtains an excellent performance. It indicates that the developed method is valuable for the application of avoiding traffic accidents caused by driver's fatigue.

[Movement disorders is psychiatric diseases].

PubMed

Hidasi, Zoltan; Salacz, Pal; Csibri, Eva

2014-12-01

Movement disorders are common in psychiatry. The movement disorder can either be the symptom of a psychiatric disorder, can share a common aetiological factor with it, or can be the consequence of psychopharmacological therapy. Most common features include tic, stereotypy, compulsion, akathisia, dyskinesias, tremor, hypokinesia and disturbances of posture and gait. We discuss characteristics and clinical importance of these features. Movement disorders are frequently present in mood disorders, anxiety disorders, schizophrenia, catatonia, Tourette-disorder and psychogenic movement disorder, leading to differential-diagnostic and therapeutical difficulties in everyday practice. Movement disorders due to psychopharmacotherapy can be classified as early-onset, late-onset and tardive. Frequent psychiatric comorbidity is found in primary movement disorders, such as Parkinson's disease, Wilson's disease, Huntington's disease, diffuse Lewy-body disorder. Complex neuropsychiatric approach is effective concerning overlapping clinical features and spectrums of disorders in terms of movement disorders and psychiatric diseases.

Hydrogeomorphic features mediate the effects of land use/cover on reservoir productivity and food webs

USGS Publications Warehouse

Bremigan, M.T.; Soranno, P.A.; Gonzalez, M.J.; Bunnell, D.B.; Arend, K.K.; Renwick, W.H.; Stein, R.A.; Vanni, M.J.

2008-01-01

Although effects of land use/cover on nutrient concentrations in aquatic systems are well known, half or more of the variation in nutrient concentration remains unexplained by land use/cover alone. Hydrogeomorphic (HGM) landscape features can explain much remaining variation and influence food web interactions. To explore complex linkages among land use/cover, HGM features, reservoir productivity, and food webs, we sampled 11 Ohio reservoirs, ranging broadly in agricultural catchment land use/cover, for 3 years. We hypothesized that HGM features mediate the bottom-up effects of land use/cover on reservoir productivity, chlorophyll a, zooplankton, and recruitment of gizzard shad, an omnivorous fish species common throughout southeastern U.S. reservoirs and capable of exerting strong effects on food web and nutrient dynamics. We tested specific hypotheses using a model selection approach. Percent variation explained was highest for total nitrogen (R2 = 0.92), moderately high for total phosphorus, chlorophyll a, and rotifer biomass (R2 = 0.57 to 0.67), relatively low for crustacean zooplankton biomass and larval gizzard shad hatch abundance (R2 = 0.43 and 0.42), and high for larval gizzard shad survivor abundance (R2 = 0.79). The trophic status models included agricultural land use/cover and an HGM predictor, whereas the zooplankton models had few HGM predictors. The larval gizzard shad models had the highest complexity, including more than one HGM feature and food web components. We demonstrate the importance of integrating land use/cover, HGM features, and food web interactions to investigate critical interactions and feedbacks among physical, chemical, and biological components of linked land-water ecosystems.

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer's disease progression.

PubMed

Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Celaya-Padilla, José M; Galván-Tejada, Jorge I; Treviño, Victor; Tamez-Peña, Jose

2014-10-01

Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different ([Formula: see text]). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.

Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions.

PubMed

Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X

2010-05-01

Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.

An audiovisual emotion recognition system

NASA Astrophysics Data System (ADS)

Han, Yi; Wang, Guoyin; Yang, Yong; He, Kun

2007-12-01

Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both regarded as emotional information which is playing an important role in human-computer interaction. Based on our previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules include speech enhancement for eliminating noises, rapid face detection for locating face from background image, example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier. Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10 facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected due to the synchronization when speech and video fused together. The experiment results have demonstrated that this system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules fused recognition will become the trend of emotion recognition in the future.

Feature level fusion of hand and face biometrics

NASA Astrophysics Data System (ADS)

Ross, Arun A.; Govindarajan, Rohin

2005-03-01

Multibiometric systems utilize the evidence presented by multiple biometric sources (e.g., face and fingerprint, multiple fingers of a user, multiple matchers, etc.) in order to determine or verify the identity of an individual. Information from multiple sources can be consolidated in several distinct levels, including the feature extraction level, match score level and decision level. While fusion at the match score and decision levels have been extensively studied in the literature, fusion at the feature level is a relatively understudied problem. In this paper we discuss fusion at the feature level in 3 different scenarios: (i) fusion of PCA and LDA coefficients of face; (ii) fusion of LDA coefficients corresponding to the R,G,B channels of a face image; (iii) fusion of face and hand modalities. Preliminary results are encouraging and help in highlighting the pros and cons of performing fusion at this level. The primary motivation of this work is to demonstrate the viability of such a fusion and to underscore the importance of pursuing further research in this direction.

Perceptual approaches to finding features in data

NASA Astrophysics Data System (ADS)

Rogowitz, Bernice E.

2013-03-01

Electronic imaging applications hinge on the ability to discover features in data. For example, doctors examine diagnostic images for tumors, broken bones and changes in metabolic activity. Financial analysts explore visualizations of market data to find correlations, outliers and interaction effects. Seismologists look for signatures in geological data to tell them where to drill or where an earthquake may begin. These data are very diverse, including images, numbers, graphs, 3-D graphics, and text, and are growing exponentially, largely through the rise in automatic data collection technologies such as sensors and digital imaging. This paper explores important trends in the art and science of finding features in data, such as the tension between bottom-up and top-down processing, the semantics of features, and the integration of human- and algorithm-based approaches. This story is told from the perspective of the IS and T/SPIE Conference on Human Vision and Electronic Imaging (HVEI), which has fostered research at the intersection between human perception and the evolution of new technologies.

Point cloud registration from local feature correspondences-Evaluation on challenging datasets.

PubMed

Petricek, Tomas; Svoboda, Tomas

2017-01-01

Registration of laser scans, or point clouds in general, is a crucial step of localization and mapping with mobile robots or in object modeling pipelines. A coarse alignment of the point clouds is generally needed before applying local methods such as the Iterative Closest Point (ICP) algorithm. We propose a feature-based approach to point cloud registration and evaluate the proposed method and its individual components on challenging real-world datasets. For a moderate overlap between the laser scans, the method provides a superior registration accuracy compared to state-of-the-art methods including Generalized ICP, 3D Normal-Distribution Transform, Fast Point-Feature Histograms, and 4-Points Congruent Sets. Compared to the surface normals, the points as the underlying features yield higher performance in both keypoint detection and establishing local reference frames. Moreover, sign disambiguation of the basis vectors proves to be an important aspect in creating repeatable local reference frames. A novel method for sign disambiguation is proposed which yields highly repeatable reference frames.

An 'inflammatory' variant of solar purpura: a simulant of leukocytoclastic vasculitis and neutrophilic dermatoses.

PubMed

Wood, Benjamin A; LeBoit, Philip E

2013-08-01

To study the clinical and pathological features of cases of apparent solar purpura, with attention to the recently described phenomenon of inflammatory changes within otherwise typical lesions. We studied 95 cases diagnosed as solar purpura and identified 10 cases (10.5%) in which significant neutrophilic inflammation was present, potentially simulating a leukocytoclastic vasculitis or neutrophilic dermatosis. An additional three cases were identified in subsequent routine practice. The clinical features, including follow-up for subsequent development of vasculitis and histological features were studied. In all cases the histological features were typical of solar purpura, with the exception of inflammatory changes, typically associated with clefting of elastotic stroma. Clinical follow-up information was available for all patients and none developed subsequent evidence of a cutaneous or systemic vasculitis or neutrophilic dermatosis. Inflammatory changes appear to be more frequent in solar purpura than is generally recognised. Awareness of this histological variation and correlation with the clinical findings and evolution is important in avoiding misdiagnosis.

Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support.

PubMed

Miller, Kristen; Mosby, Danielle; Capan, Muge; Kowalski, Rebecca; Ratwani, Raj; Noaiseh, Yaman; Kraft, Rachel; Schwartz, Sanford; Weintraub, William S; Arnold, Ryan

2018-05-01

Provider acceptance and associated patient outcomes are widely discussed in the evaluation of clinical decision support systems (CDSSs), but critical design criteria for tools have generally been overlooked. The objective of this work is to inform electronic health record alert optimization and clinical practice workflow by identifying, compiling, and reporting design recommendations for CDSS to support the efficient, effective, and timely delivery of high-quality care. A narrative review was conducted from 2000 to 2016 in PubMed and The Journal of Human Factors and Ergonomics Society to identify papers that discussed/recommended design features of CDSSs that are associated with the success of these systems. Fourteen papers were included as meeting the criteria and were found to have a total of 42 unique recommendations; 11 were classified as interface features, 10 as information features, and 21 as interaction features. Features are defined and described, providing actionable guidance that can be applied to CDSS development and policy. To our knowledge, no reviews have been completed that discuss/recommend design features of CDSS at this scale, and thus we found that this was important for the body of literature. The recommendations identified in this narrative review will help to optimize design, organization, management, presentation, and utilization of information through presentation, content, and function. The designation of 3 categories (interface, information, and interaction) should be further evaluated to determine the critical importance of the categories. Future work will determine how to prioritize them with limited resources for designers and developers in order to maximize the clinical utility of CDSS. This review will expand the field of knowledge and provide a novel organization structure to identify key recommendations for CDSS.

Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM

NASA Astrophysics Data System (ADS)

Sosa, Germán. D.; Cruz-Roa, Angel; González, Fabio A.

2015-01-01

This work addresses the problem of lung sound classification, in particular, the problem of distinguishing between wheeze and normal sounds. Wheezing sound detection is an important step to associate lung sounds with an abnormal state of the respiratory system, usually associated with tuberculosis or another chronic obstructive pulmonary diseases (COPD). The paper presents an approach for automatic lung sound classification, which uses different state-of-the-art sound features in combination with a C-weighted support vector machine (SVM) classifier that works better for unbalanced data. Feature extraction methods used here are commonly applied in speech recognition and related problems thanks to the fact that they capture the most informative spectral content from the original signals. The evaluated methods were: Fourier transform (FT), wavelet decomposition using Wavelet Packet Transform bank of filters (WPT) and Mel Frequency Cepstral Coefficients (MFCC). For comparison, we evaluated and contrasted the proposed approach against previous works using different combination of features and/or classifiers. The different methods were evaluated on a set of lung sounds including normal and wheezing sounds. A leave-two-out per-case cross-validation approach was used, which, in each fold, chooses as validation set a couple of cases, one including normal sounds and the other including wheezing sounds. Experimental results were reported in terms of traditional classification performance measures: sensitivity, specificity and balanced accuracy. Our best results using the suggested approach, C-weighted SVM and MFCC, achieve a 82.1% of balanced accuracy obtaining the best result for this problem until now. These results suggest that supervised classifiers based on kernel methods are able to learn better models for this challenging classification problem even using the same feature extraction methods.

Explosive Cyclogenesis Over the Eastern United States.

NASA Astrophysics Data System (ADS)

MacDonald, Bruce Calvin

Cases of explosive cyclogenesis occurring over the east central United States are identified and analyzed. Other selected cases of weak or nonintensifying cyclones over the same area are identified and studied for comparative purposes. Signatures of explosively deepening cyclones (bombs) are derived from the analyses, including vertical profiles of vorticity, divergence, and latent heating, and also the relative importance of terms in the vorticity tendency equation and the relative importance of convective and stable latent heating. Composite analyses for the differing phases of bomb development and for regular cyclones are presented. Analyses of individual cases reveal the importance of a low-level jet streak, low-level moisture content, and moisture gradients in the lower troposphere. A numerical model is used to further examine the important processes in explosive cyclogenesis. A mesoscale feature is introduced to improve the prediction of sea -level pressure. This feature is based on the tendency of the large scale height field and vorticity field to adjust concurrently at each time step. The model is also used to provide air parcel trajectories to indicate the importance of parcels with high vorticity and moisture content as an ingredient in explosive cyclogenesis. Sensitivity studies are carried out with the model in order to determine the effect of changes in the initial vorticity and moisture field on cyclogenesis.

Why replication is important in landscape genetics: American black bear in the Rocky Mountains

Treesearch

R. A. Short Bull; Samuel Cushman; R. Mace; T. Chilton; K. C. Kendall; E. L. Landguth; Michael Schwartz; Kevin McKelvey; Fred W. Allendorf; G. Luikart

2011-01-01

We investigated how landscape features influence gene flow of black bears by testing the relative support for 36 alternative landscape resistance hypotheses, including isolation by distance (IBD) in each of 12 study areas in the north central U.S. Rocky Mountains. The study areas all contained the same basic elements, but differed in extent of forest fragmentation,...

Guillain-Barré syndrome. Review and presentation of a case with pedal manifestations.

PubMed

Viegas, G V

1997-05-01

Guillan-Barré syndrome is an acute, symmetrical polyneuropathy with distinctive features. The early clinical course involves painful paresthesia that is usually followed by proximal motor weakness. Albuminocytologic dissociation in the cerebrospinal fluid is considered diagnostically important. Therapy ranges from supportive measures including physical therapy to surgical intervention for residual deformities. A case with pedal manifestations is presented.

Modal analysis applied to circular, rectangular, and coaxial waveguides

NASA Technical Reports Server (NTRS)

Hoppe, D. J.

1988-01-01

Recent developments in the analysis of various waveguide components and feedhorns using Modal Analysis (Mode Matching Method) are summarized. A brief description of the theory is presented, and the important features of the method are pointed out. Specific examples in circular, rectangular, and coaxial waveguides are included, with comparisons between the theory and experimental measurements. Extensions to the methods are described.

Roads in landscape modeling: a case study of a road data layer and use in the interior northwest landscape analysis system.

Treesearch

Marti Aitken; Jane L. Hayes

2006-01-01

Roads are important ecological features of forest landscapes, but their cause-and effect relationships with other ecosystem components are only recently becoming included in integrated landscape analyses. Simulation models can help us to understand how forested landscapes respond over time to disturbance and socioeconomic factors, and potentially to address the...

Mitigating climate change through afforestation: new cost estimates for the United States

Treesearch

Anne Sofie Elberg Nielsen; Andrew J. Plantinga; Ralph J. Alig

2014-01-01

We provide new cost estimates for carbon sequestration through afforestation in the U.S. We extend existing studies of carbon sequestration costs in several important ways, while ensuring the transparency of our approach. Our costs estimates have five distinguishing features: (1) we estimate costs for each county in the contiguous U.S., (2) we include afforestation of...

CCProf: exploring conformational change profile of proteins

PubMed Central

Chang, Che-Wei; Chou, Chai-Wei; Chang, Darby Tien-Hao

2016-01-01

In many biological processes, proteins have important interactions with various molecules such as proteins, ions or ligands. Many proteins undergo conformational changes upon these interactions, where regions with large conformational changes are critical to the interactions. This work presents the CCProf platform, which provides conformational changes of entire proteins, named conformational change profile (CCP) in the context. CCProf aims to be a platform where users can study potential causes of novel conformational changes. It provides 10 biological features, including conformational change, potential binding target site, secondary structure, conservation, disorder propensity, hydropathy propensity, sequence domain, structural domain, phosphorylation site and catalytic site. All these information are integrated into a well-aligned view, so that researchers can capture important relevance between different biological features visually. The CCProf contains 986 187 protein structure pairs for 3123 proteins. In addition, CCProf provides a 3D view in which users can see the protein structures before and after conformational changes as well as binding targets that induce conformational changes. All information (e.g. CCP, binding targets and protein structures) shown in CCProf, including intermediate data are available for download to expedite further analyses. Database URL: http://zoro.ee.ncku.edu.tw/ccprof/ PMID:27016699

Pragmatic characteristics of patient-reported outcome measures are important for use in clinical practice.

PubMed

Kroenke, Kurt; Monahan, Patrick O; Kean, Jacob

2015-09-01

Measures for assessing patient-reported outcomes (PROs) that may have initially been developed for research are increasingly being recommended for use in clinical practice as well. Although psychometric rigor is essential, this article focuses on pragmatic characteristics of PROs that may enhance uptake into clinical practice. Three sources were drawn on in identifying pragmatic criteria for PROs: (1) selected literature review including recommendations by other expert groups; (2) key features of several model public domain PROs; and (3) the authors' experience in developing practical PROs. Eight characteristics of a practical PRO include: (1) actionability (i.e., scores guide diagnostic or therapeutic actions/decision making); (2) appropriateness for the relevant clinical setting; (3) universality (i.e., for screening, severity assessment, and monitoring across multiple conditions); (4) self-administration; (5) item features (number of items and bundling issues); (6) response options (option number and dimensions, uniform vs. varying options, time frame, intervals between options); (7) scoring (simplicity and interpretability); and (8) accessibility (nonproprietary, downloadable, available in different languages and for vulnerable groups, and incorporated into electronic health records). Balancing psychometric and pragmatic factors in the development of PROs is important for accelerating the incorporation of PROs into clinical practice. Published by Elsevier Inc.

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.

PubMed

Bokulich, Nicholas A; Kaehler, Benjamin D; Rideout, Jai Ram; Dillon, Matthew; Bolyen, Evan; Knight, Rob; Huttley, Gavin A; Gregory Caporaso, J

2018-05-17

Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

Role of Importance and Distinctiveness of Semantic Features in People with Aphasia: A Replication Study

ERIC Educational Resources Information Center

Mason-Baughman, Mary Beth; Wallace, Sarah E.

2014-01-01

Previous studies suggest that people with aphasia have incomplete lexical-semantic representations with decreased low-importance distinctive (LID) feature knowledge. In addition, decreased LID feature knowledge correlates with ability to discriminate among semantically related words. The current study seeks to replicate and extend previous…

Effect of current federal regulations on handgun safety features.

PubMed

Milne, John S; Hargarten, Stephen W; Kellermann, Arthur L; Wintemute, Garen J

2003-01-01

In the late 1960s, the Bureau of Alcohol, Tobacco, and Firearms implemented the "factoring criteria," a set of minimum size and safety standards required for any handgun imported into the United States. These standards, however, were not applied to guns manufactured domestically. We determine whether extending the factoring criteria to all handguns sold in the United States, as has been proposed in Congress, would increase the likelihood that safety devices would be included in new handgun designs. Imported and domestic handgun models produced in 1996 were examined to determine the prevalence of 4 passively acting safety devices on pistols and 1 passive safety device on revolvers. Domestic models were also scored against the factoring criteria. Compared with domestic pistol models, imported pistols were more likely to include a firing pin block (odds ratio [OR] 2.43; 95% confidence interval [CI] 1.54 to 3.85) and a loaded chamber indicator (OR 1.59; 95% CI 0.98 to 2.56). Domestic pistol models that already met the factoring criteria were more likely to include a loaded chamber indicator (OR 12.05; 95% CI 2.74 to 53.02), a grip safety (OR 24.12; 95% CI 7.8 to 74.33), and a firing pin block (OR 4.92; 95% CI 2.35 to 10.29) than domestic models that did not meet the criteria. Although pistol models that meet the factoring criteria are more likely to contain safety devices than those that do not, the net effect is modest. Thus, the factoring criteria alone are insufficient to ensure consistent incorporation of safety features into new handgun designs.

Functional connectivity supporting the selective maintenance of feature-location binding in visual working memory

PubMed Central

Takahama, Sachiko; Saiki, Jun

2014-01-01

Information on an object's features bound to its location is very important for maintaining object representations in visual working memory. Interactions with dynamic multi-dimensional objects in an external environment require complex cognitive control, including the selective maintenance of feature-location binding. Here, we used event-related functional magnetic resonance imaging to investigate brain activity and functional connectivity related to the maintenance of complex feature-location binding. Participants were required to detect task-relevant changes in feature-location binding between objects defined by color, orientation, and location. We compared a complex binding task requiring complex feature-location binding (color-orientation-location) with a simple binding task in which simple feature-location binding, such as color-location, was task-relevant and the other feature was task-irrelevant. Univariate analyses showed that the dorsolateral prefrontal cortex (DLPFC), hippocampus, and frontoparietal network were activated during the maintenance of complex feature-location binding. Functional connectivity analyses indicated cooperation between the inferior precentral sulcus (infPreCS), DLPFC, and hippocampus during the maintenance of complex feature-location binding. In contrast, the connectivity for the spatial updating of simple feature-location binding determined by reanalyzing the data from Takahama et al. (2010) demonstrated that the superior parietal lobule (SPL) cooperated with the DLPFC and hippocampus. These results suggest that the connectivity for complex feature-location binding does not simply reflect general memory load and that the DLPFC and hippocampus flexibly modulate the dorsal frontoparietal network, depending on the task requirements, with the infPreCS involved in the maintenance of complex feature-location binding and the SPL involved in the spatial updating of simple feature-location binding. PMID:24917833

Functional connectivity supporting the selective maintenance of feature-location binding in visual working memory.

PubMed

Takahama, Sachiko; Saiki, Jun

2014-01-01

Information on an object's features bound to its location is very important for maintaining object representations in visual working memory. Interactions with dynamic multi-dimensional objects in an external environment require complex cognitive control, including the selective maintenance of feature-location binding. Here, we used event-related functional magnetic resonance imaging to investigate brain activity and functional connectivity related to the maintenance of complex feature-location binding. Participants were required to detect task-relevant changes in feature-location binding between objects defined by color, orientation, and location. We compared a complex binding task requiring complex feature-location binding (color-orientation-location) with a simple binding task in which simple feature-location binding, such as color-location, was task-relevant and the other feature was task-irrelevant. Univariate analyses showed that the dorsolateral prefrontal cortex (DLPFC), hippocampus, and frontoparietal network were activated during the maintenance of complex feature-location binding. Functional connectivity analyses indicated cooperation between the inferior precentral sulcus (infPreCS), DLPFC, and hippocampus during the maintenance of complex feature-location binding. In contrast, the connectivity for the spatial updating of simple feature-location binding determined by reanalyzing the data from Takahama et al. (2010) demonstrated that the superior parietal lobule (SPL) cooperated with the DLPFC and hippocampus. These results suggest that the connectivity for complex feature-location binding does not simply reflect general memory load and that the DLPFC and hippocampus flexibly modulate the dorsal frontoparietal network, depending on the task requirements, with the infPreCS involved in the maintenance of complex feature-location binding and the SPL involved in the spatial updating of simple feature-location binding.

Permutation importance: a corrected feature importance measure.

PubMed

Altmann, André; Toloşi, Laura; Sander, Oliver; Lengauer, Thomas

2010-05-15

In life sciences, interpretability of machine learning models is as important as their prediction accuracy. Linear models are probably the most frequently used methods for assessing feature relevance, despite their relative inflexibility. However, in the past years effective estimators of feature relevance have been derived for highly complex or non-parametric models such as support vector machines and RandomForest (RF) models. Recently, it has been observed that RF models are biased in such a way that categorical variables with a large number of categories are preferred. In this work, we introduce a heuristic for normalizing feature importance measures that can correct the feature importance bias. The method is based on repeated permutations of the outcome vector for estimating the distribution of measured importance for each variable in a non-informative setting. The P-value of the observed importance provides a corrected measure of feature importance. We apply our method to simulated data and demonstrate that (i) non-informative predictors do not receive significant P-values, (ii) informative variables can successfully be recovered among non-informative variables and (iii) P-values computed with permutation importance (PIMP) are very helpful for deciding the significance of variables, and therefore improve model interpretability. Furthermore, PIMP was used to correct RF-based importance measures for two real-world case studies. We propose an improved RF model that uses the significant variables with respect to the PIMP measure and show that its prediction accuracy is superior to that of other existing models. R code for the method presented in this article is available at http://www.mpi-inf.mpg.de/ approximately altmann/download/PIMP.R CONTACT: altmann@mpi-inf.mpg.de, laura.tolosi@mpi-inf.mpg.de Supplementary data are available at Bioinformatics online.

Similarity-dissimilarity plot for visualization of high dimensional data in biomedical pattern classification.

PubMed

Arif, Muhammad

2012-06-01

In pattern classification problems, feature extraction is an important step. Quality of features in discriminating different classes plays an important role in pattern classification problems. In real life, pattern classification may require high dimensional feature space and it is impossible to visualize the feature space if the dimension of feature space is greater than four. In this paper, we have proposed a Similarity-Dissimilarity plot which can project high dimensional space to a two dimensional space while retaining important characteristics required to assess the discrimination quality of the features. Similarity-dissimilarity plot can reveal information about the amount of overlap of features of different classes. Separable data points of different classes will also be visible on the plot which can be classified correctly using appropriate classifier. Hence, approximate classification accuracy can be predicted. Moreover, it is possible to know about whom class the misclassified data points will be confused by the classifier. Outlier data points can also be located on the similarity-dissimilarity plot. Various examples of synthetic data are used to highlight important characteristics of the proposed plot. Some real life examples from biomedical data are also used for the analysis. The proposed plot is independent of number of dimensions of the feature space.

PrimerMapper: high throughput primer design and graphical assembly for PCR and SNP detection

PubMed Central

O’Halloran, Damien M.

2016-01-01

Primer design represents a widely employed gambit in diverse molecular applications including PCR, sequencing, and probe hybridization. Variations of PCR, including primer walking, allele-specific PCR, and nested PCR provide specialized validation and detection protocols for molecular analyses that often require screening large numbers of DNA fragments. In these cases, automated sequence retrieval and processing become important features, and furthermore, a graphic that provides the user with a visual guide to the distribution of designed primers across targets is most helpful in quickly ascertaining primer coverage. To this end, I describe here, PrimerMapper, which provides a comprehensive graphical user interface that designs robust primers from any number of inputted sequences while providing the user with both, graphical maps of primer distribution for each inputted sequence, and also a global assembled map of all inputted sequences with designed primers. PrimerMapper also enables the visualization of graphical maps within a browser and allows the user to draw new primers directly onto the webpage. Other features of PrimerMapper include allele-specific design features for SNP genotyping, a remote BLAST window to NCBI databases, and remote sequence retrieval from GenBank and dbSNP. PrimerMapper is hosted at GitHub and freely available without restriction. PMID:26853558

Relationships between Genetic Variations of PNPLA3, TM6SF2 and Histological Features of Nonalcoholic Fatty Liver Disease in Japan.

PubMed

Akuta, Norio; Kawamura, Yusuke; Arase, Yasuji; Suzuki, Fumitaka; Sezaki, Hitomi; Hosaka, Tetsuya; Kobayashi, Masahiro; Kobayashi, Mariko; Saitoh, Satoshi; Suzuki, Yoshiyuki; Ikeda, Kenji; Kumada, Hiromitsu

2016-05-23

It is important to determine the noninvasive parameters of histological features in nonalcoholic fatty liver disease (NAFLD). The aim of this study was to investigate the value of genetic variations as surrogate markers of histological features. The parameters that affected the histological features of NAFLD were investigated in 211 Japanese patients with biopsy-proven NAFLD. The relationships between genetic variations in PNPLA3 rs738409 or TM6SF2 rs58542926 and histological features were analyzed. Furthermore, the impact of genetic variations that affected the pathological criteria for the diagnosis of nonalcoholic steatohepatitis (NASH) (Matteoni classification and NAFLD activity score) was evaluated. The fibrosis stage of PNPLA3 GG was significantly more progressive than that of CG by multiple comparisons. Multivariate analysis identified PNPLA3 genotypes as predictors of fibrosis of stage 2 or more, but the impact tended to decrease at stage 3 or greater. There were no significant differences among the histological features of the three genotypes of TM6SF2. PNPLA3 genotypes partly affected the definition of NASH by the NAFLD activity score, but TM6SF2 genotypes did not affect the definition of NASH. In Japanese patients with biopsy-proven NAFLD, PNPLA3 genotypes may partly affect histological features, including stage of fibrosis, but the TM6SF2 genotype does not affect histological features.

Prevalence of histological features of idiopathic noncirrhotic portal hypertension in general population: a retrospective study of incidental liver biopsies.

PubMed

Zuo, Chunlai; Chumbalkar, Vaibhav; Ells, Peter F; Bonville, Daniel J; Lee, Hwajeong

2017-09-01

Idiopathic noncirrhotic portal hypertension (INCPH) is associated with histologic changes secondary to obliterative portal venopathy without cirrhosis. We studied the prevalence of individual histological features of INCPH in liver biopsies obtained incidentally during unrelated elective procedures and in elective liver biopsies with the diagnosis of fatty liver disease. A total of 53 incidental liver biopsies obtained intraoperatively during unrelated elective procedures and an additional 28 elective biopsies with the diagnosis of fatty liver disease without portal hypertension and cirrhosis were studied. Various histologic features of INCPH were evaluated. Shunt vessel (30%), phlebosclerosis (27%), increased number of portal vessels (19%) and incomplete septa (17%) were common in these liver biopsies after confounding factors such as co-existing fatty liver disease or fibrosis were excluded. At least one feature of INCPH was noted in 90% of the biopsies. Eight (10%) biopsies showed 5-6 features of INCPH. In total, 11 (14%) of 81 patients had risk factors associated with INCPH, including hypercoagulability, autoimmune disease, exposure to drugs, and infections. No patient had portal hypertension at the end of the follow-up. The histologic features of INCPH are seen in incidental liver biopsies and fatty liver disease without portal hypertension. Ten percent of the biopsies show 5-6 features of INCPH without portal hypertension. Interpreting histologic features in the right clinical context is important for proper patient care.

Multiple approaches to valuation of conservation design and low-impact development features in residential subdivisions.

PubMed

Bowman, Troy; Tyndall, John C; Thompson, Janette; Kliebenstein, James; Colletti, Joe P

2012-08-15

Residents, developers and civic officials are often faced with difficult decisions about appropriate land uses in and around metropolitan boundaries. Urban expansion brings with it the potential for negative environmental impacts, but there are alternatives, such as conservation subdivision design (CSD) or low-impact development (LID), which offer the possibility of mitigating some of these effects at the development site. Many urban planning jurisdictions across the Midwest do not currently have any examples of these designs and lack information to identify public support or barriers to use of these methods. This is a case study examining consumer value for conservation and low-impact design features in one housing market by using four different valuation techniques to estimate residents' willingness to pay for CSD and LID features in residential subdivisions. A contingent valuation survey of 1804 residents in Ames, IA assessed familiarity with and perceptions of subdivision development and used an ordered value approach to estimate willingness to pay for CSD and LID features. A majority of residents were not familiar with CSD or LID practices. Residents indicated a willingness to pay for most CSD and LID features with the exception of clustered housing. Gender, age, income, familiarity with LID practices, perceptions of attractiveness of features and the perceived effect of CSD and LID features on ease of future home sales were important factors influencing residents' willingness to pay. A hypothetical referendum measured willingness to pay for tax-funded conservation land purchases and estimated that a property tax of around $50 would be the maximum increase that would pass. Twenty-seven survey respondents participated in a subsequent series of experimental real estate negotiations that used an experimental auction mechanism to estimate willingness to pay for CSD and LID features. Participants indicated that clustered housing (with interspersed preserved forest or open space areas), rain gardens, and neighborhood streams with a forested buffer were the features they were most willing to pay for. Participants were not willing to pay for neighborhood streams without buffers. Finally, a spatial hedonic price model using 2093 homes in Ames, IA was used to estimate the effect of public and private open space on housing values. The model indicated that presence of neighborhood association-owned forest and water features as well as proximity to public parks had significant positive effects on housing prices. However, proximity to a public lake had a negative effect on home values. The four methods used in this study include both stated and revealed preference techniques. Although the relative magnitude of value expressed varied, all methods indicated that residents value CSD and LID subdivision features. Subdivision features that included explicit environmental benefits were also consistently preferred over features that did not. Familiarity with alternative designs was an important factor influencing resident willingness to pay for neighborhood features, and developers and civic officials should consider ways to educate citizens about CSD and LID development techniques to increase interest in these designs. Published by Elsevier Ltd.

Evaluation of Sexual Communication Message Strategies

PubMed Central

2011-01-01

Parent-child communication about sex is an important proximal reproductive health outcome. But while campaigns to promote it such as the Parents Speak Up National Campaign (PSUNC) have been effective, little is known about how messages influence parental cognitions and behavior. This study examines which message features explain responses to sexual communication messages. We content analyzed 4 PSUNC ads to identify specific, measurable message and advertising execution features. We then develop quantitative measures of those features, including message strategies, marketing strategies, and voice and other stylistic features, and merged the resulting data into a dataset drawn from a national media tracking survey of the campaign. Finally, we conducted multivariable logistic regression models to identify relationships between message content and ad reactions/receptivity, and between ad reactions/receptivity and parents' cognitions related to sexual communication included in the campaign's conceptual model. We found that overall parents were highly receptive to the PSUNC ads. We did not find significant associations between message content and ad reactions/receptivity. However, we found that reactions/receptivity to specific PSUNC ads were associated with increased norms, self-efficacy, short- and long-term expectations about parent-child sexual communication, as theorized in the conceptual model. This study extends previous research and methods to analyze message content and reactions/receptivity. The results confirm and extend previous PSUNC campaign evaluation and provide further evidence for the conceptual model. Future research should examine additional message content features and the effects of reactions/receptivity. PMID:21599875

Ultrastructure of preimaginal stages of Piophila megastigmata McAlpine, 1978 (Diptera, Piophilidae): a fly of forensic importance.

PubMed

Paños, A; Arnaldos, M I; García, M D; Ubero-Pascal, N

2013-11-01

Piophila Fallén, 1810 is a genus of small flies composed of two species: Piophila casei (P. casei ) (Linnaeus, 1758), worldwide distributed, and Piophila megastigmata (P. megastigmata ) McAlpine, 1978, recently referred in the Palaearctic Region, from the Iberian Peninsula. Both species share ecological niche and are very interesting for forensic purposes, since they are present in carrion in advance stages of decay and have been found to be related to human corpses. The immature stages of P. megastigmata have ever been described, so this paper gives the ultrastructural morphologies of all preimaginal stages of P. megastigmata studied by light microscopy and scanning electron microscopy (SEM). Particular attention is given to pseudocephalon features—antenna, maxillary palps, facial mask, etc.—cephalopharyngeal skeleton, anterior and posterior spiracles, tegumentary sculpturing, and anal division among others. A comparative analysis of the main distinguishing features is made in order to understand how those features evolve along the developmental process, while larvae II and III are morphologically similar to each other, the larva I shows particular features. Larvae of all stages and pupae are easily distinguishable from other Diptera of forensic importance just based on the presence of trichoid sensilla associated to respiratory slits, instead of peristigmatig tufts, as well as on thewell-known disposition of anal papillae. The shapes of both dorsal edge at the basal part of mouthhook and dorsal bridge of cephalopharyngeal skeleton, and the tegumental ornamentationmay be considered as good features to distinguish the Piophila species, especially for P. megastigmata and P. casei . At the SEM level, shape, number, and arrangement of oral combs, oral ridges, sensilla of maxillary palpus, papillae of anterior spiracle, scales of spiracular field, and posterior spiracles represent good features to distinguish P. megastigmata from P. casei, but further studies will be necessary in West-Paleartic specimens of latter species. The key for identifying third instar larvae of forensically important Piophilidae in the Iberian Peninsula has been updated to include P. megastigmata.

Imaging trace element distributions in single organelles and subcellular features

NASA Astrophysics Data System (ADS)

Kashiv, Yoav; Austin, Jotham R.; Lai, Barry; Rose, Volker; Vogt, Stefan; El-Muayed, Malek

2016-02-01

The distributions of chemical elements within cells are of prime importance in a wide range of basic and applied biochemical research. An example is the role of the subcellular Zn distribution in Zn homeostasis in insulin producing pancreatic beta cells and the development of type 2 diabetes mellitus. We combined transmission electron microscopy with micro- and nano-synchrotron X-ray fluorescence to image unequivocally for the first time, to the best of our knowledge, the natural elemental distributions, including those of trace elements, in single organelles and other subcellular features. Detected elements include Cl, K, Ca, Co, Ni, Cu, Zn and Cd (which some cells were supplemented with). Cell samples were prepared by a technique that minimally affects the natural elemental concentrations and distributions, and without using fluorescent indicators. It could likely be applied to all cell types and provide new biochemical insights at the single organelle level not available from organelle population level studies.

The geographical vector in distribution of genetic diversity for Clonorchis sinensis.

PubMed

Solodovnik, Daria A; Tatonova, Yulia V; Burkovskaya, Polina V

2018-01-01

Clonorchis sinensis, the causative agent of clonorchiasis, is one of the most important parasites that inhabit countries of East and Southeast Asia. In this study, we validated the existence of a geographical vector for C. sinensis using the partial cox1 mtDNA gene, which includes a conserved region. The samples of parasite were divided into groups corresponding to three river basins, and the size of the conserved region had a strong tendency to increase from the northernmost to the southernmost samples. This indicates the availability of the geographical vector in distribution of genetic diversity. A vector is a quantity that is characterized by magnitude and direction. Geographical vector obtained in cox1 gene of C. sinensis has both these features. The reasons for the occurrence of this feature, including the influence of intermediate and definitive hosts on vector formation, and the possibility of its use for clonorchiasis monitoring are discussed. Graphical abstract ᅟ.

Genome Analysis of Streptococcus pyogenes Associated with Pharyngitis and Skin Infections

PubMed Central

Ibrahim, Joe; Eisen, Jonathan A.; Jospin, Guillaume; Coil, David A.; Khazen, Georges

2016-01-01

Streptococcus pyogenes is a very important human pathogen, commonly associated with skin or throat infections but can also cause life-threatening situations including sepsis, streptococcal toxic shock syndrome, and necrotizing fasciitis. Various studies involving typing and molecular characterization of S. pyogenes have been published to date; however next-generation sequencing (NGS) studies provide a comprehensive collection of an organism’s genetic variation. In this study, the genomes of nine S. pyogenes isolates associated with pharyngitis and skin infection were sequenced and studied for the presence of virulence genes, resistance elements, prophages, genomic recombination, and other genomic features. Additionally, a comparative phylogenetic analysis of the isolates with global clones highlighted their possible evolutionary lineage and their site of infection. The genomes were found to also house a multitude of features including gene regulation systems, virulence factors and antimicrobial resistance mechanisms. PMID:27977735

Gender differences in contributions of emotion to psychopathy and antisocial personality disorder.

PubMed

Rogstad, Jill E; Rogers, Richard

2008-12-01

Traditional conceptualizations of psychopathy highlight the importance of affective features as they relate to social deviance; however, little empirical research has actually investigated specific roles of emotion and emotion processing with respect to antisocial conduct. Antisocial personality disorder (APD), prevalent in forensic populations, is commonly associated with psychopathy despite the notable omission of such core affective features in its diagnosis. In this paper, we review the empirical literature on the contribution of emotion to psychopathy and APD, highlighting in particular research on emotion processing and various facets of emotional expression, including empathy and alexithymia. Research findings are discussed on gender differences in emotional functioning and their likely effects on the assessment of psychopathy and APD. Given the known gender differences in the expressions of emotion, the article concludes with recommendations to bridge research for different offender groups, including psychopathy and APD.

Cohort Profile: Generation Scotland: Scottish Family Health Study (GS:SFHS). The study, its participants and their potential for genetic research on health and illness.

PubMed

Smith, Blair H; Campbell, Archie; Linksted, Pamela; Fitzpatrick, Bridie; Jackson, Cathy; Kerr, Shona M; Deary, Ian J; Macintyre, Donald J; Campbell, Harry; McGilchrist, Mark; Hocking, Lynne J; Wisely, Lucy; Ford, Ian; Lindsay, Robert S; Morton, Robin; Palmer, Colin N A; Dominiczak, Anna F; Porteous, David J; Morris, Andrew D

2013-06-01

GS:SFHS is a family-based genetic epidemiology study with DNA and socio-demographic and clinical data from about 24 000 volunteers across Scotland aged 18-98 years, from February 2006 to March 2011. Biological samples and anonymized data form a resource for research on the genetics of health, disease and quantitative traits of current and projected public health importance. Specific and important features of GS:SFHS include the family-based recruitment, with the intent of obtaining family groups; the breadth and depth of phenotype information, including detailed data on cognitive function, personality traits and mental health; consent and mechanisms for linkage of all data to comprehensive routine health-care records; and 'broad' consent from participants to use their data and samples for a wide range of medical research, including commercial research, and for re-contact for the potential collection of other data or samples, or for participation in related studies and the design and review of the protocol in parallel with in-depth sociological research on (potential) participants and users of the research outcomes. These features were designed to maximize the power of the resource to identify, replicate or control for genetic factors associated with a wide spectrum of illnesses and risk factors, both now and in the future.

Subduction, Extension, and a Mantle Plume in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Hawley, W. B.; Allen, R. M.; Richards, M. A.

2016-12-01

Subduction zones are some of the most important systems that control the dynamics and evolution of the earth. The Cascadia Subduction Zone offers a unique natural laboratory for understanding the subduction process, and how subduction interacts with other large-scale geodynamical phenomena. The small size of the Juan de Fuca (JdF) plate and the proximity of the system to the Yellowstone Hotspot and the extensional Basin and Range province allow for detailed study of the effects these important systems have on each other. We present both a P-wave and an S-wave tomographic model of the Pacific Northwestern United States using regional seismic arrays, including the amphibious Cascadia Initiative. These models share important features, such as the Yellowstone plume, the subducting JdF slab, a gap in the subducting slab, and a low-velocity feature beneath the shallowest portions of the slab. But subtle differences in these features between the models—the size of the gap in the subducting JdF slab and the shape of the Yellowstone plume shaft above the transition zone, for example—provide physical insight into the interpretation of these models. The physics that we infer from our seismic tomography and other studies of the region will refine our understanding of subduction zones worldwide, and will help to identify targets for future amphibious seismic array studies. The discovery of a pronounced low-velocity feature beneath the JdF slab as it subducts beneath the coastal Pacific Northwest is, thus far, the most surprising result from our imaging work, and implies a heretofore unanticipated regime of dynamical interaction between the sublithospheric oceanic asthenosphere and the subduction process. Such discoveries are made possible, and rendered interpretable, by ever-increasing resolution that the Cascadia Initiative affords seismic tomography models.

A discontinuous Galerkin method for gravity-driven viscous fingering instabilities in porous media

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

Scovazzi, G.; Gerstenberger, A.; Collis, S. S.

2013-01-01

We present a new approach to the simulation of gravity-driven viscous fingering instabilities in porous media flow. These instabilities play a very important role during carbon sequestration processes in brine aquifers. Our approach is based on a nonlinear implementation of the discontinuous Galerkin method, and possesses a number of key features. First, the method developed is inherently high order, and is therefore well suited to study unstable flow mechanisms. Secondly, it maintains high-order accuracy on completely unstructured meshes. The combination of these two features makes it a very appealing strategy in simulating the challenging flow patterns and very complex geometriesmore » of actual reservoirs and aquifers. This article includes an extensive set of verification studies on the stability and accuracy of the method, and also features a number of computations with unstructured grids and non-standard geometries.« less

Optical imaging of tumor microenvironment

PubMed Central

Wu, Yihan; Zhang, Wenjie; Li, Jinbo; Zhang, Yan

2013-01-01

Tumor microenvironment plays important roles in tumor development and metastasis. Features of the tumor microenvironment that are significantly different from normal tissues include acidity, hypoxia, overexpressed proteases and so on. Therefore, these features can serve as not only biomarkers for tumor diagnosis but also theraputic targets for tumor treatment. Imaging modalities such as optical, positron emission tomography (PET) and magnetic resonance imaging (MRI) have been intensively applied to investigate tumor microenvironment. Various imaging probes targeting pH, hypoxia and proteases in tumor microenvironment were thus well developed. In this review, we will focus on recent examples on fluorescent probes for optical imaging of tumor microenvironment. Construction of these fluorescent probes were based on characteristic feature of pH, hypoxia and proteases in tumor microenvironment. Strategies for development of these fluorescent probes and applications of these probes in optical imaging of tumor cells or tissues will be discussed in this review paper. PMID:23342297

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

PubMed Central

Wang, Changjian; Liu, Xiaohui; Jin, Shiyao

2018-01-01

Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment. PMID:29955227

Eyes Matched to the Prize: The State of Matched Filters in Insect Visual Circuits.

PubMed

Kohn, Jessica R; Heath, Sarah L; Behnia, Rudy

2018-01-01

Confronted with an ever-changing visual landscape, animals must be able to detect relevant stimuli and translate this information into behavioral output. A visual scene contains an abundance of information: to interpret the entirety of it would be uneconomical. To optimally perform this task, neural mechanisms exist to enhance the detection of important features of the sensory environment while simultaneously filtering out irrelevant information. This can be accomplished by using a circuit design that implements specific "matched filters" that are tuned to relevant stimuli. Following this rule, the well-characterized visual systems of insects have evolved to streamline feature extraction on both a structural and functional level. Here, we review examples of specialized visual microcircuits for vital behaviors across insect species, including feature detection, escape, and estimation of self-motion. Additionally, we discuss how these microcircuits are modulated to weigh relevant input with respect to different internal and behavioral states.

Breast Cancer Recognition Using a Novel Hybrid Intelligent Method

PubMed Central

Addeh, Jalil; Ebrahimzadeh, Ata

2012-01-01

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM-based classifier is proposed. In support vector machine training, the hyperparameters have very important roles for its recognition accuracy. Therefore, in the optimization module, the bees algorithm (BA) is proposed for selecting appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer database and simulation results show that the recommended system has a high accuracy. PMID:23626945

48,XXYY, 48,XXXY and 49,XXXXY syndromes: not just variants of Klinefelter syndrome

PubMed Central

Tartaglia, Nicole; Ayari, Natalie; Howell, Susan; D’Epagnier, Cheryl; Zeitler, Philip

2012-01-01

Sex chromosome tetrasomy and pentasomy conditions occur in 1:18 000–1:100 000 male births. While often compared with 47,XXY/Klinefelter syndrome because of shared features including tall stature and hypergonadotropic hypogonadism, 48,XXYY, 48,XXXY and 49,XXXXY syndromes are associated with additional physical findings, congenital malformations, medical problems and psychological features. While the spectrum of cognitive abilities extends much higher than originally described, developmental delays, cognitive impairments and behavioural disorders are common and require strong treatment plans. Future research should focus on genotype–phenotype relationships and the development of evidence-based treatments. Conclusion The more complex physical, medical and psychological phenotypes of 48,XXYY, 48,XXXY and 49,XXXXY syndromes make distinction from 47,XXY important; however, all of these conditions share features of hypergonadotropic hypogonadism and the need for increased awareness, biomedical research and the development of evidence-based treatments. PMID:21342258

Scanning electron microscope cathodoluminescence imaging of subgrain boundaries, twins and planar deformation features in quartz

NASA Astrophysics Data System (ADS)

Hamers, M. F.; Pennock, G. M.; Drury, M. R.

2017-04-01

The study of deformation features has been of great importance to determine deformation mechanisms in quartz. Relevant microstructures in both growth and deformation processes include dislocations, subgrains, subgrain boundaries, Brazil and Dauphiné twins and planar deformation features (PDFs). Dislocations and twin boundaries are most commonly imaged using a transmission electron microscope (TEM), because these cannot directly be observed using light microscopy, in contrast to PDFs. Here, we show that red-filtered cathodoluminescence imaging in a scanning electron microscope (SEM) is a useful method to visualise subgrain boundaries, Brazil and Dauphiné twin boundaries. Because standard petrographic thin sections can be studied in the SEM, the observed structures can be directly and easily correlated to light microscopy studies. In contrast to TEM preparation methods, SEM techniques are non-destructive to the area of interest on a petrographic thin section.

Many-body effects and excitonic features in 2D biphenylene carbon

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

Lüder, Johann, E-mail: johann.luder@physics.uu.se; Puglia, Carla; Eriksson, Olle

2016-01-14

The remarkable excitonic effects in low dimensional materials in connection to large binding energies of excitons are of great importance for research and technological applications such as in solar energy and quantum information processing as well as for fundamental investigations. In this study, the unique electronic and excitonic properties of the two dimensional carbon network biphenylene carbon were investigated with GW approach and the Bethe-Salpeter equation accounting for electron correlation effects and electron-hole interactions, respectively. Biphenylene carbon exhibits characteristic features including bright and dark excitons populating the optical gap of 0.52 eV and exciton binding energies of 530 meV asmore » well as a technologically relevant intrinsic band gap of 1.05 eV. Biphenylene carbon’s excitonic features, possibly tuned, suggest possible applications in the field of solar energy and quantum information technology in the future.« less

Report of the Regional Literacy Seminar for Representatives of Non-Governmental Organizations in Asia (Colombo, Ceylon, January 18-28, 1970).

ERIC Educational Resources Information Center

World Association of Girl Guides and Girl Scouts, London (England).

This report highlights the main features of each talk and discussion given at the seminar and pinpoints conclusions reached. The seminar made a number of recommendations, which include: (1) that Unesco place greater emphasis in all future functional literacy projects on the importance of literacy as a factor in the civic and political education of…

Modularized battery management for large lithium ion cells

NASA Astrophysics Data System (ADS)

Stuart, Thomas A.; Zhu, Wei

A modular electronic battery management system (BMS) is described along with important features for protecting and optimizing the performance of large lithium ion (LiIon) battery packs. Of particular interest is the use of a much improved cell equalization system that can increase or decrease individual cell voltages. Experimental results are included for a pack of six series connected 60 Ah (amp-hour) LiIon cells.

[Evidence-based medicine as a fundamental principle of health care management for workers].

PubMed

Amirov, N Kh; Fatkhutdinova, L M

2011-01-01

Evidence-based principles in occupational medicine should include prevention, diagnosis, treatment and rehabilitation. Specific feature of occupational medicine is necessity to prove cause-effect relationships between occupational factor and the disease emerged. Important place is occupied by cohort and intervention studies, systematic reviews and meta-analysis. Information obtained by scientific society should be presented to practical specialists and put into everyday activities.

Case Study of Airborne Pathogen Dispersion Patterns in Emergency Departments with Different Ventilation and Partition Conditions

PubMed Central

Cheong, Chang Heon; Lee, Seonhye

2018-01-01

The prevention of airborne infections in emergency departments is a very important issue. This study investigated the effects of architectural features on airborne pathogen dispersion in emergency departments by using a CFD (computational fluid dynamics) simulation tool. The study included three architectural features as the major variables: increased ventilation rate, inlet and outlet diffuser positions, and partitions between beds. The most effective method for preventing pathogen dispersion and reducing the pathogen concentration was found to be increasing the ventilation rate. Installing partitions between the beds and changing the ventilation system’s inlet and outlet diffuser positions contributed only minimally to reducing the concentration of airborne pathogens. PMID:29534043

Case Study of Airborne Pathogen Dispersion Patterns in Emergency Departments with Different Ventilation and Partition Conditions.

PubMed

Cheong, Chang Heon; Lee, Seonhye

2018-03-13

The prevention of airborne infections in emergency departments is a very important issue. This study investigated the effects of architectural features on airborne pathogen dispersion in emergency departments by using a CFD (computational fluid dynamics) simulation tool. The study included three architectural features as the major variables: increased ventilation rate, inlet and outlet diffuser positions, and partitions between beds. The most effective method for preventing pathogen dispersion and reducing the pathogen concentration was found to be increasing the ventilation rate. Installing partitions between the beds and changing the ventilation system's inlet and outlet diffuser positions contributed only minimally to reducing the concentration of airborne pathogens.

Granulomatous lobular mastitis.

PubMed

Going, J J; Anderson, T J; Wilkinson, S; Chetty, U

1987-05-01

The clinical and pathological features of nine cases of granulomatous mastitis were compared with those of 10 cases of duct ectasia/periductal mastitis (DE/PM), all of which were associated with active granulomatous inflammation. Granulomatous mastitis affects a younger age group, and although there is some overlap with DE/PM, it has distinctive pathological features, particularly a lobule centred distribution, for which the term "granulomatous lobular mastitis" is recommended. There is a strong tendency for persistence or recurrence. Our cases of granulomatous mastitis all occurred in parous women, five of them within three years of pregnancy. Awareness of this condition is important, because surgery does not offer the best treatment of recurrent disease, and trials of adequate drug treatment, including corticosteroids, are required.

Trichoscopy of Steroid-Induced Atrophy.

PubMed

Pirmez, Rodrigo; Abraham, Leonardo S; Duque-Estrada, Bruna; Damasco, Patrícia; Farias, Débora Cadore; Kelly, Yanna; Doche, Isabella

2017-10-01

Intralesional corticosteroid (IL-CS) injections have been used to treat a variety of dermatological and nondermatological diseases. Although an important therapeutic tool in dermatology, a number of local side effects, including skin atrophy, have been reported following IL-CS injections. We recently noticed that a subset of patients with steroid-induced atrophy presented with ivory-colored areas under trichoscopy. We performed a retrospective analysis of trichoscopic images and medical records from patients presenting ivory-colored areas associated with atrophic scalp lesions. In this paper, we associate this feature with the presence of steroid deposits in the dermis and report additional trichoscopic features of steroid-induced atrophy on the scalp, such as prominent blood vessels and visualization of hair bulbs.

Document localization algorithms based on feature points and straight lines

NASA Astrophysics Data System (ADS)

Skoryukina, Natalya; Shemiakina, Julia; Arlazarov, Vladimir L.; Faradjev, Igor

2018-04-01

The important part of the system of a planar rectangular object analysis is the localization: the estimation of projective transform from template image of an object to its photograph. The system also includes such subsystems as the selection and recognition of text fields, the usage of contexts etc. In this paper three localization algorithms are described. All algorithms use feature points and two of them also analyze near-horizontal and near- vertical lines on the photograph. The algorithms and their combinations are tested on a dataset of real document photographs. Also the method of localization quality estimation is proposed that allows configuring the localization subsystem independently of the other subsystems quality.

Performing skin microbiome research: A method to the madness

PubMed Central

Kong, Heidi H.; Andersson, Björn; Clavel, Thomas; Common, John E.; Jackson, Scott A.; Olson, Nathan D.; Segre, Julia A.; Traidl-Hoffmann, Claudia

2017-01-01

Growing interest in microbial contributions to human health and disease has increasingly led investigators to examine the microbiome in both healthy skin and cutaneous disorders, including acne, psoriasis and atopic dermatitis. The need for common language, effective study design, and validated methods are critical for high-quality, standardized research. Features, unique to skin, pose particular challenges when conducting microbiome research. This review discusses microbiome research standards and highlights important factors to consider, including clinical study design, skin sampling, sample processing, DNA sequencing, control inclusion, and data analysis. PMID:28063650

Diagnosis abnormalities of limb movement in disorders of the nervous system

NASA Astrophysics Data System (ADS)

Tymchik, Gregory S.; Skytsiouk, Volodymyr I.; Klotchko, Tatiana R.; Bezsmertna, Halyna; Wójcik, Waldemar; Luganskaya, Saule; Orazbekov, Zhassulan; Iskakova, Aigul

2017-08-01

The paper deals with important issues of diagnosis early signs of diseases of the nervous system, including Parkinson's disease and other specific diseases. Small quantities of violation trajectory of spatial movement of the extremities of human disease at the primary level as the most appropriate features are studied. In modern medical practice is very actual the control the emergence of diseases of the nervous system, including Parkinson's disease. In work a model limbs with six rotational kinematic pairs for diagnosis of early signs of diseases of the nervous system is considered. subject.

Generically Used Expert Scheduling System (GUESS): User's Guide Version 1.0

NASA Technical Reports Server (NTRS)

Liebowitz, Jay; Krishnamurthy, Vijaya; Rodens, Ira

1996-01-01

This user's guide contains instructions explaining how to best operate the program GUESS, a generic expert scheduling system. GUESS incorporates several important features for a generic scheduler, including automatic scheduling routines to generate a 'first' schedule for the user, a user interface that includes Gantt charts and enables the human scheduler to manipulate schedules manually, diagnostic report generators, and a variety of scheduling techniques. The current version of GUESS runs on an IBM PC or compatible in the Windows 3.1 or Windows '95 environment.

Psychosomatic aspects of end-stage renal failure.

PubMed

Sensky, T

1993-01-01

End-stage renal failure (ESRD) is more than a typical chronic disease. Its treatment includes features which arguably make this condition unique. Selected psychosomatic aspects of ESRD are reviewed, including psychiatric morbidity, patients' adherence to their treatments, quality of life and the emotional impact on staff involved in treating patients with ESRD. Rather than presenting a comprehensive review of the results of published research, particular emphasis is laid on the critical appraisal of the methodology of published studies, to examine the extent to which these have provided answers to clinically important questions.

Classification of clinically useful sentences in clinical evidence resources.

PubMed

Morid, Mohammad Amin; Fiszman, Marcelo; Raja, Kalpana; Jonnalagadda, Siddhartha R; Del Fiol, Guilherme

2016-04-01

Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care. To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians' information needs. We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient population identified by a pattern-based natural language processing (NLP) algorithm, and cue words extracted by a feature selection technique. Algorithm performance was measured in terms of precision, recall, and F-measure. The feature-rich approach yielded an F-measure of 74% versus 37% for a feature co-occurrence method (p<0.001). Excluding predication, population, semantic concept or text-based features reduced the F-measure to 62%, 66%, 58% and 69% respectively (p<0.01). The classifier applied to Medline sentences reached an F-measure of 73%, which is equivalent to the performance of the classifier on UpToDate sentences (p=0.62). The feature-rich approach significantly outperformed general baseline methods. This approach significantly outperformed classifiers based on a single type of feature. Different types of semantic features provided a unique contribution to overall classification performance. The classifier's model and features used for UpToDate generalized well to Medline abstracts. Copyright © 2016 Elsevier Inc. All rights reserved.

Mobile personal health records: an evaluation of features and functionality.

PubMed

Kharrazi, Hadi; Chisholm, Robin; VanNasdale, Dean; Thompson, Benjamin

2012-09-01

To evaluate stand-alone mobile personal health record (mPHR) applications for the three leading cellular phone platforms (iOS, BlackBerry, and Android), assessing each for content, function, security, and marketing characteristics. Nineteen stand-alone mPHR applications (8 for iOS, 5 for BlackBerry, and 6 for Android) were identified and evaluated. Main criteria used to include mPHRs were: operating standalone on a mobile platform; not requiring external connectivity; and covering a wide range of health topics. Selected mPHRs were analyzed considering product characteristics, data elements, and application features. We also reviewed additional features such as marketing tactics. Within and between the different mobile platforms attributes for the mPHR were highly variable. None of the mPHRs contained all attributes included in our evaluation. The top four mPHRs contained 13 of the 14 features omitting only the in-case-of emergency feature. Surprisingly, seven mPHRs lacked basic security measures as important as password protection. The mPHRs were relatively inexpensive: ranging from no cost to $9.99. The mPHR application cost varied in some instances based on whether it supported single or multiple users. Ten mPHRs supported multiple user profiles. Notably, eight mPHRs used scare tactics as marketing strategy. mPHR is an emerging health care technology. The majority of existing mPHR apps is limited by at least one of the attributes considered for this study; however, as the mobile market continues to expand it is likely that more comprehensive mPHRs will be developed in the near future. New advancements in mobile technology can be utilized to enhance mPHRs by long-term patient empowerment features. Marketing strategies for mPHRs should target specific subpopulations and avoid scare tactics. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Geomorphometric multi-scale analysis for the recognition of Moon surface features using multi-resolution DTMs

NASA Astrophysics Data System (ADS)

Li, Ke; Chen, Jianping; Sofia, Giulia; Tarolli, Paolo

2014-05-01

Moon surface features have great significance in understanding and reconstructing the lunar geological evolution. Linear structures like rilles and ridges are closely related to the internal forced tectonic movement. The craters widely distributed on the moon are also the key research targets for external forced geological evolution. The extremely rare availability of samples and the difficulty for field works make remote sensing the most important approach for planetary studies. New and advanced lunar probes launched by China, U.S., Japan and India provide nowadays a lot of high-quality data, especially in the form of high-resolution Digital Terrain Models (DTMs), bringing new opportunities and challenges for feature extraction on the moon. The aim of this study is to recognize and extract lunar features using geomorphometric analysis based on multi-scale parameters and multi-resolution DTMs. The considered digital datasets include CE1-LAM (Chang'E One, Laser AltiMeter) data with resolution of 500m/pix, LRO-WAC (Lunar Reconnaissance Orbiter, Wide Angle Camera) data with resolution of 100m/pix, LRO-LOLA (Lunar Reconnaissance Orbiter, Lunar Orbiter Laser Altimeter) data with resolution of 60m/pix, and LRO-NAC (Lunar Reconnaissance Orbiter, Narrow Angle Camera) data with resolution of 2-5m/pix. We considered surface derivatives to recognize the linear structures including Rilles and Ridges. Different window scales and thresholds for are considered for feature extraction. We also calculated the roughness index to identify the erosion/deposits area within craters. The results underline the suitability of the adopted methods for feature recognition on the moon surface. The roughness index is found to be a useful tool to distinguish new craters, with higher roughness, from the old craters, which present a smooth and less rough surface.

Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images.

PubMed

Al-Khafaji, Suhad Lateef; Jun Zhou; Zia, Ali; Liew, Alan Wee-Chung

2018-02-01

Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials. This method, which is named spectral-spatial scale invariant feature transform (SS-SIFT), explores both spectral and spatial dimensions simultaneously to extract spectral and geometric transformation invariant features. Similar to the classic SIFT algorithm, SS-SIFT consists of keypoint detection and descriptor construction steps. Keypoints are extracted from spectral-spatial scale space and are detected from extrema after 3D difference of Gaussian is applied to the data cube. Two descriptors are proposed for each keypoint by exploring the distribution of spectral-spatial gradient magnitude in its local 3D neighborhood. The effectiveness of the SS-SIFT approach is validated on images collected in different light conditions, different geometric projections, and using two hyperspectral cameras with different spectral wavelength ranges and resolutions. The experimental results show that our method generates robust invariant features for spectral-spatial image matching.

Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

PubMed Central

Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi

2014-01-01

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

Perceived Importance of Wellness Features at a Cancer Center: Patient and Staff Perspectives.

PubMed

Tinner, Michelle; Crovella, Paul; Rosenbaum, Paula F

2018-01-01

Determine the relative impact of 11 building wellness features on preference and on the ability to deliver/receive quality care for two groups: patients and caregivers. The impact of building features that promote wellness is of increasing interest to the building owners, designers, and occupants. This study performed a postoccupancy evaluation of two user groups at a healthcare facility with specific wellness features. Seventy-six staff and 62 patients of a cancer center were polled separately to determine their preferences in 11 categories. Results showed that all wellness features were viewed favorably by the two groups, with natural lighting, views of nature, and thermal comfort as top categories for both. The t-test comparisons were performed, and significant differences ( p < .05) between the two groups were found for three of the features (views of nature, art and murals, and indoor plants). Discussion of these differences and the interaction of competing design goals (thermal comfort, views of nature, natural light, and desire for privacy) are included. Designers and owners will want to consider the preferred use of roof gardens, art and murals, and indoor plants for patient spaces, where their relative value is greater. Access to private and quiet spaces is the top need for caregivers. Ease of movement, thermal comfort, and natural light were top needs for patients.

Titan solar occultation observations reveal transit spectra of a hazy world.

PubMed

Robinson, Tyler D; Maltagliati, Luca; Marley, Mark S; Fortney, Jonathan J

2014-06-24

High-altitude clouds and hazes are integral to understanding exoplanet observations, and are proposed to explain observed featureless transit spectra. However, it is difficult to make inferences from these data because of the need to disentangle effects of gas absorption from haze extinction. Here, we turn to the quintessential hazy world, Titan, to clarify how high-altitude hazes influence transit spectra. We use solar occultation observations of Titan's atmosphere from the Visual and Infrared Mapping Spectrometer aboard National Aeronautics and Space Administration's (NASA) Cassini spacecraft to generate transit spectra. Data span 0.88-5 μm at a resolution of 12-18 nm, with uncertainties typically smaller than 1%. Our approach exploits symmetry between occultations and transits, producing transit radius spectra that inherently include the effects of haze multiple scattering, refraction, and gas absorption. We use a simple model of haze extinction to explore how Titan's haze affects its transit spectrum. Our spectra show strong methane-absorption features, and weaker features due to other gases. Most importantly, the data demonstrate that high-altitude hazes can severely limit the atmospheric depths probed by transit spectra, bounding observations to pressures smaller than 0.1-10 mbar, depending on wavelength. Unlike the usual assumption made when modeling and interpreting transit observations of potentially hazy worlds, the slope set by haze in our spectra is not flat, and creates a variation in transit height whose magnitude is comparable to those from the strongest gaseous-absorption features. These findings have important consequences for interpreting future exoplanet observations, including those from NASA's James Webb Space Telescope.

Wood anatomy of tribe Detarieae and comparison with tribe Caesalpinieae (Leguminosae, Caesalpinioideae) in Venezuela.

PubMed

Melandri, José Luis; de Pernía, Narcisana Espinoza

2009-01-01

We studied the wood anatomy of 29 species belonging to 10 genera of the tribe Detarieae, subfamily Caesalpinioideae and compare them with tribe Caesalpinieae. Detarieae is the largest of four tribes of Caesalpinioideae, with 84 genera, only eleven occur in Venezuela with species of timber importance. The specimens were collected in Venezuela and include wood samples from the collection of the Laboratorio de Anatomía de Maderas de la Facultad de Ciencias Forestales y Ambientales de la Universidad de Los Andes, Venezuela, and of the Forest Products Laboratory of the USDA Forest Service in Madison, Wisconsin, USA. The terminology and methodology used followed the IAWA List of Microscopic Features for Hardwood Identification of the IAWA Committee, 1989. Measurements from each specimen were averaged (vessel diameters, vessel element lengths, intervessels pit size, fibre lengths and ray height). The species of Detarieae can be separated using a combination of diagnostic features. Wood characters that provide the most important diagnosis and may be used in systematics of Detarieae include: intercellular axial canals, rays heterocellular, rays exclusively or predominantly uniseriate, prismatic crystals common in ray cells, irregular storied structure and fibre wall thickness. For comparative anatomy between Detarieae and Caesalpinieae: intercellular axial canals, heterocellular rays, rays exclusively or predominantly uniseriate, prismatic crystals common in ray cells (in Detarieae) and regular storied structure, fibres septate, fibre wall thick or very thick, rays homocellular, multiseriate rays and silica bodies (in Caesalpinieae). Axial parenchyma is typically a good diagnostic feature for Leguminosae, but not for Detarieae and Caesalpinieae comparisons.

A Comparison of the Near-Infrared Spectral Features of Early-Type Galaxies in the Virgo and Coma Clusters

NASA Astrophysics Data System (ADS)

Houdashelt, M. L.

1992-05-01

Initial results are presented from an examination of near-infrared spectra (6800 - 9200 Angstroms) of 34 early-type galaxies - 17 in the Virgo cluster, 10 in the Coma cluster and seven field members. It has previously been speculated that E/S0 galaxies of similar luminosity in the Virgo and Coma clusters have different red stellar populations. To explore this possibility, pseudo-equivalent widths of a number of near-IR spectral features have been measured. The important features studied include the TiO bands near 7100, 7890, 8197, 8500 and 8950 Angstroms, which are mainly produced by the late-type stars whose flux contributes only about 10-20\\ the near-IR. The strengths of the Ca triplet (8498, 8542, 8662 Angstroms) and Na I doublet (8183, 8195 Angstroms) are also measured, since these features are affected by the relative contribution of dwarf stars to the red light. Although the main focus of this work is the search for spectral differences among the Coma, Virgo and field E/S0 populations, each subgroup of galaxies (and the sample as a whole) are also examined for correlations among the feature strengths, galaxy color and luminosity.

Applying different independent component analysis algorithms and support vector regression for IT chain store sales forecasting.

PubMed

Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

PubMed Central

Dai, Wensheng

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740

Identification of chemogenomic features from drug–target interaction networks using interpretable classifiers

PubMed Central

Tabei, Yasuo; Pauwels, Edouard; Stoven, Véronique; Takemoto, Kazuhiro; Yamanishi, Yoshihiro

2012-01-01

Motivation: Drug effects are mainly caused by the interactions between drug molecules and their target proteins including primary targets and off-targets. Identification of the molecular mechanisms behind overall drug–target interactions is crucial in the drug design process. Results: We develop a classifier-based approach to identify chemogenomic features (the underlying associations between drug chemical substructures and protein domains) that are involved in drug–target interaction networks. We propose a novel algorithm for extracting informative chemogenomic features by using L1 regularized classifiers over the tensor product space of possible drug–target pairs. It is shown that the proposed method can extract a very limited number of chemogenomic features without loosing the performance of predicting drug–target interactions and the extracted features are biologically meaningful. The extracted substructure–domain association network enables us to suggest ligand chemical fragments specific for each protein domain and ligand core substructures important for a wide range of protein families. Availability: Softwares are available at the supplemental website. Contact: yamanishi@bioreg.kyushu-u.ac.jp Supplementary Information: Datasets and all results are available at http://cbio.ensmp.fr/~yyamanishi/l1binary/ . PMID:22962471

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer’s disease progression

PubMed Central

Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, Jose

2014-01-01

Abstract. Early diagnoses of Alzheimer’s disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different (p-value=2.04e−11). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones. PMID:26158047

YamiPred: A Novel Evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features.

PubMed

Kleftogiannis, Dimitrios; Theofilatos, Konstantinos; Likothanassis, Spiros; Mavroudi, Seferina

2015-01-01

MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of support vector machines (SVM) with genetic algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and the prevalent SVM-based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature selection component selects a compact feature subset that contributes to the performance optimization. Further experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum number of samples required for developing a robust predictor. YamiPred also confirmed the important role of commonly used features such as entropy and enthalpy, and uncovered the significance of newly introduced features, such as %A-U aggregate nucleotide frequency and positional entropy. The best model trained on human data has successfully predicted pre-miRNAs to other organisms including the category of viruses.

Emotion Recognition from Chinese Speech for Smart Affective Services Using a Combination of SVM and DBN

PubMed Central

Zhu, Lianzhang; Chen, Leiming; Zhao, Dehai

2017-01-01

Accurate emotion recognition from speech is important for applications like smart health care, smart entertainment, and other smart services. High accuracy emotion recognition from Chinese speech is challenging due to the complexities of the Chinese language. In this paper, we explore how to improve the accuracy of speech emotion recognition, including speech signal feature extraction and emotion classification methods. Five types of features are extracted from a speech sample: mel frequency cepstrum coefficient (MFCC), pitch, formant, short-term zero-crossing rate and short-term energy. By comparing statistical features with deep features extracted by a Deep Belief Network (DBN), we attempt to find the best features to identify the emotion status for speech. We propose a novel classification method that combines DBN and SVM (support vector machine) instead of using only one of them. In addition, a conjugate gradient method is applied to train DBN in order to speed up the training process. Gender-dependent experiments are conducted using an emotional speech database created by the Chinese Academy of Sciences. The results show that DBN features can reflect emotion status better than artificial features, and our new classification approach achieves an accuracy of 95.8%, which is higher than using either DBN or SVM separately. Results also show that DBN can work very well for small training databases if it is properly designed. PMID:28737705

MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression

NASA Astrophysics Data System (ADS)

Martínez-Torteya, Antonio; Rodríguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, José G.

2014-03-01

An early diagnosis of Alzheimer's disease (AD) confers many benefits. Several biomarkers from different information modalities have been proposed for the prediction of MCI to AD progression, where features extracted from MRI have played an important role. However, studies have focused almost exclusively in the morphological characteristics of the images. This study aims to determine whether features relating to the signal and texture of the image could add predictive power. Baseline clinical, biological and PET information, and MP-RAGE images for 62 subjects from the Alzheimer's Disease Neuroimaging Initiative were used in this study. Images were divided into 83 regions and 50 features were extracted from each one of these. A multimodal database was constructed, and a feature selection algorithm was used to obtain an accurate and small logistic regression model, which achieved a cross-validation accuracy of 0.96. These model included six features, five of them obtained from the MP-RAGE image, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index, showing that both groups are statistically different (p-value of 2.04e-11). The results demonstrate that MRI features related to both signal and texture, add MCI to AD predictive power, and support the idea that multimodal biomarkers outperform single-modality biomarkers.

Clinical Correlations With Lewy Body Pathology in LRRK2-Related Parkinson Disease

PubMed Central

Kalia, Lorraine V.; Lang, Anthony E.; Hazrati, Lili-Naz; Fujioka, Shinsuke; Wszolek, Zbigniew K.; Dickson, Dennis W.; Ross, Owen A.; Van Deerlin, Vivianna M.; Trojanowski, John Q.; Hurtig, Howard I.; Alcalay, Roy N.; Marder, Karen S.; Clark, Lorraine N.; Gaig, Carles; Tolosa, Eduardo; Ruiz-Martínez, Javier; Marti-Masso, Jose F.; Ferrer, Isidre; de Munain, Adolfo López; Goldman, Samuel M.; Schüle, Birgitt; Langston, J. William; Aasly, Jan O.; Giordana, Maria T.; Bonifati, Vincenzo; Puschmann, Andreas; Canesi, Margherita; Pezzoli, Gianni; De Paula, Andre Maues; Hasegawa, Kazuko; Duyckaerts, Charles; Brice, Alexis; Stoessl, A. Jon; Marras, Connie

2015-01-01

IMPORTANCE Mutations in leucine-rich repeat kinase 2 (LRRK2) are the most common cause of genetic Parkinson disease (PD) known to date. The clinical features of manifesting LRRK2 mutation carriers are generally indistinguishable from those of patients with sporadic PD. However, some PD cases associated with LRRK2 mutations lack Lewy bodies (LBs), a neuropathological hallmark of PD. We investigated whether the presence or absence of LBs correlates with different clinical features in LRRK2-related PD. OBSERVATIONS We describe genetic, clinical, and neuropathological findings of 37 cases of LRRK2-related PD including 33 published and 4 unpublished cases through October 2013. Among the different mutations, the LRRK2 p.G2019S mutation was most frequently associated with LB pathology. Nonmotor features of cognitive impairment/dementia, anxiety, and orthostatic hypotension were correlated with the presence of LBs. In contrast, a primarily motor phenotype was associated with a lack of LBs. CONCLUSIONS AND RELEVANCE To our knowledge, this is the first report of clinicopathological correlations in a series of LRRK2-related PD cases. Findings from this selected group of patients with PD demonstrated that parkinsonian motor features can occur in the absence of LBs. However, LB pathology in LRRK2-related PD may be a marker for a broader parkinsonian symptom complex including cognitive impairment. PMID:25401511

Prescribing patterns of psychotropic medications and clinical features in patients with major depressive disorder with and without comorbid dysthymia in China.

PubMed

Feng, Yuan; Sha, Sha; Hu, Chen; Wang, Gang; Ungvari, Gabor S; Chiu, Helen F K; Ng, Chee H; Si, Tian-Mei; Chen, Da-Fang; Fang, Yi-Ru; Lu, Zheng; Yang, Hai-Chen; Hu, Jian; Chen, Zhi-Yu; Huang, Yi; Sun, Jing; Wang, Xiao-Ping; Li, Hui-Chun; Zhang, Jin-Bei; Xiang, Yu-Tao

2017-03-01

Little has been reported about the demographic and clinical features of major depressive disorder (MDD) with comorbid dysthymia in Chinese patients. This study examined the frequency of comorbid dysthymia in Chinese MDD patients together with the demographic and clinical correlates and prescribing patterns of psychotropic drugs. Consecutively collected sample of 1178 patients with MDD were examined in 13 major psychiatric hospitals in China. Patients' demographic and clinical characteristics and psychotropic drugs prescriptions were recorded using a standardized protocol and data collection procedure. The diagnosis of dysthymia was established using the Mini International Neuropsychiatric Interview. Medications ascertained included antidepressants, antipsychotics, benzodiazepines, and mood stabilizers. One hundred and three (8.7%) patients fulfilled criteria for dysthymia. In multiple logistic regression analyses, compared to non-dysthymia counterparts, MDD patients with dysthymia had more depressive episodes with atypical features including increased appetite, sleep, and weight gain, more frequent lifetime depressive episodes, and less likelihood of family history of psychiatric disorders. There was no significant difference in the pattern of psychotropic prescription between the 2 groups. There are important differences in the demographic and clinical features of comorbid dysthymia in Chinese MDD patients compared with previous reports. The clinical profile found in this study has implications for treatment decisions. © 2016 John Wiley & Sons Australia, Ltd.

Open-Source GIS

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

Vatsavai, Raju; Burk, Thomas E; Lime, Steve

2012-01-01

The components making up an Open Source GIS are explained in this chapter. A map server (Sect. 30.1) can broadly be defined as a software platform for dynamically generating spatially referenced digital map products. The University of Minnesota MapServer (UMN Map Server) is one such system. Its basic features are visualization, overlay, and query. Section 30.2 names and explains many of the geospatial open source libraries, such as GDAL and OGR. The other libraries are FDO, JTS, GEOS, JCS, MetaCRS, and GPSBabel. The application examples include derived GIS-software and data format conversions. Quantum GIS, its origin and its applications explainedmore » in detail in Sect. 30.3. The features include a rich GUI, attribute tables, vector symbols, labeling, editing functions, projections, georeferencing, GPS support, analysis, and Web Map Server functionality. Future developments will address mobile applications, 3-D, and multithreading. The origins of PostgreSQL are outlined and PostGIS discussed in detail in Sect. 30.4. It extends PostgreSQL by implementing the Simple Feature standard. Section 30.5 details the most important open source licenses such as the GPL, the LGPL, the MIT License, and the BSD License, as well as the role of the Creative Commons.« less

A systematic literature review on security and privacy of electronic health record systems: technical perspectives.

PubMed

Rezaeibagha, Fatemeh; Win, Khin Than; Susilo, Willy

Even though many safeguards and policies for electronic health record (EHR) security have been implemented, barriers to the privacy and security protection of EHR systems persist. This article presents the results of a systematic literature review regarding frequently adopted security and privacy technical features of EHR systems. Our inclusion criteria were full articles that dealt with the security and privacy of technical implementations of EHR systems published in English in peer-reviewed journals and conference proceedings between 1998 and 2013; 55 selected studies were reviewed in detail. We analysed the review results using two International Organization for Standardization (ISO) standards (29100 and 27002) in order to consolidate the study findings. Using this process, we identified 13 features that are essential to security and privacy in EHRs. These included system and application access control, compliance with security requirements, interoperability, integration and sharing, consent and choice mechanism, policies and regulation, applicability and scalability and cryptography techniques. This review highlights the importance of technical features, including mandated access control policies and consent mechanisms, to provide patients' consent, scalability through proper architecture and frameworks, and interoperability of health information systems, to EHR security and privacy requirements.

[A complexity analysis of Chinese herbal property theory: the multiple formations of herbal property].

PubMed

Jin, Rui; Zhang, Bing

2012-11-01

Chinese herbal property theory (CHPT) is the fundamental characteristic of Chinese materia medica different from modern medicines. It reflects the herbal properties associated with efficacy and formed the early framework of four properties and five flavors in Shennong's Classic of Materia Medica. After the supplement and improvement of CHPT in the past thousands of years, it has developed a theory system including four properties, five flavors, meridian entry, direction of medicinal actions (ascending, descending, floating and sinking) and toxicity. However, because of the influence of philosophy about yin-yang theory and five-phase theory and the difference of cognitive approach and historical background at different times, CHPT became complex. One of the complexity features was the multiple methods for determining herbal property, which might include the inference from herbal efficacy, the thought of Chinese Taoist School and witchcraft, the classification thinking according to manifestations, etc. Another complexity feature was the multiselection associations between herbal property and efficacy, which indicated that the same property could be inferred from different kinds of efficacy. This paper analyzed these complexity features and provided the importance of cognitive approaches and efficacy attributes corresponding to certain herbal property in the study of CHPT.

Application of anatomy and HPTLC in characterizing species of Dioscorea (Dioscoreaceae)

PubMed Central

Galal, Ahmed M.; Avula, Bharathi; Sagi, Satyanarayanaraju; Smillie, Troy J.

2017-01-01

The edible tubers from different species of Dioscorea are a major source of food and nutrition for millions of people. Some of the species are medicinally important but others are toxic. The genus consists of about 630 species of almost wholly dioecious plants, many of them poorly characterized. The taxonomy of Dioscorea is confusing and identification of the species is generally problematic. There are no adequate anatomical studies available for most of the species. This study is aimed to fill this gap and provides a detailed investigation of the anatomy and micromorphology of the rhizomes and tubers of five different species of Dioscorea, namely D. balcanica, D. bulbifera, D. polystachya, D. rotundata and D. villosa. The primary features that can help in distinguishing the species include the nature of periderm, presence or absence of pericyclic sclereids, lignification in the phloem, types of calcium oxalate crystals and features of starch grains. The descriptions are supported with images of bright-field and scanning electron microscopy for better understanding of these species. The diagnostic key of anatomical features included in this paper can help distinguish the investigated species unambiguously. Additionally, HPTLC analyses of authentic and commercial samples of the five species are described. PMID:24928704

Interaction matters: Strategies to promote engaged learning in an online introductory nutrition course.

PubMed

Banna, Jinan; Grace Lin, Meng-Fen; Stewart, Maria; Fialkowski, Marie K

2015-06-01

Fostering interaction in the online classroom is an important consideration in ensuring that students actively create their own knowledge and reach a high level of achievement in science courses. This study focuses on fostering interaction in an online introductory nutrition course offered in a public institution of higher education in Hawai'i, USA. Interactive features included synchronous discussions and polls in scheduled sessions, and social media tools for sharing of information and resources. Qualitative student feedback was solicited regarding the new course features. Findings indicated that students who attended monthly synchronous sessions valued live interaction with peers and the instructor. Issues identified included technical difficulties during synchronous sessions, lack of participation on the part of fellow students in discussion and inability to attend synchronous sessions due to scheduling conflicts. In addition, few students made use of the opportunity to interact via social media. While students indicated that the interactive components of the course were valuable, several areas in which improvement may be made remain. Future studies may explore potential solutions to issues identified with new features to further promote interaction and foster learning in the course. Recommendations for instructors who are interested in offering online science courses in higher education are provided.

A bootstrap based Neyman-Pearson test for identifying variable importance.

PubMed

Ditzler, Gregory; Polikar, Robi; Rosen, Gail

2015-04-01

Selection of most informative features that leads to a small loss on future data are arguably one of the most important steps in classification, data analysis and model selection. Several feature selection (FS) algorithms are available; however, due to noise present in any data set, FS algorithms are typically accompanied by an appropriate cross-validation scheme. In this brief, we propose a statistical hypothesis test derived from the Neyman-Pearson lemma for determining if a feature is statistically relevant. The proposed approach can be applied as a wrapper to any FS algorithm, regardless of the FS criteria used by that algorithm, to determine whether a feature belongs in the relevant set. Perhaps more importantly, this procedure efficiently determines the number of relevant features given an initial starting point. We provide freely available software implementations of the proposed methodology.

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling

PubMed Central

Zhou, Fuqun; Zhang, Aining

2016-01-01

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2–3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests’ features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data. PMID:27792152

Motivation to study dental professions in one London Dental Institute.

PubMed

Belsi, A; Asimakopoulou, K; Donaldson, N; Gallagher, J

2014-02-01

While past research has explored dental students' motivation to study, there is limited understanding in the reasons behind career choice for hygienists/therapists and dental nurses. The aim of this study was to investigate simultaneously the views of students of dentistry, hygiene/therapy and dental nursing in King's College London and explore similarities or differences in career choice. All first-year students were invited to the questionnaire survey, exploring motivation to study using a 23-item instrument. Data were analysed using SPSS v18; statistical analysis included one-way analyses of variance and factor analysis. The overall response rate to the study was 75% (n = 209). Ten out of 23 factors were considered important by more than 80% of respondents, with 'job security' (93.8%), 'desire to work with people' (88%) and 'degree leading to recognised job' (87.5%) being top three. Analysis suggested that 52% of the total variation in motivating influences was explained by four factors: 'features of the job' (26%), 'education/skills' (11%), 'public service' (8%) and 'careers-advising' (7%); at group level 'features of the job' were significantly more important for the direct entrants to dentistry (P = 0.001). The findings suggest that across groups students were motivated to study by common influences reflecting altruistic, but also pragmatic and realistic motives, while 'features of the job' were more important for the direct entrants to dentistry. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 2-Uncommon Sarcomas.

PubMed

Levy, Angela D; Manning, Maria A; Miettinen, Markku M

2017-01-01

Soft-tissue sarcomas occurring in the abdomen and pelvis are an uncommon but important group of malignancies. Recent changes to the World Health Organization classification of soft-tissue tumors include the movement of gastrointestinal stromal tumors (GISTs) into the soft-tissue tumor classification. GIST is the most common intraperitoneal sarcoma. Liposarcoma is the most common retroperitoneal sarcoma, and leiomyosarcoma is the second most common. GIST, liposarcoma, and leiomyosarcoma account for the majority of sarcomas encountered in the abdomen and pelvis and are discussed in part 1 of this article. Undifferentiated pleomorphic sarcoma (previously called malignant fibrous histiocytoma), dermatofibrosarcoma protuberans, solitary fibrous tumor, malignant peripheral nerve sheath tumor, rhabdomyosarcoma, extraskeletal chondro-osseous sarcomas, vascular sarcomas, and sarcomas of uncertain differentiation uncommonly arise in the abdomen and pelvis and the abdominal wall. Although these lesions are rare sarcomas and their imaging features overlap, familiarity with the locations where they occur and their imaging features is important so they can be diagnosed accurately. The anatomic location and clinical history are important factors in the differential diagnosis of these lesions because metastasis, more-common sarcomas, borderline fibroblastic proliferations (such as desmoid tumors), and endometriosis have imaging findings that overlap with those of these uncommon sarcomas. In this article, the clinical, pathologic, and imaging findings of uncommon soft-tissue sarcomas of the abdomen and pelvis and the abdominal wall are reviewed, with an emphasis on their differential diagnosis.

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 2—Uncommon Sarcomas

PubMed Central

Manning, Maria A.; Miettinen, Markku M.

2017-01-01

Soft-tissue sarcomas occurring in the abdomen and pelvis are an uncommon but important group of malignancies. Recent changes to the World Health Organization classification of soft-tissue tumors include the movement of gastrointestinal stromal tumors (GISTs) into the soft-tissue tumor classification. GIST is the most common intraperitoneal sarcoma. Liposarcoma is the most common retroperitoneal sarcoma, and leiomyosarcoma is the second most common. GIST, liposarcoma, and leiomyosarcoma account for the majority of sarcomas encountered in the abdomen and pelvis and are discussed in part 1 of this article. Undifferentiated pleomorphic sarcoma (previously called malignant fibrous histiocytoma), dermatofibrosarcoma protuberans, solitary fibrous tumor, malignant peripheral nerve sheath tumor, rhabdomyosarcoma, extraskeletal chondro-osseous sarcomas, vascular sarcomas, and sarcomas of uncertain differentiation uncommonly arise in the abdomen and pelvis and the abdominal wall. Although these lesions are rare sarcomas and their imaging features overlap, familiarity with the locations where they occur and their imaging features is important so they can be diagnosed accurately. The anatomic location and clinical history are important factors in the differential diagnosis of these lesions because metastasis, more-common sarcomas, borderline fibroblastic proliferations (such as desmoid tumors), and endometriosis have imaging findings that overlap with those of these uncommon sarcomas. In this article, the clinical, pathologic, and imaging findings of uncommon soft-tissue sarcomas of the abdomen and pelvis and the abdominal wall are reviewed, with an emphasis on their differential diagnosis. PMID:28493803

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

PubMed

Zhou, Fuqun; Zhang, Aining

2016-10-25

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

ClearTK 2.0: Design Patterns for Machine Learning in UIMA

PubMed Central

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-01-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework. PMID:29104966

ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

PubMed

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-05-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.

Land-use planning for nearshore ecosystem services—the Puget Sound Ecosystem Portfolio Model

USGS Publications Warehouse

Byrd, Kristin

2011-01-01

The 2,500 miles of shoreline and nearshore areas of Puget Sound, Washington, provide multiple benefits to people—"ecosystem services"—including important fishing, shellfishing, and recreation industries. To help resource managers plan for expected growth in coming decades, the U.S. Geological Survey Western Geographic Science Center has developed the Puget Sound Ecosystem Portfolio Model (PSEPM). Scenarios of urban growth and shoreline modifications serve as model inputs to develop alternative futures of important nearshore features such as water quality and beach habitats. Model results will support regional long-term planning decisions for the Puget Sound region.

WUVS simulator: detectability of spectral lines with the WSO-UV spectrographs

NASA Astrophysics Data System (ADS)

Marcos-Arenal, Pablo; de Castro, Ana I. Gómez; Abarca, Belén Perea; Sachkov, Mikhail

2017-04-01

The World Space Observatory Ultraviolet telescope is equipped with high dispersion (55,000) spectrographs working in the 1150 to 3100 Å spectral range. To evaluate the impact of the design on the scientific objectives of the mission, a simulation software tool has been developed. This simulator builds on the development made for the PLATO space mission and it is designed to generate synthetic time-series of images by including models of all important noise sources. We describe its design and performance. Moreover, its application to the detectability of important spectral features for star formation and exoplanetary research is addressed.

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

Theoretical features of MHD equilibria with flow

NASA Astrophysics Data System (ADS)

Beklemishev, Alexei; Tessarotto, Massimo

2002-11-01

The effect produced on plasma dynamics by plasma flows, especially those produced by strong E× B-drifts represent an important theoretical issue in magnetic confinement. These include in particular Stellarator equilibria in the presence of weak flows, with velocity much smaller in magnitude than the ion thermal velocity [1]. Strong flows, however, more generally can be produced locally in a variety of physical situations (for example due to strong radial electric fields, neutral beams, RF heating, etc.). These flows can be important in establishing advanced operational regimes, such as the recently discovered HDH mode in the W7-AS Stellarator [2]. Goal of this work is to investigate theoretical features of the MHD equilibria in the presence of strong flows, with particular reference to conditions of existence of kinetic equilibria, particle adiabatic and/or bounce-averaged invariants. References 1 - M. Tessarotto, J.L. Johnson, R.B. White and L.J. Zheng, Phys. Plasmas 3, 2653 (1996); 2 - K. McCormick et al., Phys. Rev. Lett. 89, 15001 (2002).

Climate and land-use change in wetlands: A dedication

USGS Publications Warehouse

Middleton, Beth A.

2017-01-01

Future climate and land-use change may wreak havoc on wetlands, with the potential to erode their values as harbors for biota and providers of human services. Wetlands are important to protect, particularly because these provide a variety of ecosystem services including wildlife habitat, water purification, flood storage, and storm protection (Mitsch, Bernal, and Hernandez 2015). Without healthy wetlands, future generations may become increasingly less in harmony with the sustainability of the Earth. To this end, the thematic feature on climate and land-use change in wetlands explores the critical role of wetlands in the overall health and well-being of humans and our planet. Our special feature contributes to the understanding of the idea that the health of natural ecosystems and humans are linked and potentially stressed by climate change and land-use change (Horton and Lo 2015; McDonald 2015). In particular, this special issue considers the important role of wetlands in the environment, and how land-use and environmental change might affect them in the future.

Experimenting liver fibrosis diagnostic by two photon excitation microscopy and Bag-of-Features image classification.

PubMed

Stanciu, Stefan G; Xu, Shuoyu; Peng, Qiwen; Yan, Jie; Stanciu, George A; Welsch, Roy E; So, Peter T C; Csucs, Gabor; Yu, Hanry

2014-04-10

The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) can image the endogenous signals of tissue structures and can be used for fibrosis assessment on non-stained tissue samples. While image analysis of collagen in SHG images was consistently addressed until now, cellular and tissue information included in TPEF images, such as inflammatory and hepatic cell damage, equally important as collagen deposition imaged by SHG, remain poorly exploited to date. We address this situation by experimenting liver fibrosis quantification and scoring using a combined approach based on TPEF liver surface imaging on a Thioacetamide-induced rat model and a gradient based Bag-of-Features (BoF) image classification strategy. We report the assessed performance results and discuss the influence of specific BoF parameters to the performance of the fibrosis scoring framework.

Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification

PubMed Central

Stanciu, Stefan G.; Xu, Shuoyu; Peng, Qiwen; Yan, Jie; Stanciu, George A.; Welsch, Roy E.; So, Peter T. C.; Csucs, Gabor; Yu, Hanry

2014-01-01

The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) can image the endogenous signals of tissue structures and can be used for fibrosis assessment on non-stained tissue samples. While image analysis of collagen in SHG images was consistently addressed until now, cellular and tissue information included in TPEF images, such as inflammatory and hepatic cell damage, equally important as collagen deposition imaged by SHG, remain poorly exploited to date. We address this situation by experimenting liver fibrosis quantification and scoring using a combined approach based on TPEF liver surface imaging on a Thioacetamide-induced rat model and a gradient based Bag-of-Features (BoF) image classification strategy. We report the assessed performance results and discuss the influence of specific BoF parameters to the performance of the fibrosis scoring framework. PMID:24717650

Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification

NASA Astrophysics Data System (ADS)

Stanciu, Stefan G.; Xu, Shuoyu; Peng, Qiwen; Yan, Jie; Stanciu, George A.; Welsch, Roy E.; So, Peter T. C.; Csucs, Gabor; Yu, Hanry

2014-04-01

The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) can image the endogenous signals of tissue structures and can be used for fibrosis assessment on non-stained tissue samples. While image analysis of collagen in SHG images was consistently addressed until now, cellular and tissue information included in TPEF images, such as inflammatory and hepatic cell damage, equally important as collagen deposition imaged by SHG, remain poorly exploited to date. We address this situation by experimenting liver fibrosis quantification and scoring using a combined approach based on TPEF liver surface imaging on a Thioacetamide-induced rat model and a gradient based Bag-of-Features (BoF) image classification strategy. We report the assessed performance results and discuss the influence of specific BoF parameters to the performance of the fibrosis scoring framework.

Towards enhanced and interpretable clustering/classification in integrative genomics

PubMed Central

Lu, Yang Young; Lv, Jinchi; Fuhrman, Jed A.

2017-01-01

Abstract High-throughput technologies have led to large collections of different types of biological data that provide unprecedented opportunities to unravel molecular heterogeneity of biological processes. Nevertheless, how to jointly explore data from multiple sources into a holistic, biologically meaningful interpretation remains challenging. In this work, we propose a scalable and tuning-free preprocessing framework, Heterogeneity Rescaling Pursuit (Hetero-RP), which weighs important features more highly than less important ones in accord with implicitly existing auxiliary knowledge. Finally, we demonstrate effectiveness of Hetero-RP in diverse clustering and classification applications. More importantly, Hetero-RP offers an interpretation of feature importance, shedding light on the driving forces of the underlying biology. In metagenomic contig binning, Hetero-RP automatically weighs abundance and composition profiles according to the varying number of samples, resulting in markedly improved performance of contig binning. In RNA-binding protein (RBP) binding site prediction, Hetero-RP not only improves the prediction performance measured by the area under the receiver operating characteristic curves (AUC), but also uncovers the evidence supported by independent studies, including the distribution of the binding sites of IGF2BP and PUM2, the binding competition between hnRNPC and U2AF2, and the intron–exon boundary of U2AF2 [availability: https://github.com/younglululu/Hetero-RP]. PMID:28977511

The regional cerebral blood flow changes in major depressive disorder with and without psychotic features.

PubMed

Gonul, Ali Saffet; Kula, Mustafa; Bilgin, Arzu Guler; Tutus, Ahmet; Oguz, Aslan

2004-09-01

Depressive patients with psychotic features demonstrate distinct biological abnormalities in the hypothalamic-pituitary-adrenal axis (HPA), dopaminergic activity, electroencephalogram sleep profiles and measures of serotonergic function when compared to nonpsychotic depressive patients. However, very few functional neuroimaging studies were specifically designed for studying the effects of psychotic features on neuroimaging findings in depressed patients. The objective of the present study was to compare brain Single Photon Emission Tomography (SPECT) images in a group of unmedicated depressive patients with and without psychotic features. Twenty-eight patients who fully met DSM-IV criteria for major depressive disorder (MDD, 12 had psychotic features) were included in the study. They were compared with 16 control subjects matched for age, gender and education. Both psychotic and nonpsychotic depressed patients showed significantly lower regional cerebral blood flow (rCBF) values in the left and right superior frontal cortex, and left anterior cingulate cortex compared to those of controls. In comparison with depressive patients without psychotic features (DwoPF), depressive patients with psychotic features (DwPF) showed significantly lower rCBF perfusion ratios in left parietal cortex, left cerebellum but had higher rCBF perfusion ratio in the left inferior frontal cortex and caudate nucleus. The present study showed that DwPF have a different rCBF pattern compared to patients without psychotic features. Abnormalities involving inferior frontal cortex, striatum and cerebellum may play an important role in the generation of psychotic symptoms in depression.

A method of plane geometry primitive presentation

NASA Astrophysics Data System (ADS)

Jiao, Anbo; Luo, Haibo; Chang, Zheng; Hui, Bin

2014-11-01

Point feature and line feature are basic elements in object feature sets, and they play an important role in object matching and recognition. On one hand, point feature is sensitive to noise; on the other hand, there are usually a huge number of point features in an image, which makes it complex for matching. Line feature includes straight line segment and curve. One difficulty in straight line segment matching is the uncertainty of endpoint location, the other is straight line segment fracture problem or short straight line segments joined to form long straight line segment. While for the curve, in addition to the above problems, there is another difficulty in how to quantitatively describe the shape difference between curves. Due to the problems of point feature and line feature, the robustness and accuracy of target description will be affected; in this case, a method of plane geometry primitive presentation is proposed to describe the significant structure of an object. Firstly, two types of primitives are constructed, they are intersecting line primitive and blob primitive. Secondly, a line segment detector (LSD) is applied to detect line segment, and then intersecting line primitive is extracted. Finally, robustness and accuracy of the plane geometry primitive presentation method is studied. This method has a good ability to obtain structural information of the object, even if there is rotation or scale change of the object in the image. Experimental results verify the robustness and accuracy of this method.

The role of landscape and habitat characteristics in limiting abundance of grassland nesting songbirds in an urban open space

USGS Publications Warehouse

Haire, S.; Bock, C.E.; Cade, B.S.; Bennett, B.C.

2000-01-01

We examine the relationships between abundance of grassland nesting songbirds observed in the Boulder Open Space, CO, USA and parameters that described landscape and habitat characteristics, in order to provide information for Boulder Open Space planners and managers. Data sets included bird abundance and plant species composition, collected during three breeding seasons (1994–1996), and landscape composition and configuration measures from a satellite image-derived land-cover map. We used regression quantiles to estimate the limitations imposed on bird abundance by urban encroachment and decreasing areas of grassland cover-types on the landscape, and habitat characteristics within 200 m diameter sample plots. After accounting for the effect of landscape grassland composition on four species (Western Meadowlark (Sturnella neglecta), Vesper Sparrow (Pooecetes gramineus), Horned Lark (Eremophila alpestris), and Grasshopper Sparrow (Ammodramus savannarum)), change in abundance with proportion of urban area in the landscape was consistent with the pattern expected for limiting factors that were the active constraint at some times and places. Area of preferred grassland cover-types on the landscape was important for all species, and this remained the case when habitat variables were included in combined landscape–habitat models, with one exception (Western Meadowlark). Analysis of habitat variables enabled identification of important features at the local scale (e.g. shale plant communities in Lark Sparrow (Chondestes grammacus) habitat) that were indistinguishable using landscape data alone. Consideration of changes in the landscape due to urbanization and loss of grassland habitat are crucial for open space planning, and habitat features associated with localized and clumped bird species distributions provide important additional information. Widening the management focus to include areas that are not part of the open space system will facilitate a more complete understanding of potential limiting factor processes.

Sustaining complex interventions in long-term care: a qualitative study of direct care staff and managers.

PubMed

Colón-Emeric, Cathleen; Toles, Mark; Cary, Michael P; Batchelor-Murphy, Melissa; Yap, Tracey; Song, Yuting; Hall, Rasheeda; Anderson, Amber; Burd, Andrew; Anderson, Ruth A

2016-07-16

Little is known about the sustainability of behavioral change interventions in long-term care (LTC). Following a cluster randomized trial of an intervention to improve staff communication (CONNECT), we conducted focus groups of direct care staff and managers to elicit their perceptions of factors that enhance or reduce sustainability in the LTC setting. The overall aim was to generate hypotheses about how to sustain complex interventions in LTC. In eight facilities, we conducted 15 focus groups with 83 staff who had participated in at least one intervention session. Where possible, separate groups were conducted with direct care staff and managers. An interview guide probed for staff perceptions of intervention salience and sustainability. Framework analysis of coded transcripts was used to distill insights about sustainability related to intervention features, organizational context, and external supports. Staff described important factors for intervention sustainability that are particularly challenging in LTC. Because of the tremendous diversity in staff roles and education level, interventions should balance complexity and simplicity, use a variety of delivery methods and venues (e.g., group and individual sessions, role-play/storytelling), and be inclusive of many work positions. Intervention customizability and flexibility was particularly prized in this unpredictable and resource-strapped environment. Contextual features noted to be important include addressing the frequent lack of trust between direct care staff and managers and ensuring that direct care staff directly observe manager participation and support for the program. External supports suggested to be useful for sustainability include formalization of changes into facility routines, using "train the trainer" approaches and refresher sessions. High staff turnover is common in LTC, and providing materials for new staff orientation was reported to be important for sustainability. When designing or implementing complex behavior change interventions in LTC, consideration of these particularly salient intervention features, contextual factors, and external supports identified by staff may enhance sustainability. ClinicalTrial.gov, NCT00636675.

Detection of white matter lesions in cerebral small vessel disease

NASA Astrophysics Data System (ADS)

Riad, Medhat M.; Platel, Bram; de Leeuw, Frank-Erik; Karssemeijer, Nico

2013-02-01

White matter lesions (WML) are diffuse white matter abnormalities commonly found in older subjects and are important indicators of stroke, multiple sclerosis, dementia and other disorders. We present an automated WML detection method and evaluate it on a dataset of small vessel disease (SVD) patients. In early SVD, small WMLs are expected to be of importance for the prediction of disease progression. Commonly used WML segmentation methods tend to ignore small WMLs and are mostly validated on the basis of total lesion load or a Dice coefficient for all detected WMLs. Therefore, in this paper, we present a method that is designed to detect individual lesions, large or small, and we validate the detection performance of our system with FROC (free-response ROC) analysis. For the automated detection, we use supervised classification making use of multimodal voxel based features from different magnetic resonance imaging (MRI) sequences, including intensities, tissue probabilities, voxel locations and distances, neighborhood textures and others. After preprocessing, including co-registration, brain extraction, bias correction, intensity normalization, and nonlinear registration, ventricle segmentation is performed and features are calculated for each brain voxel. A gentle-boost classifier is trained using these features from 50 manually annotated subjects to give each voxel a probability of being a lesion voxel. We perform ROC analysis to illustrate the benefits of using additional features to the commonly used voxel intensities; significantly increasing the area under the curve (Az) from 0.81 to 0.96 (p<0.05). We perform the FROC analysis by testing our classifier on 50 previously unseen subjects and compare the results with manual annotations performed by two experts. Using the first annotator results as our reference, the second annotator performs at a sensitivity of 0.90 with an average of 41 false positives per subject while our automated method reached the same level of sensitivity at approximately 180 false positives per subject.

Phonological Feature Re-Assembly and the Importance of Phonetic Cues

ERIC Educational Resources Information Center

Archibald, John

2009-01-01

It is argued that new phonological features can be acquired in second languages, but that both feature acquisition and feature re-assembly are affected by the robustness of phonetic cues in the input.

Kinematic Features of Jaw and Lips Distinguish Symptomatic From Presymptomatic Stages of Bulbar Decline in Amyotrophic Lateral Sclerosis.

PubMed

Bandini, Andrea; Green, Jordan R; Wang, Jun; Campbell, Thomas F; Zinman, Lorne; Yunusova, Yana

2018-05-17

The goals of this study were to (a) classify speech movements of patients with amyotrophic lateral sclerosis (ALS) in presymptomatic and symptomatic phases of bulbar function decline relying solely on kinematic features of lips and jaw and (b) identify the most important measures that detect the transition between early and late bulbar changes. One hundred ninety-two recordings obtained from 64 patients with ALS were considered for the analysis. Feature selection and classification algorithms were used to analyze lip and jaw movements recorded with Optotrak Certus (Northern Digital Inc.) during a sentence task. A feature set, which included 35 measures of movement range, velocity, acceleration, jerk, and area measures of lips and jaw, was used to classify sessions according to the speaking rate into presymptomatic (> 160 words per minute) and symptomatic (< 160 words per minute) groups. Presymptomatic and symptomatic phases of bulbar decline were distinguished with high accuracy (87%), relying only on lip and jaw movements. The best features that allowed detecting the differences between early and later bulbar stages included cumulative path of lower lip and jaw, peak values of velocity, acceleration, and jerk of lower lip and jaw. The results established a relationship between facial kinematics and bulbar function decline in ALS. Considering that facial movements can be recorded by means of novel inexpensive and easy-to-use, video-based methods, this work supports the development of an automatic system for facial movement analysis to help clinicians in tracking the disease progression in ALS.

Patient-derived xenografts as preclinical neuroblastoma models.

PubMed

Braekeveldt, Noémie; Bexell, Daniel

2018-05-01

The prognosis for children with high-risk neuroblastoma is often poor and survivors can suffer from severe side effects. Predictive preclinical models and novel therapeutic strategies for high-risk disease are therefore a clinical imperative. However, conventional cancer cell line-derived xenografts can deviate substantially from patient tumors in terms of their molecular and phenotypic features. Patient-derived xenografts (PDXs) recapitulate many biologically and clinically relevant features of human cancers. Importantly, PDXs can closely parallel clinical features and outcome and serve as excellent models for biomarker and preclinical drug development. Here, we review progress in and applications of neuroblastoma PDX models. Neuroblastoma orthotopic PDXs share the molecular characteristics, neuroblastoma markers, invasive properties and tumor stroma of aggressive patient tumors and retain spontaneous metastatic capacity to distant organs including bone marrow. The recent identification of genomic changes in relapsed neuroblastomas opens up opportunities to target treatment-resistant tumors in well-characterized neuroblastoma PDXs. We highlight and discuss the features and various sources of neuroblastoma PDXs, methodological considerations when establishing neuroblastoma PDXs, in vitro 3D models, current limitations of PDX models and their application to preclinical drug testing.

Family-based risk factors for non-suicidal self-injury: Considering influences of maltreatment, adverse family-life experiences, and parent-child relational risk.

PubMed

Martin, Jodi; Bureau, Jean-François; Yurkowski, Kim; Fournier, Tania Renaud; Lafontaine, Marie-France; Cloutier, Paula

2016-06-01

The current investigation addressed the potential for unique influences of perceived childhood maltreatment, adverse family-life events, and parent-child relational trauma on the lifetime occurrence and addictive features of non-suicidal self-injury (NSSI). Participants included 957 undergraduate students (747 females; M = 20.14 years, SD = 3.88) who completed online questionnaires regarding the key variables under study. Although self-injuring youth reported more experiences with each family-based risk factor, different patterns of association were found when lifetime engagement in NSSI or its addictive features were under study. Perceived parent-child relational trauma was uniquely linked with NSSI behavior after accounting for perceived childhood maltreatment; adverse family-life events had an additional unique association. In contrast, perceived paternal maltreatment was uniquely related with NSSI's addictive features. Findings underline the importance of studying inter-related family-based risk factors of NSSI simultaneously for a comprehensive understanding of familial correlates of NSSI behavior and its underlying features. Copyright © 2016 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

Abnormal semantic knowledge in a case of developmental amnesia.

PubMed

Blumenthal, Anna; Duke, Devin; Bowles, Ben; Gilboa, Asaf; Rosenbaum, R Shayna; Köhler, Stefan; McRae, Ken

2017-07-28

An important theory holds that semantic knowledge can develop independently of episodic memory. One strong source of evidence supporting this independence comes from the observation that individuals with early hippocampal damage leading to developmental amnesia generally perform normally on standard tests of semantic memory, despite their profound impairment in episodic memory. However, one aspect of semantic memory that has not been explored is conceptual structure. We built on the theoretically important distinction between intrinsic features of object concepts (e.g., shape, colour, parts) and extrinsic features (e.g., how something is used, where it is typically located). The accrual of extrinsic feature knowledge that is important for concepts such as chair or spoon may depend on binding mechanisms in the hippocampus. We tested HC, an individual with developmental amnesia due to a well-characterized lesion of the hippocampus, on her ability to generate semantic features for object concepts. HC generated fewer extrinsic features than controls, but a similar number of intrinsic features than controls. We also tested her on typicality ratings. Her typicality ratings were abnormal for nonliving things (which more strongly depend on extrinsic features), but normal for living things (which more strongly depend on intrinsic features). In contrast, NB, who has MTL but not hippocampal damage due to surgery, showed no impairments in either task. These results suggest that episodic and semantic memory are not entirely independent, and that the hippocampus is important for learning some aspects of conceptual knowledge. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cerebral correlates of psychotic syndromes in neurodegenerative diseases.

PubMed

Jellinger, Kurt A

2012-05-01

Psychosis has been recognized as a common feature in neurodegenerative diseases and a core feature of dementia that worsens most clinical courses. It includes hallucinations, delusions including paranoia, aggressive behaviour, apathy and other psychotic phenomena that occur in a wide range of degenerative disorders including Alzheimer's disease, synucleinopathies (Parkinson's disease, dementia with Lewy bodies), Huntington's disease, frontotemporal degenerations, motoneuron and prion diseases. Many of these psychiatric manifestations may be early expressions of cognitive impairment, but often there is a dissociation between psychotic/behavioural symptoms and the rather linear decline in cognitive function, suggesting independent pathophysiological mechanisms. Strictly neuropathological explanations are likely to be insufficient to explain them, and a large group of heterogeneous factors (environmental, neurochemical changes, genetic factors, etc.) may influence their pathogenesis. Clinico-pathological evaluation of behavioural and psychotic symptoms (PS) in the setting of neurodegenerative and dementing disorders presents a significant challenge for modern neurosciences. Recognition and understanding of these manifestations may lead to the development of more effective preventive and therapeutic options that can serve to delay long-term progression of these devastating disorders and improve the patients' quality of life. A better understanding of the pathophysiology and distinctive pathological features underlying the development of PS in neurodegenerative diseases may provide important insights into psychotic processes in general. © 2011 The Author Journal of Cellular and Molecular Medicine © 2011 Foundation for Cellular and Molecular Medicine/Blackwell Publishing Ltd.

Stochastic Nature in Cellular Processes

NASA Astrophysics Data System (ADS)

Liu, Bo; Liu, Sheng-Jun; Wang, Qi; Yan, Shi-Wei; Geng, Yi-Zhao; Sakata, Fumihiko; Gao, Xing-Fa

2011-11-01

The importance of stochasticity in cellular processes is increasingly recognized in both theoretical and experimental studies. General features of stochasticity in gene regulation and expression are briefly reviewed in this article, which include the main experimental phenomena, classification, quantization and regulation of noises. The correlation and transmission of noise in cascade networks are analyzed further and the stochastic simulation methods that can capture effects of intrinsic and extrinsic noise are described.

Seminar on Understanding Digital Control and Analysis in Vibration Test Systems, part 2

NASA Technical Reports Server (NTRS)

1975-01-01

A number of techniques for dealing with important technical aspects of the random vibration control problem are described. These include the generation of pseudo-random and true random noise, the control spectrum estimation problem, the accuracy/speed tradeoff, and control correction strategies. System hardware, the operator-system interface, safety features, and operational capabilities of sophisticated digital random vibration control systems are also discussed.

Study of Varying Boundary Layer Height on Turret Flow Structures

DTIC Science & Technology

2011-06-01

fluid dynamics. The difficulties of the problem arise in modeling several complex flow features including separation, reattachment, three-dimensional...impossible. In this case, the approach is to create a model to calculate the properties of interest. The main issue with resolving turbulent flows...operation and their effect is modeled through subgrid scale models . As a result, the the most important turbulent scales are resolved and the

Methods of Determining Playa Surface Conditions Using Remote Sensing

DTIC Science & Technology

1987-10-08

NO. 11. TITLE (include Security Classification) METHODS OF DETERMINING PLAYA SURFACE CONDITIONS USING REMOTE SENSING 12. PERSONAL AUTHOR(S) J. PONDER...PLAYA SURFACE CONDITIONS USING REMOTE SENSING J. Ponder Henley U. S. Army Engineer Topographic Laboratories Fort Belvoir, Virginia 22060-5546 "ABSTRACT...geochemistry, hydrology and remote sensing but all of these are important to the understanding of these unique geomorphic features. There is a large body

Stellar Evolution and Modelling Stars

NASA Astrophysics Data System (ADS)

Silva Aguirre, Víctor

In this chapter I give an overall description of the structure and evolution of stars of different masses, and review the main ingredients included in state-of-the-art calculations aiming at reproducing observational features. I give particular emphasis to processes where large uncertainties still exist as they have strong impact on stellar properties derived from large compilations of tracks and isochrones, and are therefore of fundamental importance in many fields of astrophysics.

Maxwell iteration for the lattice Boltzmann method with diffusive scaling

NASA Astrophysics Data System (ADS)

Zhao, Weifeng; Yong, Wen-An

2017-03-01

In this work, we present an alternative derivation of the Navier-Stokes equations from Bhatnagar-Gross-Krook models of the lattice Boltzmann method with diffusive scaling. This derivation is based on the Maxwell iteration and can expose certain important features of the lattice Boltzmann solutions. Moreover, it will be seen to be much more straightforward and logically clearer than the existing approaches including the Chapman-Enskog expansion.

Has Group Work Education Lost Its Social Group Work Essence? A Content Analysis of MSW Course Syllabi in Search of Mutual Aid and Group Conflict Content

ERIC Educational Resources Information Center

Sweifach, Jay Stephen

2015-01-01

This article presents the results of a content analysis of MSW group work course syllabi in an effort to better understand the extent to which mutual aid and group conflict, two important dimensions of social group work, are included and featured as prominent elements in MSW-level group work instruction.

Larvae of five horticulturally important species of Chrysopodes (Neuroptera, Chrysopidae): shared generic features, descriptions and keys

PubMed Central

Silva, Patrícia S.; Tauber, Catherine A.; Albuquerque, Gilberto S.; Tauber, Maurice J.

2013-01-01

Abstract An expanded list of generic level larval characteristics is presented for Chrysopodes; it includes a reinterpretation of the mesothoracic and metathoracic structure and setation. Keys, descriptions and images of Semaphoront A (first instar) and Semaphoront B (second and third instars) are offered for identifying five species of Chrysopodes (Chrysopodes) that are commonly reported from horticultural habitats in the Neotropical region. PMID:23653514

Monozygotic twins discordant for ROHHAD phenotype.

PubMed

Patwari, Pallavi P; Rand, Casey M; Berry-Kravis, Elizabeth M; Ize-Ludlow, Diego; Weese-Mayer, Debra E

2011-09-01

Rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD) falls within a group of pediatric disorders with both respiratory control and autonomic nervous system dysregulation. Children with ROHHAD typically present after 1.5 years of age with rapid weight gain as the initial sign. Subsequently, they develop alveolar hypoventilation, autonomic nervous system dysregulation, and, if untreated, cardiorespiratory arrest. To our knowledge, this is the first report of discordant presentation of ROHHAD in monozygotic twins. Twin girls, born at term, had concordant growth and development until 8 years of age. From 8 to 12 years of age, the affected twin developed features characteristic of ROHHAD including obesity, alveolar hypoventilation, scoliosis, hypothalamic dysfunction (central diabetes insipidus, hypothyroidism, premature pubarche, and growth hormone deficiency), right paraspinal/thoracic ganglioneuroblastoma, seizures, and autonomic dysregulation including altered pain perception, large and sluggishly reactive pupils, hypothermia, and profound bradycardia that required a cardiac pacemaker. Results of genetic testing for PHOX2B (congenital central hypoventilation syndrome disease-defining gene) mutations were negative. With early recognition and conservative management, the affected twin had excellent neurocognitive outcome that matched that of the unaffected twin. The unaffected twin demonstrated rapid weight gain later in age but not development of signs/symptoms consistent with ROHHAD. This discordant twin pair demonstrates key features of ROHHAD including the importance of early recognition (especially hypoventilation), complexity of signs/symptoms and clinical course, and importance of initiating comprehensive, multispecialty care. These cases confound the hypothesis of a monogenic etiology for ROHHAD and indicate alternative etiologies including autoimmune or epigenetic phenomenon or a combination of genetic predisposition and acquired precipitant.

Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms.

PubMed

Athanasiou, Alkinoos; Xygonakis, Ioannis; Pandria, Niki; Kartsidis, Panagiotis; Arfaras, George; Kavazidi, Kyriaki Rafailia; Foroglou, Nicolas; Astaras, Alexander; Bamidis, Panagiotis D

2017-01-01

Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.

Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms

PubMed Central

Xygonakis, Ioannis; Pandria, Niki; Kartsidis, Panagiotis; Arfaras, George; Kavazidi, Kyriaki Rafailia; Foroglou, Nicolas

2017-01-01

Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality. PMID:28948168

Library of Advanced Materials for Engineering (LAME) 4.44.

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

Scherzinger, William M.; Lester, Brian T.

Accurate and efficient constitutive modeling remains a cornerstone issues for solid mechanics analysis. Over the years, the LAME advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to s ti ff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco) plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization ofmore » the LAME library in application, this effort seeks to document and verify the various models in the LAME library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.« less

Library of Advanced Materials for Engineering (LAME) 4.48.

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

Scherzinger, William M.; Lester, Brian T.

Accurate and efficient constitutive modeling remains a cornerstone issues for solid mechanics analysis. Over the years, the LAME advanced material model library has grown to address this challenge by implement- ing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting imple- mentation. Therefore, to enhance confidence and enable the utilization of themore » LAME library in application, this effort seeks to document and verify the various models in the LAME library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verifi- cation tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.« less

A Transparent Window into Biology: A Primer on Caenorhabditis elegans.

PubMed

Corsi, Ann K; Wightman, Bruce; Chalfie, Martin

2015-06-01

A little over 50 years ago, Sydney Brenner had the foresight to develop the nematode (round worm) Caenorhabditis elegans as a genetic model for understanding questions of developmental biology and neurobiology. Over time, research on C. elegans has expanded to explore a wealth of diverse areas in modern biology including studies of the basic functions and interactions of eukaryotic cells, host-parasite interactions, and evolution. C. elegans has also become an important organism in which to study processes that go awry in human diseases. This primer introduces the organism and the many features that make it an outstanding experimental system, including its small size, rapid life cycle, transparency, and well-annotated genome. We survey the basic anatomical features, common technical approaches, and important discoveries in C. elegans research. Key to studying C. elegans has been the ability to address biological problems genetically, using both forward and reverse genetics, both at the level of the entire organism and at the level of the single, identified cell. These possibilities make C. elegans useful not only in research laboratories, but also in the classroom where it can be used to excite students who actually can see what is happening inside live cells and tissues. Copyright © 2015 Corsi, Wightman, and Chalfie.

Information Commons for Rice (IC4R)

PubMed Central

2016-01-01

Rice is the most important staple food for a large part of the world's human population and also a key model organism for plant research. Here, we present Information Commons for Rice (IC4R; http://ic4r.org), a rice knowledgebase featuring adoption of an extensible and sustainable architecture that integrates multiple omics data through community-contributed modules. Each module is developed and maintained by different committed groups, deals with data collection, processing and visualization, and delivers data on-demand via web services. In the current version, IC4R incorporates a variety of rice data through multiple committed modules, including genome-wide expression profiles derived entirely from RNA-Seq data, resequencing-based genomic variations obtained from re-sequencing data of thousands of rice varieties, plant homologous genes covering multiple diverse plant species, post-translational modifications, rice-related literatures and gene annotations contributed by the rice research community. Unlike extant related databases, IC4R is designed for scalability and sustainability and thus also features collaborative integration of rice data and low costs for database update and maintenance. Future directions of IC4R include incorporation of other omics data and association of multiple omics data with agronomically important traits, dedicating to build IC4R into a valuable knowledgebase for both basic and translational researches in rice. PMID:26519466

SHINE Tritium Nozzle Design: Activity 6, Task 1 Report

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

Okhuysen, Brett S.; Pulliam, Elias Noel

In FY14, we studied the qualitative and quantitative behavior of a SHINE/PNL tritium nozzle under varying operating conditions. The result is an understanding of the nozzle’s performance in terms of important flow features that manifest themselves under different parametric profiles. In FY15, we will consider nozzle design with a focus on nozzle geometry and integration. From FY14 work, we will understand how the SHINE/PNL nozzle behaves under different operating scenarios. The first task for FY15 is to evaluate the FY14 model as a predictor of the actual flow. Considering different geometries is more time-intensive than parameter studies, therefore we recommendmore » considering any relevant flow features that were not included in the FY14 model. In the absence of experimental data, it is particularly important to consider any sources of heat in the domain or boundary conditions that may affect the flow and incorporate these into the simulation if they are significant. Additionally, any geometric features of the beamline segment should be added to the model such as the orifice plate. The FY14 model works with hydrogen. An improvement that can be made for FY15 is to develop CFD properties for tritium and incorporate those properties into the new models.« less

Impact of Recent Hardware and Software Trends on High Performance Transaction Processing and Analytics

NASA Astrophysics Data System (ADS)

Mohan, C.

In this paper, I survey briefly some of the recent and emerging trends in hardware and software features which impact high performance transaction processing and data analytics applications. These features include multicore processor chips, ultra large main memories, flash storage, storage class memories, database appliances, field programmable gate arrays, transactional memory, key-value stores, and cloud computing. While some applications, e.g., Web 2.0 ones, were initially built without traditional transaction processing functionality in mind, slowly system architects and designers are beginning to address such previously ignored issues. The availability, analytics and response time requirements of these applications were initially given more importance than ACID transaction semantics and resource consumption characteristics. A project at IBM Almaden is studying the implications of phase change memory on transaction processing, in the context of a key-value store. Bitemporal data management has also become an important requirement, especially for financial applications. Power consumption and heat dissipation properties are also major considerations in the emergence of modern software and hardware architectural features. Considerations relating to ease of configuration, installation, maintenance and monitoring, and improvement of total cost of ownership have resulted in database appliances becoming very popular. The MapReduce paradigm is now quite popular for large scale data analysis, in spite of the major inefficiencies associated with it.

Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning

NASA Astrophysics Data System (ADS)

Vetrivel, Anand; Gerke, Markus; Kerle, Norman; Nex, Francesco; Vosselman, George

2018-06-01

Oblique aerial images offer views of both building roofs and façades, and thus have been recognized as a potential source to detect severe building damages caused by destructive disaster events such as earthquakes. Therefore, they represent an important source of information for first responders or other stakeholders involved in the post-disaster response process. Several automated methods based on supervised learning have already been demonstrated for damage detection using oblique airborne images. However, they often do not generalize well when data from new unseen sites need to be processed, hampering their practical use. Reasons for this limitation include image and scene characteristics, though the most prominent one relates to the image features being used for training the classifier. Recently features based on deep learning approaches, such as convolutional neural networks (CNNs), have been shown to be more effective than conventional hand-crafted features, and have become the state-of-the-art in many domains, including remote sensing. Moreover, often oblique images are captured with high block overlap, facilitating the generation of dense 3D point clouds - an ideal source to derive geometric characteristics. We hypothesized that the use of CNN features, either independently or in combination with 3D point cloud features, would yield improved performance in damage detection. To this end we used CNN and 3D features, both independently and in combination, using images from manned and unmanned aerial platforms over several geographic locations that vary significantly in terms of image and scene characteristics. A multiple-kernel-learning framework, an effective way for integrating features from different modalities, was used for combining the two sets of features for classification. The results are encouraging: while CNN features produced an average classification accuracy of about 91%, the integration of 3D point cloud features led to an additional improvement of about 3% (i.e. an average classification accuracy of 94%). The significance of 3D point cloud features becomes more evident in the model transferability scenario (i.e., training and testing samples from different sites that vary slightly in the aforementioned characteristics), where the integration of CNN and 3D point cloud features significantly improved the model transferability accuracy up to a maximum of 7% compared with the accuracy achieved by CNN features alone. Overall, an average accuracy of 85% was achieved for the model transferability scenario across all experiments. Our main conclusion is that such an approach qualifies for practical use.

Sentiment Analysis Using Common-Sense and Context Information

PubMed Central

Mittal, Namita; Bansal, Pooja; Garg, Sonal

2015-01-01

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods. PMID:25866505

Sentiment analysis using common-sense and context information.

PubMed

Agarwal, Basant; Mittal, Namita; Bansal, Pooja; Garg, Sonal

2015-01-01

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.

The importance of living botanical collections for plant biology and the “next generation” of evo-devo research

PubMed Central

Dosmann, Michael; Groover, Andrew

2012-01-01

Living botanical collections include germplasm repositories, long-term experimental plantings, and botanical gardens. We present here a series of vignettes to illustrate the central role that living collections have played in plant biology research, including evo-devo research. Looking toward the future, living collections will become increasingly important in support of future evo-devo research. The driving force behind this trend is nucleic acid sequencing technologies, which are rapidly becoming more powerful and cost-effective, and which can be applied to virtually any species. This allows for more extensive sampling, including non-model organisms with unique biological features and plants from diverse phylogenetic positions. Importantly, a major challenge for sequencing-based evo-devo research is to identify, access, and propagate appropriate plant materials. We use a vignette of the ongoing 1,000 Transcriptomes project as an example of the challenges faced by such projects. We conclude by identifying some of the pinch points likely to be encountered by future evo-devo researchers, and how living collections can help address them. PMID:22737158

Feature selection using probabilistic prediction of support vector regression.

PubMed

Yang, Jian-Bo; Ong, Chong-Jin

2011-06-01

This paper presents a new wrapper-based feature selection method for support vector regression (SVR) using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiments show that the proposed method generally performs better than, or at least as well as, the existing methods, with notable advantage when the dataset is sparse.

Landscape of the spliced leader trans-splicing mechanism in Schistosoma mansoni.

PubMed

Boroni, Mariana; Sammeth, Michael; Gava, Sandra Grossi; Jorge, Natasha Andressa Nogueira; Macedo, Andréa Mara; Machado, Carlos Renato; Mourão, Marina Moraes; Franco, Glória Regina

2018-03-01

Spliced leader dependent trans-splicing (SLTS) has been described as an important RNA regulatory process that occurs in different organisms, including the trematode Schistosoma mansoni. We identified more than seven thousand putative SLTS sites in the parasite, comprising genes with a wide spectrum of functional classes, which underlines the SLTS as a ubiquitous mechanism in the parasite. Also, SLTS gene expression levels span several orders of magnitude, showing that SLTS frequency is not determined by the expression level of the target gene, but by the presence of particular gene features facilitating or hindering the trans-splicing mechanism. Our in-depth investigation of SLTS events demonstrates widespread alternative trans-splicing (ATS) acceptor sites occurring in different regions along the entire gene body, highlighting another important role of SLTS generating alternative RNA isoforms in the parasite, besides the polycistron resolution. Particularly for introns where SLTS directly competes for the same acceptor substrate with cis-splicing, we identified for the first time additional and important features that might determine the type of splicing. Our study substantially extends the current knowledge of RNA processing by SLTS in S. mansoni, and provide basis for future studies on the trans-splicing mechanism in other eukaryotes.

'I want to feel at home': establishing what aspects of environmental design are important to people with dementia nearing the end of life.

PubMed

Fleming, Richard; Kelly, Fiona; Stillfried, Gillian

2015-05-12

The design of environments in which people with dementia live should be understandable, reinforce personal identity and maintain their abilities. The focus on supporting people with dementia to live well has omitted considering the needs or wishes for a supportive physical environment of those who are nearing the end of their lives. Using a combination of focus groups and a Delphi survey, this study explored the views of people with dementia, family carers and professionals on what aspects of the physical environment would be important to support a good quality of life to the very end. Three focus groups were carried out in three cities along the East Coast of Australia using a discussion guide informed by a literature review. Focus groups comprised recently bereaved family carers of people with dementia (FG1), people with dementia and family carers of people with dementia (FG2) and practitioners caring for people with dementia nearing or at the end of their lives (FG3). Focus group conversations were audio-recorded with participants' consent. Audio files were transcribed verbatim and analysed thematically to identify environmental features that could contribute to achieving the goal of providing a comfortable life to the end. A list of design features derived from analysis of focus group transcripts was distributed to a range of experts in the dementia field and a consensus sought on their appropriateness. From this, a set of features to inform the design of environments for people with dementia nearing the end of life was defined. Eighteen people took part in three focus groups: two with dementia, eleven current or recently bereaved family carers and five practitioners. There were differences in opinion on what were important environmental considerations. People with dementia and family carers identified comfort through engagement, feeling at home, a calm environment, privacy and dignity and use of technology to remain connected as important. For practitioners, design to facilitate duty of care and institutional influences on their practice were salient themes. Twenty one experts in the dementia field took part in the survey to agree a consensus on the desirable features derived from analysis of focus group transcripts, with fifteen features agreed. The fifteen features are compatible with the design principles for people with dementia who are mobile, but include a stronger focus on sensory engagement. We suggest that considering these features as part of a continuum of care will support good practice and offer those with dementia the opportunity to live well until the end and give their families a more positive experience in the last days of their lives together.

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification.

PubMed

Huang, Chuen-Der; Lin, Chin-Teng; Pal, Nikhil Ranjan

2003-12-01

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.

Is There a Good App for That? Evaluating m-Health Apps for Strategies That Promote Pediatric Medication Adherence.

PubMed

Nguyen, Eve; Bugno, Lindsey; Kandah, Cassandra; Plevinsky, Jill; Poulopoulos, Natasha; Wojtowicz, Andrea; Schneider, Kristin L; Greenley, Rachel Neff

2016-11-01

Mobile health medication reminder apps may be a useful supplement to traditional adherence-promotion interventions for pediatric chronic illness populations because they can give real-time reminders and provide education and promote behavior modification (components known to enhance adherence in traditional interventions) in an engaging and developmentally acceptable way. Moreover, apps have the potential to be used by youth and parents, an important consideration given that shared involvement in condition management is associated with better adherence. This study evaluated the content and usability of existing medication reminder apps operating on the Apple platform. Two researchers coded 101 apps on 15 desirable reminder, educational, and behavioral modification features. Usability testing was conducted with the subset of apps (n = 8) that had the greatest number of content features using a validated measure. Apps contained an average of 4.21 of 15 content features, with medication reminder features being more common than either educational or behavioral modification features. Apps most commonly included a medication name storage feature (95%), a time-based reminder feature (87%), and a medication dosage storage feature (68%). Of the eight apps that had the highest number of content features, Mango Health, myRX Planner, and MediSafe evidenced the highest usability ratings. No apps identified were specifically designed for pediatric use. Most apps lacked content known to be useful in traditional pediatric adherence-promotion interventions. Greater attention to educational and behavioral modification features may enhance the usefulness of medication reminder apps for pediatric groups. Collaborations between behavioral medicine providers and app developers may improve the quality of medication reminder apps for use in pediatric populations.

Paleopathological Study of Dwarfism-Related Skeletal Dysplasia in a Late Joseon Dynasty (South Korean) Population.

PubMed

Woo, Eun Jin; Lee, Won-Joon; Hu, Kyung-Seok; Hwang, Jae Joon

2015-01-01

Skeletal dysplasias related to genetic etiologies have rarely been reported for past populations. This report presents the skeletal characteristics of an individual with dwarfism-related skeletal dysplasia from South Korea. To assess abnormal deformities, morphological features, metric data, and computed tomography scans are analyzed. Differential diagnoses include achondroplasia or hypochondroplasia, chondrodysplasia, multiple epiphyseal dysplasia, thalassemia-related hemolytic anemia, and lysosomal storage disease. The diffused deformities in the upper-limb bones and several coarsened features of the craniofacial bones indicate the most likely diagnosis to have been a certain type of lysosomal storage disease. The skeletal remains of EP-III-4-No.107 from the Eunpyeong site, although incomplete and fragmented, provide important clues to the paleopathological diagnosis of skeletal dysplasias.

Quetiapine responsive catatonia in an autistic patient with comorbid bipolar disorder and idiopathic basal ganglia calcification.

PubMed

Ishitobi, Makoto; Kawatani, Masao; Asano, Mizuki; Kosaka, Hirotaka; Goto, Takashi; Hiratani, Michio; Wada, Yuji

2014-10-01

Bipolar disorder (BD) has been linked with the manifestation of catatonia in subjects with autism spectrum disorders (ASD). Idiopathic basal ganglia calcification (IBGC) is characterized by movement disorders and various neuropsychiatric disturbances including mood disorder. We present a patient with ASD and IBGC who developed catatonia presenting with prominent dystonic feature caused by comorbid BD, which was treated effectively with quetiapine. In addition to considering the possibility of neurodegenerative disease, careful psychiatric interventions are important to avoid overlooking treatable catatonia associated with BD in cases of ASD presenting with both prominent dystonic features and apparent fluctuation of the mood state. Copyright © 2014 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Morphometrical study on senile larynx.

PubMed

Zieliński, R

2001-01-01

The aim of the study was a morphometrical macroscopic evaluation of senile larynges, according to its usefulness in ORL diagnostic and operational methods. Larynx preparations were taken from cadavers of both sexes, of age 65 and over, about 24 hours after death. Clinically important laryngeal diameters were collected using common morphometrical methods. A few body features were also being gathered. Computer statistical methods were used in data assessment, including basic statistics and linear correlations between diameters and between diameters and body features. The data presented in the study may be very helpful in evaluation of diagnostic methods. It may also help in selection of right operational tool' sizes, the most appropriate operational technique choice, preoperative preparations and designing and building virtual and plastic models for physicians' training.

Literary aesthetics: beauty, the brain, and Mrs. Dalloway.

PubMed

Hogan, Patrick Colm

2013-01-01

Empirical research indicates that beauty is in part a matter of prototype approximation. Some research suggests that unanticipated pattern recognition is important as well. This essay begins by briefly outlining an account of beauty based on these factors. It goes on to consider complications. Minor complications include the partial incompatibility of these accounts and the importance of differentiating judgments of beauty from aesthetic response. More serious issues include the relative neglect of literature in neurologically-based discussions of beauty, which tend to focus on music or visual art. There is also a relative neglect of emotion, beyond the reward system. Finally, there is the almost complete absence of the sublime. After considering these problems broadly, the essay turns to Virginia Woolf's Mrs. Dalloway, examining its treatment of beauty and sublimity. The aim of this section is not merely to illuminate Woolf's novel by reference to neuroscientific research. It is equally, perhaps more fully, to expand our neuroscientifically grounded account of aesthetic response by drawing on Woolf's novel. In Mrs. Dalloway, there are gestures toward prototypes and patterns in beauty. But the key features are clearly emotional. Specifically, the emotions at issue in feelings of beauty and sublimity appear to be primarily attachment, on the one hand, and a profound sense of isolation, on the other. Woolf's novel also points us toward other features of aesthetic experience, crucially including the emotion-sharing that is a key function of the production and circulation of art. © 2013 Elsevier B.V. All rights reserved.

Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy

NASA Astrophysics Data System (ADS)

Valdes, Gilmer; Solberg, Timothy D.; Heskel, Marina; Ungar, Lyle; Simone, Charles B., II

2016-08-01

To develop a patient-specific ‘big data’ clinical decision tool to predict pneumonitis in stage I non-small cell lung cancer (NSCLC) patients after stereotactic body radiation therapy (SBRT). 61 features were recorded for 201 consecutive patients with stage I NSCLC treated with SBRT, in whom 8 (4.0%) developed radiation pneumonitis. Pneumonitis thresholds were found for each feature individually using decision stumps. The performance of three different algorithms (Decision Trees, Random Forests, RUSBoost) was evaluated. Learning curves were developed and the training error analyzed and compared to the testing error in order to evaluate the factors needed to obtain a cross-validated error smaller than 0.1. These included the addition of new features, increasing the complexity of the algorithm and enlarging the sample size and number of events. In the univariate analysis, the most important feature selected was the diffusion capacity of the lung for carbon monoxide (DLCO adj%). On multivariate analysis, the three most important features selected were the dose to 15 cc of the heart, dose to 4 cc of the trachea or bronchus, and race. Higher accuracy could be achieved if the RUSBoost algorithm was used with regularization. To predict radiation pneumonitis within an error smaller than 10%, we estimate that a sample size of 800 patients is required. Clinically relevant thresholds that put patients at risk of developing radiation pneumonitis were determined in a cohort of 201 stage I NSCLC patients treated with SBRT. The consistency of these thresholds can provide radiation oncologists with an estimate of their reliability and may inform treatment planning and patient counseling. The accuracy of the classification is limited by the number of patients in the study and not by the features gathered or the complexity of the algorithm.

Assessment of current and proposed nature reserves of Mexico based on their capacity to protect geophysical features and biodiversity

USGS Publications Warehouse

Cantu, C.; Wright, R.G.; Scott, J.M.; Strand, Espen

2004-01-01

Mexico currently has 144 nature reserves covering approximately 9.1% of its land area. These reserves were established for a variety of reasons - often unrelated to the protection of biodiversity. In 2000 in response to a growing concern about the lack of organized conservation reserve planning to protect the important threatened biological and physical features of Mexico, the Mexican Commission for Knowledge and Use of Biodiversity (CONABIO) proposed the establishment of 151 new reserves for Mexico covering 51,429,500 ha. We compiled a GIS analysis using digital thematic maps of physical and biological features to examine how the existing and proposed reserves serve to protect the biodiversity and physical features of the country. Using a conservation target of placing a minimum of 12% of the land area of each important biophysical feature in nature reserves, we found that the 144 existing nature reserves covering 18 million ha (9% of the country) only meet that target for elevation ranges >3000 m and areas with poor soils. These mountainous areas represent less than 1% of the country. The gaps in the existing nature reserves network occur mainly at lower and intermediate elevations (<3000 m) areas with xeric, tropical, and temperate ecosystems, and high productivity soils. The areas proposed by CONABIO increase the proportion of protected lands in the country to over 27% and most of the conservation targets for geophysical features, and land cover, categories are met. Whether this area would be sufficient to maintain viable populations and ecological integrity of species and ecosystems is unknown. Even with the new reserves, low elevation coastal lands would be below the conservation target in the nature reserves. To include a representative sample of these lands would be difficult as these are the same areas where the majority of people live. ?? 2003 Elsevier Ltd. All rights reserved.

Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches.

PubMed

Morris, Jeffrey S

2012-01-01

In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational aspects of comparative proteomic studies, and summarizes contributions I along with numerous collaborators have made. First, there is an overview of comparative proteomics technologies, followed by a discussion of important experimental design and preprocessing issues that must be considered before statistical analysis can be done. Next, the two key approaches to analyzing proteomics data, feature extraction and functional modeling, are described. Feature extraction involves detection and quantification of discrete features like peaks or spots that theoretically correspond to different proteins in the sample. After an overview of the feature extraction approach, specific methods for mass spectrometry ( Cromwell ) and 2D gel electrophoresis ( Pinnacle ) are described. The functional modeling approach involves modeling the proteomic data in their entirety as functions or images. A general discussion of the approach is followed by the presentation of a specific method that can be applied, wavelet-based functional mixed models, and its extensions. All methods are illustrated by application to two example proteomic data sets, one from mass spectrometry and one from 2D gel electrophoresis. While the specific methods presented are applied to two specific proteomic technologies, MALDI-TOF and 2D gel electrophoresis, these methods and the other principles discussed in the paper apply much more broadly to other expression proteomics technologies.

Building Data and Information Capacity in Environmental Public Health: A Best-Worst Scaling Experiment.

PubMed

Wallar, Lauren E; Sargeant, Jan M; McEwen, Scott A; Mercer, Nicola J; Papadopoulos, Andrew

Environmental public health practitioners rely on information technology (IT) to maintain and improve environmental health. However, current systems have limited capacity. A better understanding of the importance of IT features is needed to enhance data and information capacity. (1) Rank IT features according to the percentage of respondents who rated them as essential to an information management system and (2) quantify the relative importance of a subset of these features using best-worst scaling. Information technology features were initially identified from a previously published systematic review of software evaluation criteria and a list of software options from a private corporation specializing in inspection software. Duplicates and features unrelated to environmental public health were removed. The condensed list was refined by a working group of environmental public health management to a final list of 57 IT features. The essentialness of features was electronically rated by environmental public health managers. Features where 50% to 80% of respondents rated them as essential (n = 26) were subsequently evaluated using best-worst scaling. Ontario, Canada. Environmental public health professionals in local public health. Importance scores of IT features. The majority of IT features (47/57) were considered essential to an information management system by at least half of the respondents (n = 52). The highest-rated features were delivery to printer, software encryption capability, and software maintenance services. Of the 26 features evaluated in the best-worst scaling exercise, the most important features were orientation to all practice areas, off-line capability, and ability to view past inspection reports and results. The development of a single, unified environmental public health information management system that fulfills the reporting and functionality needs of system users is recommended. This system should be implemented by all public health units to support data and information capacity in local environmental public health. This study can be used to guide vendor evaluation, negotiation, and selection in local environmental public health, and provides an example of academia-practice partnerships and the use of best-worst scaling in public health research.

High Dimensional Classification Using Features Annealed Independence Rules.

PubMed

Fan, Jianqing; Fan, Yingying

2008-01-01

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

A feature selection approach towards progressive vector transmission over the Internet

NASA Astrophysics Data System (ADS)

Miao, Ru; Song, Jia; Feng, Min

2017-09-01

WebGIS has been applied for visualizing and sharing geospatial information popularly over the Internet. In order to improve the efficiency of the client applications, the web-based progressive vector transmission approach is proposed. Important features should be selected and transferred firstly, and the methods for measuring the importance of features should be further considered in the progressive transmission. However, studies on progressive transmission for large-volume vector data have mostly focused on map generalization in the field of cartography, but rarely discussed on the selection of geographic features quantitatively. This paper applies information theory for measuring the feature importance of vector maps. A measurement model for the amount of information of vector features is defined based upon the amount of information for dealing with feature selection issues. The measurement model involves geometry factor, spatial distribution factor and thematic attribute factor. Moreover, a real-time transport protocol (RTP)-based progressive transmission method is then presented to improve the transmission of vector data. To clearly demonstrate the essential methodology and key techniques, a prototype for web-based progressive vector transmission is presented, and an experiment of progressive selection and transmission for vector features is conducted. The experimental results indicate that our approach clearly improves the performance and end-user experience of delivering and manipulating large vector data over the Internet.

Linguistic feature analysis for protein interaction extraction

PubMed Central

2009-01-01

Background The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels. Results Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared. Conclusion Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches. PMID:19909518

Differences in manifestations of Marfan syndrome, Ehlers-Danlos syndrome, and Loeys-Dietz syndrome.

PubMed

Meester, Josephina A N; Verstraeten, Aline; Schepers, Dorien; Alaerts, Maaike; Van Laer, Lut; Loeys, Bart L

2017-11-01

Many different heritable connective tissue disorders (HCTD) have been described over the past decades. These syndromes often affect the connective tissue of various organ systems, including heart, blood vessels, skin, joints, bone, eyes, and lungs. The discovery of these HCTD was followed by the identification of mutations in a wide range of genes encoding structural proteins, modifying enzymes, or components of the TGFβ-signaling pathway. Three typical examples of HCTD are Marfan syndrome (MFS), Ehlers-Danlos syndrome (EDS), and Loeys-Dietz syndrome (LDS). These syndromes show some degree of phenotypical overlap of cardiovascular, skeletal, and cutaneous features. MFS is typically characterized by cardiovascular, ocular, and skeletal manifestations and is caused by heterozygous mutations in FBN1 , coding for the extracellular matrix (ECM) protein fibrillin-1. The most common cardiovascular phenotype involves aortic aneurysm and dissection at the sinuses of Valsalva. LDS is caused by mutations in TGBR1/2 , SMAD2/3 , or TGFB2/3 , all coding for components of the TGFβ-signaling pathway. LDS can be distinguished from MFS by the unique presence of hypertelorism, bifid uvula or cleft palate, and widespread aortic and arterial aneurysm and tortuosity. Compared to MFS, LDS cardiovascular manifestations tend to be more severe. In contrast, no association is reported between LDS and the presence of ectopia lentis, a key distinguishing feature of MFS. Overlapping features between MFS and LDS include scoliosis, pes planus, anterior chest deformity, spontaneous pneumothorax, and dural ectasia. EDS refers to a group of clinically and genetically heterogeneous connective tissue disorders and all subtypes are characterized by variable abnormalities of skin, ligaments and joints, blood vessels, and internal organs. Typical presenting features include joint hypermobility, skin hyperextensibility, and tissue fragility. Up to one quarter of the EDS patients show aortic aneurysmal disease. The latest EDS nosology distinguishes 13 subtypes. Many phenotypic features show overlap between the different subtypes, which makes the clinical diagnosis rather difficult and highlights the importance of molecular diagnostic confirmation.

Differences in manifestations of Marfan syndrome, Ehlers-Danlos syndrome, and Loeys-Dietz syndrome

PubMed Central

Meester, Josephina A. N.; Verstraeten, Aline; Schepers, Dorien; Alaerts, Maaike; Van Laer, Lut

2017-01-01

Many different heritable connective tissue disorders (HCTD) have been described over the past decades. These syndromes often affect the connective tissue of various organ systems, including heart, blood vessels, skin, joints, bone, eyes, and lungs. The discovery of these HCTD was followed by the identification of mutations in a wide range of genes encoding structural proteins, modifying enzymes, or components of the TGFβ-signaling pathway. Three typical examples of HCTD are Marfan syndrome (MFS), Ehlers-Danlos syndrome (EDS), and Loeys-Dietz syndrome (LDS). These syndromes show some degree of phenotypical overlap of cardiovascular, skeletal, and cutaneous features. MFS is typically characterized by cardiovascular, ocular, and skeletal manifestations and is caused by heterozygous mutations in FBN1, coding for the extracellular matrix (ECM) protein fibrillin-1. The most common cardiovascular phenotype involves aortic aneurysm and dissection at the sinuses of Valsalva. LDS is caused by mutations in TGBR1/2, SMAD2/3, or TGFB2/3, all coding for components of the TGFβ-signaling pathway. LDS can be distinguished from MFS by the unique presence of hypertelorism, bifid uvula or cleft palate, and widespread aortic and arterial aneurysm and tortuosity. Compared to MFS, LDS cardiovascular manifestations tend to be more severe. In contrast, no association is reported between LDS and the presence of ectopia lentis, a key distinguishing feature of MFS. Overlapping features between MFS and LDS include scoliosis, pes planus, anterior chest deformity, spontaneous pneumothorax, and dural ectasia. EDS refers to a group of clinically and genetically heterogeneous connective tissue disorders and all subtypes are characterized by variable abnormalities of skin, ligaments and joints, blood vessels, and internal organs. Typical presenting features include joint hypermobility, skin hyperextensibility, and tissue fragility. Up to one quarter of the EDS patients show aortic aneurysmal disease. The latest EDS nosology distinguishes 13 subtypes. Many phenotypic features show overlap between the different subtypes, which makes the clinical diagnosis rather difficult and highlights the importance of molecular diagnostic confirmation. PMID:29270370

A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data.

PubMed

Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel

2017-01-01

Finding a good predictive model for a high-dimensional data set can be challenging. For genetic data, it is not only important to find a model with high predictive accuracy, but it is also important that this model uses only few features and that the selection of these features is stable. This is because, in bioinformatics, the models are used not only for prediction but also for drawing biological conclusions which makes the interpretability and reliability of the model crucial. We suggest using three target criteria when fitting a predictive model to a high-dimensional data set: the classification accuracy, the stability of the feature selection, and the number of chosen features. As it is unclear which measure is best for evaluating the stability, we first compare a variety of stability measures. We conclude that the Pearson correlation has the best theoretical and empirical properties. Also, we find that for the stability assessment behaviour it is most important that a measure contains a correction for chance or large numbers of chosen features. Then, we analyse Pareto fronts and conclude that it is possible to find models with a stable selection of few features without losing much predictive accuracy.

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

PubMed

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.

Housing Stakeholder Preferences for the "Soft" Features of Sustainable and Healthy Housing Design in the UK.

PubMed

Prochorskaite, Agne; Couch, Chris; Malys, Naglis; Maliene, Vida

2016-01-07

It is widely recognised that the quantity and sustainability of new homes in the UK need to increase. However, it is important that sustainable housing is regarded holistically, and not merely in environmental terms, and incorporates elements that enhance the quality of life, health and well-being of its users. This paper focuses on the "soft" features of sustainable housing, that is, the non-technological components of sustainable housing and neighbourhood design that can impact occupants' health and well-being. Aims of the study are to ascertain the relative level of importance that key housing stakeholders attach to these features and to investigate whether the opinions of housing users and housing providers are aligned with regards to their importance. An online survey was carried out to gauge the level of importance that the key stakeholders, such as housing users, local authorities, housing associations, and developers (n = 235), attach to these features. Results revealed that while suitable indoor space was the feature regarded as most important by all stakeholders, there were also a number of disparities in opinion between housing users and housing providers (and among the different types of providers). This implies a scope for initiatives to achieve a better alignment between housing users and providers.

Housing Stakeholder Preferences for the “Soft” Features of Sustainable and Healthy Housing Design in the UK

PubMed Central

Prochorskaite, Agne; Couch, Chris; Malys, Naglis; Maliene, Vida

2016-01-01

It is widely recognised that the quantity and sustainability of new homes in the UK need to increase. However, it is important that sustainable housing is regarded holistically, and not merely in environmental terms, and incorporates elements that enhance the quality of life, health and well-being of its users. This paper focuses on the “soft” features of sustainable housing, that is, the non-technological components of sustainable housing and neighbourhood design that can impact occupants’ health and well-being. Aims of the study are to ascertain the relative level of importance that key housing stakeholders attach to these features and to investigate whether the opinions of housing users and housing providers are aligned with regards to their importance. An online survey was carried out to gauge the level of importance that the key stakeholders, such as housing users, local authorities, housing associations, and developers (n = 235), attach to these features. Results revealed that while suitable indoor space was the feature regarded as most important by all stakeholders, there were also a number of disparities in opinion between housing users and housing providers (and among the different types of providers). This implies a scope for initiatives to achieve a better alignment between housing users and providers. PMID:26751465

Piezo Proteins: Regulators of Mechanosensation and Other Cellular Processes*

PubMed Central

Bagriantsev, Sviatoslav N.; Gracheva, Elena O.; Gallagher, Patrick G.

2014-01-01

Piezo proteins have recently been identified as ion channels mediating mechanosensory transduction in mammalian cells. Characterization of these channels has yielded important insights into mechanisms of somatosensation, as well as other mechano-associated biologic processes such as sensing of shear stress, particularly in the vasculature, and regulation of urine flow and bladder distention. Other roles for Piezo proteins have emerged, some unexpected, including participation in cellular development, volume regulation, cellular migration, proliferation, and elongation. Mutations in human Piezo proteins have been associated with a variety of disorders including hereditary xerocytosis and several syndromes with muscular contracture as a prominent feature. PMID:25305018

Heart Failure: From Research to Clinical Practice.

PubMed

Islam, Md Shahidul

2018-01-01

"Heart failure: from research to clinical practice", a collection of selected reviews, which comes out also as a book, covers essentially all important aspects of heart failure, including the pathogenesis, clinical features, biomarkers, imaging techniques, medical treatment and surgical treatments, use of pacemakers and implantable cardioverter defibrillators, and palliative care. The reviews include essential background information, state of the art, critical and in-depth analysis, and directions for future researches for elucidation of the unresolved issues. Everyone interested in heart failure is expected to find this compilation helpful for a deeper understanding of some of the complex issues.

Combined multibeam and bathymetry data from Rhode Island Sound and Block Island Sound: a regional perspective

USGS Publications Warehouse

Poppe, Lawrence J.; McMullen, Katherine Y.; Danforth, William W.; Blankenship, Mark R.; Clos, Andrew R.; Glomb, Kimberly A.; Lewit, Peter G.; Nadeau, Megan A.; Wood, Douglas A.; Parker, Castleton E.

2014-01-01

Detailed bathymetric maps of the sea floor in Rhode Island and Block Island Sounds are of great interest to the New York, Rhode Island, and Massachusetts research and management communities because of this area's ecological, recreational, and commercial importance. Geologically interpreted digital terrain models from individual surveys provide important benthic environmental information, yet many applications of this information require a geographically broader perspective. For example, individual surveys are of limited use for the planning and construction of cross-sound infrastructure, such as cables and pipelines, or for the testing of regional circulation models. To address this need, we integrated 14 contiguous multibeam bathymetric datasets that were produced by the National Oceanic and Atmospheric Administration during charting operations into one digital terrain model that covers much of Block Island Sound and extends eastward across Rhode Island Sound. The new dataset, which covers over 1244 square kilometers, is adjusted to mean lower low water, gridded to 4-meter resolution, and provided in Universal Transverse Mercator Zone 19, North American Datum of 1983 and geographic World Geodetic Survey of 1984 projections. This resolution is adequate for sea-floor feature and process interpretation but is small enough to be queried and manipulated with standard Geographic Information System programs and to allow for future growth. Natural features visible in the data include boulder lag deposits of winnowed Pleistocene strata, sand-wave fields, and scour depressions that reflect the strength of oscillating tidal currents and scour by storm-induced waves. Bedform asymmetry allows interpretations of net sediment transport. Anthropogenic features visible in the data include shipwrecks and dredged channels. Together the merged data reveal a larger, more continuous perspective of bathymetric topography than previously available, providing a fundamental framework for research and resource management activities offshore of Rhode Island.

Characterization of the basal angiosperm Aristolochia fimbriata: a potential experimental system for genetic studies

PubMed Central

2013-01-01

Background Previous studies in basal angiosperms have provided insight into the diversity within the angiosperm lineage and helped to polarize analyses of flowering plant evolution. However, there is still not an experimental system for genetic studies among basal angiosperms to facilitate comparative studies and functional investigation. It would be desirable to identify a basal angiosperm experimental system that possesses many of the features found in existing plant model systems (e.g., Arabidopsis and Oryza). Results We have considered all basal angiosperm families for general characteristics important for experimental systems, including availability to the scientific community, growth habit, and membership in a large basal angiosperm group that displays a wide spectrum of phenotypic diversity. Most basal angiosperms are woody or aquatic, thus are not well-suited for large scale cultivation, and were excluded. We further investigated members of Aristolochiaceae for ease of culture, life cycle, genome size, and chromosome number. We demonstrated self-compatibility for Aristolochia elegans and A. fimbriata, and transformation with a GFP reporter construct for Saruma henryi and A. fimbriata. Furthermore, A. fimbriata was easily cultivated with a life cycle of just three months, could be regenerated in a tissue culture system, and had one of the smallest genomes among basal angiosperms. An extensive multi-tissue EST dataset was produced for A. fimbriata that includes over 3.8 million 454 sequence reads. Conclusions Aristolochia fimbriata has numerous features that facilitate genetic studies and is suggested as a potential model system for use with a wide variety of technologies. Emerging genetic and genomic tools for A. fimbriata and closely related species can aid the investigation of floral biology, developmental genetics, biochemical pathways important in plant-insect interactions as well as human health, and various other features present in early angiosperms. PMID:23347749

The role of angiogenic factors in fibroid pathogenesis: potential implications for future therapy

PubMed Central

Tal, Reshef; Segars, James H.

2014-01-01

Background It is well established that tumors are dependent on angiogenesis for their growth and survival. Although uterine fibroids are known to be benign tumors with reduced vascularization, recent work demonstrates that the vasculature of fibroids is grossly and microscopically abnormal. Accumulating evidence suggests that angiogenic growth factor dysregulation may be implicated in these vascular and other features of fibroid pathophysiology. Methods Literature searches were performed in PubMed and Google Scholar for articles with content related to angiogenic growth factors and myometrium/leiomyoma. The findings are hereby reviewed and discussed. Results Multiple growth factors involved in angiogenesis are differentially expressed in leiomyoma compared with myometrium. These include epidermal growth factor (EGF), heparin-binding-EGF, vascular endothelial growth factor, basic fibroblast growth factor, platelet-derived growth factor, transforming growth factor-β and adrenomedullin. An important paradox is that although leiomyoma tissues are hypoxic, leiomyoma feature down-regulation of key molecular regulators of the hypoxia response. Furthermore, the hypoxic milieu of leiomyoma may contribute to fibroid development and growth. Notably, common treatments for fibroids such as GnRH agonists and uterine artery embolization (UAE) are shown to work at least partly via anti-angiogenic mechanisms. Conclusions Angiogenic growth factors play an important role in mechanisms of fibroid pathophysiology, including abnormal vasculature and fibroid growth and survival. Moreover, the fibroid's abnormal vasculature together with its aberrant hypoxic and angiogenic response may make it especially vulnerable to disruption of its vascular supply, a feature which could be exploited for treatment. Further experimental studies are required in order to gain a better understanding of the growth factors that are involved in normal and pathological myometrial angiogenesis, and to assess the potential of anti-angiogenic treatment strategies for uterine fibroids. PMID:24077979

The hip adductor muscle group in caviomorph rodents: anatomy and homology.

PubMed

García-Esponda, César M; Candela, Adriana M

2015-06-01

Anatomical comparative studies including myological data of caviomorph rodents are relatively scarce, leading to a lack of use of muscular features in cladistic and morphofunctional analyses. In rodents, the hip adductor muscles constitute an important group of the hindlimb musculature, having an important function during the beginning of the stance phase. These muscles are subdivided in several distinct ways in the different clades of rodents, making the identification of their homologies hard to establish. In this contribution we provide a detailed description of the anatomical variation of the hip adductor muscle group of different genera of caviomorph rodents and identify the homologies of these muscles in the context of Rodentia. On this basis, we identify the characteristic pattern of the hip adductor muscles in Caviomorpha. Our results indicate that caviomorphs present a singular pattern of the hip adductor musculature that distinguishes them from other groups of rodents. They are characterized by having a single m. adductor brevis that includes solely its genicular part. This muscle, together with the m. gracilis, composes a muscular sheet that is medial to all other muscles of the hip adductor group. Both muscles probably have a synergistic action during locomotion, where the m. adductor brevis reinforces the multiple functions of the m. gracilis in caviomorphs. Mapping of analyzed myological characters in the context of Rodentia indicates that several features are recovered as potential synapomorphies of caviomorphs. Thus, analysis of the myological data described here adds to the current knowledge of caviomorph rodents from anatomical and functional points of view, indicating that this group has features that clearly differentiate them from other rodents. Copyright © 2015 Elsevier GmbH. All rights reserved.

Titan solar occultation observations reveal transit spectra of a hazy world

PubMed Central

Robinson, Tyler D.; Maltagliati, Luca; Marley, Mark S.; Fortney, Jonathan J.

2014-01-01

High-altitude clouds and hazes are integral to understanding exoplanet observations, and are proposed to explain observed featureless transit spectra. However, it is difficult to make inferences from these data because of the need to disentangle effects of gas absorption from haze extinction. Here, we turn to the quintessential hazy world, Titan, to clarify how high-altitude hazes influence transit spectra. We use solar occultation observations of Titan’s atmosphere from the Visual and Infrared Mapping Spectrometer aboard National Aeronautics and Space Administration’s (NASA) Cassini spacecraft to generate transit spectra. Data span 0.88–5 μm at a resolution of 12–18 nm, with uncertainties typically smaller than 1%. Our approach exploits symmetry between occultations and transits, producing transit radius spectra that inherently include the effects of haze multiple scattering, refraction, and gas absorption. We use a simple model of haze extinction to explore how Titan’s haze affects its transit spectrum. Our spectra show strong methane-absorption features, and weaker features due to other gases. Most importantly, the data demonstrate that high-altitude hazes can severely limit the atmospheric depths probed by transit spectra, bounding observations to pressures smaller than 0.1–10 mbar, depending on wavelength. Unlike the usual assumption made when modeling and interpreting transit observations of potentially hazy worlds, the slope set by haze in our spectra is not flat, and creates a variation in transit height whose magnitude is comparable to those from the strongest gaseous-absorption features. These findings have important consequences for interpreting future exoplanet observations, including those from NASA’s James Webb Space Telescope. PMID:24876272

How important is vehicle safety in the new vehicle purchase process?

PubMed

Koppel, Sjaanie; Charlton, Judith; Fildes, Brian; Fitzharris, Michael

2008-05-01

Whilst there has been a significant increase in the amount of consumer interest in the safety performance of privately owned vehicles, the role that it plays in consumers' purchase decisions is poorly understood. The aims of the current study were to determine: how important vehicle safety is in the new vehicle purchase process; what importance consumers place on safety options/features relative to other convenience and comfort features, and how consumers conceptualise vehicle safety. In addition, the study aimed to investigate the key parameters associated with ranking 'vehicle safety' as the most important consideration in the new vehicle purchase. Participants recruited in Sweden and Spain completed a questionnaire about their new vehicle purchase. The findings from the questionnaire indicated that participants ranked safety-related factors (e.g., EuroNCAP (or other) safety ratings) as more important in the new vehicle purchase process than other vehicle factors (e.g., price, reliability etc.). Similarly, participants ranked safety-related features (e.g., advanced braking systems, front passenger airbags etc.) as more important than non-safety-related features (e.g., route navigation systems, air-conditioning etc.). Consistent with previous research, most participants equated vehicle safety with the presence of specific vehicle safety features or technologies rather than vehicle crash safety/test results or crashworthiness. The key parameters associated with ranking 'vehicle safety' as the most important consideration in the new vehicle purchase were: use of EuroNCAP, gender and education level, age, drivers' concern about crash involvement, first vehicle purchase, annual driving distance, person for whom the vehicle was purchased, and traffic infringement history. The findings from this study are important for policy makers, manufacturers and other stakeholders to assist in setting priorities with regard to the promotion and publicity of vehicle safety features for particular consumer groups (such as younger consumers) in order to increase their knowledge regarding vehicle safety and to encourage them to place highest priority on safety in the new vehicle purchase process.

Opportunities and barriers to establishing a cell therapy programme in South Africa

PubMed Central

2013-01-01

The establishment of a cell therapy programme in South Africa has the potential to contribute to the alleviation of the country’s high disease burden and also to contribute to economic growth. South Africa has various positive attributes that favour the establishment of such a high-profile venture; however, there are also significant obstacles which need to be overcome. We discuss the positive and negative features of the current health biotechnology sector. The positive factors include a strong market pull and a highly innovative scientific and medical community, while the most problematic features include the lack of human resources and education and limited funding. The South African Government has undertaken to strengthen the biotechnology sector in general, but a focus on cell therapy is lacking. The next important step would be to provide financial, legal/ethical and other support for groups that are active and productive in this field through the development of a local cell therapy programme. PMID:23719318

The role of organizational culture and leadership in water safety plan implementation for improved risk management.

PubMed

Summerill, Corinna; Pollard, Simon J T; Smith, Jennifer A

2010-09-15

Appropriate implementation of WSPs offers an important opportunity to engage in and promote preventative risk management within water utilities. To ensure success, the whole organization, especially executive management, need to be advocates. Illustrated by two case studies, we discuss the influence of organizational culture on buy-in and commitment to public health protection and WSPs. Despite an internal desire to undertake risk management, some aspects of organizational culture prevented these from reaching full potential. Enabling cultural features included: camaraderie; competition; proactive, involved leaders; community focus; customer service mentality; transparency; accountability; competent workforce; empowerment; appreciation of successes, and a continual improvement culture. Blocking features included: poor communication; inflexibility; complacency; lack of awareness, interest or reward and coercion. We urge water utilities to consider the influence of organizational culture on the success and sustainability of WSP adoption, and better understand how effective leadership can mould culture to support implementation. Copyright 2010 Elsevier B.V. All rights reserved.

An interactive GIS based tool on Chinese history and its topography

NASA Astrophysics Data System (ADS)

Konda, Ashish Reddy

The aim of the thesis is to demonstrate how China was attacked by the foreign powers, the rise and fall of the empires, the border conflicts with India, Russia, Vietnam and territorial disputes in South China Sea. This thesis is focused on creating a GIS tool showcasing the modern Chinese history, which includes the major wars fought during that period. This tool is developed using the features of Google Maps that shows the location of the wars. The topography of China is also represented on the interactive Google Map by creating layers for rivers, mountain ranges and deserts. The provinces with highest population are also represented on the Google Map with circles. The application also shows the historical events in chronological order using a timeline feature. This has been implemented using JQuery, JavaScript, HTML5 and CSS. Chinese culture and biographies of important leaders are also included in this thesis, which is embedded with pictures and videos.

Imaging trace element distributions in single organelles and subcellular features

DOE PAGES

Kashiv, Yoav; Austin, Jotham R.; Lai, Barry; ...

2016-02-25

The distributions of chemical elements within cells are of prime importance in a wide range of basic and applied biochemical research. An example is the role of the subcellular Zn distribution in Zn homeostasis in insulin producing pancreatic beta cells and the development of type 2 diabetes mellitus. We combined transmission electron microscopy with micro-and nano-synchrotron X-ray fluorescence to image unequivocally for the first time, to the best of our knowledge, the natural elemental distributions, including those of trace elements, in single organelles and other subcellular features. Detected elements include Cl, K, Ca, Co, Ni, Cu, Zn and Cd (whichmore » some cells were supplemented with). Cell samples were prepared by a technique that minimally affects the natural elemental concentrations and distributions, and without using fluorescent indicators. In conclusion, it could likely be applied to all cell types and provide new biochemical insights at the single organelle level not available from organelle population level studies.« less

Genetic Forms of Epilepsies and other Paroxysmal Disorders

PubMed Central

Olson, Heather E.; Poduri, Annapurna; Pearl, Phillip L.

2016-01-01

Genetic mechanisms explain the pathophysiology of many forms of epilepsy and other paroxysmal disorders such as alternating hemiplegia of childhood, familial hemiplegic migraine, and paroxysmal dyskinesias. Epilepsy is a key feature of well-defined genetic syndromes including Tuberous Sclerosis Complex, Rett syndrome, Angelman syndrome, and others. There is an increasing number of singe gene causes or susceptibility factors associated with several epilepsy syndromes, including the early onset epileptic encephalopathies, benign neonatal/infantile seizures, progressive myoclonus epilepsies, genetic generalized and benign focal epilepsies, epileptic aphasias, and familial focal epilepsies. Molecular mechanisms are diverse, and a single gene can be associated with a broad range of phenotypes. Additional features, such as dysmorphisms, head size, movement disorders, and family history may provide clues to a genetic diagnosis. Genetic testing can impact medical care and counseling. We discuss genetic mechanisms of epilepsy and other paroxysmal disorders, tools and indications for genetic testing, known genotype-phenotype associations, the importance of genetic counseling, and a look towards the future of epilepsy genetics. PMID:25192505

Clinical and genetic features of the patients with X-Linked agammaglobulinemia from Turkey: Single-centre experience.

PubMed

Esenboga, S; Cagdas, D; Ozgur, T T; Gur Cetinkaya, P; Turkdemir, L M; Sanal, O; VanDerBurg, M; Tezcan, I

2018-03-01

X-linked agammaglobulinemia is a primary immunodeficiency disorder resulting from BTK gene mutations. There are many studies in the literature suggesting contradictory ideas about phenotype-genotype correlation. The aim of this study was to identify the mutations and clinical findings of patients with XLA in Turkey, to determine long-term complications related to the disease and to analyse the phenotype-genotype correlation. Thirty-two patients with XLA diagnosed between 1985 and 2016 in Pediatric Immunology Department of Hacettepe University Ihsan Dogramaci Children's Hospital were investigated. A clinical survey including clinical features of the patients was completed, and thirty-two patients from 26 different families were included in the study. Getting early diagnosis and regular assessment with imaging techniques seem to be the most important issues for improving the health status of the patients with XLA. Early molecular analysis gives chance for definitive diagnosis and genetic counselling, but not for predicting the clinical severity and prognosis. © 2018 The Foundation for the Scandinavian Journal of Immunology.

Article and process for producing an article

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

Lacy, Benjamin Paul; Jacala, Ariel Caesar Prepena; Kottilingam, Srikanth Chandrudu

An article and a process of producing an article are provided. The article includes a base material, a cooling feature arrangement positioned on the base material, the cooling feature arrangement including an additive-structured material, and a cover material. The cooling feature arrangement is between the base material and the cover material. The process of producing the article includes manufacturing a cooling feature arrangement by an additive manufacturing technique, and then positioning the cooling feature arrangement between a base material and a cover material.

A post-Galileo view of Io's interior

USGS Publications Warehouse

Keszthelyi, L.; Jaeger, W.L.; Turtle, E.P.; Milazzo, M.; Radebaugh, J.

2004-01-01

We present a self-consistent model for the interior of Io, taking the recent Galileo data into account. In this model, Io has a completely molten core, substantially molten mantle, and a very cold lithosphere. Heat from magmatic activity can mobilize volatile compounds such as SO2 in the lithosphere, and the movement of such cryogenic fluids may be important in the formation of surface features including sapping scarps and paterae. ?? Published by Elsevier Inc.

Yamato 980459: Crystallization of Martian Magnesian Magma

NASA Technical Reports Server (NTRS)

Koizumi, E.; Mikouchi, T.; McKay, G.; Monkawa, A.; Chokai, J.; Miyamoto, M.

2004-01-01

Recently, several basaltic shergottites have been found that include magnesian olivines as a major minerals. These have been called olivinephyric shergottites. Yamato 980459, which is a new martian meteorite recovered from the Antarctica by the Japanese Antarctic expedition, is one of them. This meteorite is different from other olivine-phyric shergottites in several key features and will give us important clues to understand crystallization of martian meteorites and the evolution of Martian magma.

Information Center Help Desk

DTIC Science & Technology

1991-09-01

listed is made. Many factors beyond what is included in the short list of features go into making that decision. Data on optical disk drives, scanners and...support existed due to lack of hardware or software. To do this analysis the IC responses were studied in relationship to the following three issues...each based on the developed criteria. No weighting factors in terms of relative importance of each criteria can be applied in this environment. As

Association between mammogram density and background parenchymal enhancement of breast MRI

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Danala, Gopichandh; Wang, Yunzhi; Zarafshani, Ali; Qian, Wei; Liu, Hong; Zheng, Bin

2018-02-01

Breast density has been widely considered as an important risk factor for breast cancer. The purpose of this study is to examine the association between mammogram density results and background parenchymal enhancement (BPE) of breast MRI. A dataset involving breast MR images was acquired from 65 high-risk women. Based on mammography density (BIRADS) results, the dataset was divided into two groups of low and high breast density cases. The Low-Density group has 15 cases with mammographic density (BIRADS 1 and 2), while the High-density group includes 50 cases, which were rated by radiologists as mammographic density BIRADS 3 and 4. A computer-aided detection (CAD) scheme was applied to segment and register breast regions depicted on sequential images of breast MRI scans. CAD scheme computed 20 global BPE features from the entire two breast regions, separately from the left and right breast region, as well as from the bilateral difference between left and right breast regions. An image feature selection method namely, CFS method, was applied to remove the most redundant features and select optimal features from the initial feature pool. Then, a logistic regression classifier was built using the optimal features to predict the mammogram density from the BPE features. Using a leave-one-case-out validation method, the classifier yields the accuracy of 82% and area under ROC curve, AUC=0.81+/-0.09. Also, the box-plot based analysis shows a negative association between mammogram density results and BPE features in the MRI images. This study demonstrated a negative association between mammogram density and BPE of breast MRI images.

Orientation Modeling for Amateur Cameras by Matching Image Line Features and Building Vector Data

NASA Astrophysics Data System (ADS)

Hung, C. H.; Chang, W. C.; Chen, L. C.

2016-06-01

With the popularity of geospatial applications, database updating is getting important due to the environmental changes over time. Imagery provides a lower cost and efficient way to update the database. Three dimensional objects can be measured by space intersection using conjugate image points and orientation parameters of cameras. However, precise orientation parameters of light amateur cameras are not always available due to their costliness and heaviness of precision GPS and IMU. To automatize data updating, the correspondence of object vector data and image may be built to improve the accuracy of direct georeferencing. This study contains four major parts, (1) back-projection of object vector data, (2) extraction of image feature lines, (3) object-image feature line matching, and (4) line-based orientation modeling. In order to construct the correspondence of features between an image and a building model, the building vector features were back-projected onto the image using the initial camera orientation from GPS and IMU. Image line features were extracted from the imagery. Afterwards, the matching procedure was done by assessing the similarity between the extracted image features and the back-projected ones. Then, the fourth part utilized line features in orientation modeling. The line-based orientation modeling was performed by the integration of line parametric equations into collinearity condition equations. The experiment data included images with 0.06 m resolution acquired by Canon EOS Mark 5D II camera on a Microdrones MD4-1000 UAV. Experimental results indicate that 2.1 pixel accuracy may be reached, which is equivalent to 0.12 m in the object space.

Uncoupling of Secretion From Growth in Some Hormone Secretory Tissues

PubMed Central

2014-01-01

Context: Most syndromes with benign primary excess of a hormone show positive coupling of hormone secretion to size or proliferation in the affected hormone secretory tissue. Syndromes that lack this coupling seem rare and have not been examined for unifying features among each other. Evidence Acquisition: Selected clinical and basic features were analyzed from original reports and reviews. We examined indices of excess secretion of a hormone and indices of size of secretory tissue within the following three syndromes, each suggestive of uncoupling between these two indices: familial hypocalciuric hypercalcemia, congenital diazoxide-resistant hyperinsulinism, and congenital primary hyperaldosteronism type III (with G151E mutation of the KCNJ5 gene). Evidence Synthesis: Some unifying features among the three syndromes were different from features present among common tumors secreting the same hormone. The unifying and distinguishing features included: 1) expression of hormone excess as early as the first days of life; 2) normal size of tissue that oversecretes a hormone; 3) diffuse histologic expression in the hormonal tissue; 4) resistance to treatment by subtotal ablation of the hormone-secreting tissue; 5) causation by a germline mutation; 6) low potential of the same mutation to cause a tumor by somatic mutation; and 7) expression of the mutated molecule in a pathway between sensing of a serum metabolite and secretion of hormone regulating that metabolite. Conclusion: Some shared clinical and basic features of uncoupling of secretion from size in a hormonal tissue characterize three uncommon states of hormone excess. These features differ importantly from features of common hormonal neoplasm of that tissue. PMID:25004249

Robust Feature Selection Technique using Rank Aggregation.

PubMed

Sarkar, Chandrima; Cooley, Sarah; Srivastava, Jaideep

2014-01-01

Although feature selection is a well-developed research area, there is an ongoing need to develop methods to make classifiers more efficient. One important challenge is the lack of a universal feature selection technique which produces similar outcomes with all types of classifiers. This is because all feature selection techniques have individual statistical biases while classifiers exploit different statistical properties of data for evaluation. In numerous situations this can put researchers into dilemma as to which feature selection method and a classifiers to choose from a vast range of choices. In this paper, we propose a technique that aggregates the consensus properties of various feature selection methods to develop a more optimal solution. The ensemble nature of our technique makes it more robust across various classifiers. In other words, it is stable towards achieving similar and ideally higher classification accuracy across a wide variety of classifiers. We quantify this concept of robustness with a measure known as the Robustness Index (RI). We perform an extensive empirical evaluation of our technique on eight data sets with different dimensions including Arrythmia, Lung Cancer, Madelon, mfeat-fourier, internet-ads, Leukemia-3c and Embryonal Tumor and a real world data set namely Acute Myeloid Leukemia (AML). We demonstrate not only that our algorithm is more robust, but also that compared to other techniques our algorithm improves the classification accuracy by approximately 3-4% (in data set with less than 500 features) and by more than 5% (in data set with more than 500 features), across a wide range of classifiers.

Plantar keloids: diagnostic and therapeutic issues in six patients.

PubMed

Vanhaecke, C; Hickman, G; Cavelier-Balloy, B; Masson, V; Duron, J-B; Gorj, M; May, P; Schneider, P; Vilmer, C; Bagot, M; Battistella, M; Petit, A

2015-07-01

Keloids are benign fibro-proliferative skin lesions that very rarely occur on the soles. Because of their rarity, the diagnosis of plantar keloids can be difficult. We describe the clinical and histopathological characteristics of eight plantar keloids. All patients presenting with plantar keloids between 2005 and 2012 in our Dermatology unit were retrospectively included. Diagnosis was definitely established by re-reading of pathological slides in all cases. Clinical characteristics, histopathological features, treatments given and their results were collected. Six patients were included. Five patients had a single plantar keloid and one had three lesions. They all were of African descent. Only one patient remembered of a previous injury at the site of the keloid. Three patients presented with associated extra-plantar keloids. In four patients, the diagnosis of keloid was not initially suspected clinically or histologically. Re-reading of the clinical photographs showed that the eight plantar keloids shared common morphological features, leading to a distinctive clinical picture, defined by a hardened lesion of rounded or polycyclic shape, with a pink surface crossed by keratotic furrows and the presence of a hyperkeratotic rim. Concerning pathological features, typical hyalinized collagen can be missing and deep fibrosis should not rule out the diagnosis of keloid. Intralesional injection of triamcinolone acetonide and orthopaedic shoes were useful. All patients who had surgical excision presented recurrence. The knowledge of the clinical features of plantar keloids is helpful to the diagnosis. There is no well-established treatment, but supportive measures are important. © 2014 European Academy of Dermatology and Venereology.

A simulation framework for auditory discrimination experiments: Revealing the importance of across-frequency processing in speech perception.

PubMed

Schädler, Marc René; Warzybok, Anna; Ewert, Stephan D; Kollmeier, Birger

2016-05-01

A framework for simulating auditory discrimination experiments, based on an approach from Schädler, Warzybok, Hochmuth, and Kollmeier [(2015). Int. J. Audiol. 54, 100-107] which was originally designed to predict speech recognition thresholds, is extended to also predict psychoacoustic thresholds. The proposed framework is used to assess the suitability of different auditory-inspired feature sets for a range of auditory discrimination experiments that included psychoacoustic as well as speech recognition experiments in noise. The considered experiments were 2 kHz tone-in-broadband-noise simultaneous masking depending on the tone length, spectral masking with simultaneously presented tone signals and narrow-band noise maskers, and German Matrix sentence test reception threshold in stationary and modulated noise. The employed feature sets included spectro-temporal Gabor filter bank features, Mel-frequency cepstral coefficients, logarithmically scaled Mel-spectrograms, and the internal representation of the Perception Model from Dau, Kollmeier, and Kohlrausch [(1997). J. Acoust. Soc. Am. 102(5), 2892-2905]. The proposed framework was successfully employed to simulate all experiments with a common parameter set and obtain objective thresholds with less assumptions compared to traditional modeling approaches. Depending on the feature set, the simulated reference-free thresholds were found to agree with-and hence to predict-empirical data from the literature. Across-frequency processing was found to be crucial to accurately model the lower speech reception threshold in modulated noise conditions than in stationary noise conditions.

Mutations in epilepsy and intellectual disability genes in patients with features of Rett syndrome.

PubMed

Olson, Heather E; Tambunan, Dimira; LaCoursiere, Christopher; Goldenberg, Marti; Pinsky, Rebecca; Martin, Emilie; Ho, Eugenia; Khwaja, Omar; Kaufmann, Walter E; Poduri, Annapurna

2015-09-01

Rett syndrome and neurodevelopmental disorders with features overlapping this syndrome frequently remain unexplained in patients without clinically identified MECP2 mutations. We recruited a cohort of 11 patients with features of Rett syndrome and negative initial clinical testing for mutations in MECP2. We analyzed their phenotypes to determine whether patients met formal criteria for Rett syndrome, reviewed repeat clinical genetic testing, and performed exome sequencing of the probands. Using 2010 diagnostic criteria, three patients had classical Rett syndrome, including two for whom repeat MECP2 gene testing had identified mutations. In a patient with neonatal onset epilepsy with atypical Rett syndrome, we identified a frameshift deletion in STXBP1. Among seven patients with features of Rett syndrome not fulfilling formal diagnostic criteria, four had suspected pathogenic mutations, one each in MECP2, FOXG1, SCN8A, and IQSEC2. MECP2 mutations are highly correlated with classical Rett syndrome. Genes associated with atypical Rett syndrome, epilepsy, or intellectual disability should be considered in patients with features overlapping with Rett syndrome and negative MECP2 testing. While most of the identified mutations were apparently de novo, the SCN8A variant was inherited from an unaffected parent mosaic for the mutation, which is important to note for counseling regarding recurrence risks. © 2015 Wiley Periodicals, Inc.

Mutations in Epilepsy and Intellectual Disability Genes in Patients with Features of Rett Syndrome

PubMed Central

Olson, Heather E.; Tambunan, Dimira; LaCoursiere, Christopher; Goldenberg, Marti; Pinsky, Rebecca; Martin, Emilie; Ho, Eugenia; Khwaja, Omar; Kaufmann, Walter E.; Poduri, Annapurna

2017-01-01

Rett syndrome and neurodevelopmental disorders with features overlapping this syndrome frequently remain unexplained in patients without clinically identified MECP2 mutations. We recruited a cohort of 11 patients with features of Rett syndrome and negative initial clinical testing for mutations in MECP2. We analyzed their phenotypes to determine whether patients met formal criteria for Rett syndrome, reviewed repeat clinical genetic testing, and performed exome sequencing of the probands. Using 2010 diagnostic criteria, three patients had classical Rett syndrome, including two for whom repeat MECP2 gene testing had identified mutations. In a patient with neonatal onset epilepsy with atypical Rett syndrome, we identified a frameshift deletion in STXBP1. Among seven patients with features of Rett syndrome not fulfilling formal diagnostic criteria, four had suspected pathogenic mutations, one each in MECP2, FOXG1, SCN8A, and IQSEC2. MECP2 mutations are highly correlated with classical Rett syndrome. Genes associated with atypical Rett syndrome, epilepsy, or intellectual disability should be considered in patients with features overlapping with Rett syndrome and negative MECP2 testing. While most of the identified mutations were apparently de novo, the SCN8A variant was inherited from an unaffected parent mosaic for the mutation, which is important to note for counseling regarding recurrence risks. PMID:25914188

Variations in lithospheric thickness on Venus

NASA Technical Reports Server (NTRS)

Johnson, C. L.; Sandwell, David T.

1992-01-01

Recent analyses of Magellan data have indicated many regions exhibiting topograhic flexure. On Venus, flexure is associated predominantly with coronae and the chasmata with Aphrodite Terra. Modeling of these flexural signatures allows the elastic and mechanical thickness of the lithosphere to be estimated. In areas where the lithosphere is flexed beyond its elastic limit the saturation moment provides information on the strength of the lithosphere. Modeling of 12 flexural features on Venus has indicated lithospheric thicknesses comparable with terrestrial values. This has important implications for the venusian heat budget. Flexure of a thin elastic plate due simultaneously to a line load on a continuous plate and a bending moment applied to the end of a broken plate is considered. The mean radius and regional topographic gradient are also included in the model. Features with a large radius of curvature were selected so that a two-dimensional approximation could be used. Comparisons with an axisymmetric model were made for some features to check the validity of the two-dimensional assumption. The best-fit elastic thickness was found for each profile crossing a given flexural feature. In addition, the surface stress and bending moment at the first zero crossing of each profile were also calculated. Flexural amplitudes and elastic thicknesses obtained for 12 features vary significantly. Three examples of the model fitting procedures are discussed.

Designing human centered GeoVisualization application--the SanaViz--for telehealth users: a case study.

PubMed

Joshi, Ashish; de Araujo Novaes, Magdala; Machiavelli, Josiane; Iyengar, Sriram; Vogler, Robert; Johnson, Craig; Zhang, Jiajie; Hsu, Chiehwen E

2012-01-01

Public health data is typically organized by geospatial unit. GeoVisualization (GeoVis) allows users to see information visually on a map. Examine telehealth users' perceptions towards existing public health GeoVis applications and obtains users' feedback about features important for the design and development of Human Centered GeoVis application "the SanaViz". We employed a cross sectional study design using mixed methods approach for this pilot study. Twenty users involved with the NUTES telehealth center at Federal University of Pernambuco (UFPE), Recife, Brazil were enrolled. Open and closed ended questionnaires were used to gather data. We performed audio recording for the interviews. Information gathered included socio-demographics, prior spatial skills and perception towards use of GeoVis to evaluate telehealth services. Card sorting and sketching methods were employed. Univariate analysis was performed for the continuous and categorical variables. Qualitative analysis was performed for open ended questions. Existing Public Health GeoVis applications were difficult to use. Results found interaction features zooming, linking and brushing and representation features Google maps, tables and bar chart as most preferred GeoVis features. Early involvement of users is essential to identify features necessary to be part of the human centered GeoVis application "the SanaViz".

A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images.

PubMed

Leontidis, Georgios

2017-11-01

Human retina is a diverse and important tissue, vastly studied for various retinal and other diseases. Diabetic retinopathy (DR), a leading cause of blindness, is one of them. This work proposes a novel and complete framework for the accurate and robust extraction and analysis of a series of retinal vascular geometric features. It focuses on studying the registered bifurcations in successive years of progression from diabetes (no DR) to DR, in order to identify the vascular alterations. Retinal fundus images are utilised, and multiple experimental designs are employed. The framework includes various steps, such as image registration and segmentation, extraction of features, statistical analysis and classification models. Linear mixed models are utilised for making the statistical inferences, alongside the elastic-net logistic regression, boruta algorithm, and regularised random forests for the feature selection and classification phases, in order to evaluate the discriminative potential of the investigated features and also build classification models. A number of geometric features, such as the central retinal artery and vein equivalents, are found to differ significantly across the experiments and also have good discriminative potential. The classification systems yield promising results with the area under the curve values ranging from 0.821 to 0.968, across the four different investigated combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Free-Form Region Description with Second-Order Pooling.

PubMed

Carreira, João; Caseiro, Rui; Batista, Jorge; Sminchisescu, Cristian

2015-06-01

Semantic segmentation and object detection are nowadays dominated by methods operating on regions obtained as a result of a bottom-up grouping process (segmentation) but use feature extractors developed for recognition on fixed-form (e.g. rectangular) patches, with full images as a special case. This is most likely suboptimal. In this paper we focus on feature extraction and description over free-form regions and study the relationship with their fixed-form counterparts. Our main contributions are novel pooling techniques that capture the second-order statistics of local descriptors inside such free-form regions. We introduce second-order generalizations of average and max-pooling that together with appropriate non-linearities, derived from the mathematical structure of their embedding space, lead to state-of-the-art recognition performance in semantic segmentation experiments without any type of local feature coding. In contrast, we show that codebook-based local feature coding is more important when feature extraction is constrained to operate over regions that include both foreground and large portions of the background, as typical in image classification settings, whereas for high-accuracy localization setups, second-order pooling over free-form regions produces results superior to those of the winning systems in the contemporary semantic segmentation challenges, with models that are much faster in both training and testing.

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

PubMed Central

Manavalan, Balachandran; Shin, Tae H.; Lee, Gwang

2018-01-01

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html. PMID:29616000

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.

PubMed

Manavalan, Balachandran; Shin, Tae H; Lee, Gwang

2018-01-01

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation.

PubMed

Mourad, Raphaël; Cuvier, Olivier

2016-05-01

Recent advances in long-range Hi-C contact mapping have revealed the importance of the 3D structure of chromosomes in gene expression. A current challenge is to identify the key molecular drivers of this 3D structure. Several genomic features, such as architectural proteins and functional elements, were shown to be enriched at topological domain borders using classical enrichment tests. Here we propose multiple logistic regression to identify those genomic features that positively or negatively influence domain border establishment or maintenance. The model is flexible, and can account for statistical interactions among multiple genomic features. Using both simulated and real data, we show that our model outperforms enrichment test and non-parametric models, such as random forests, for the identification of genomic features that influence domain borders. Using Drosophila Hi-C data at a very high resolution of 1 kb, our model suggests that, among architectural proteins, BEAF-32 and CP190 are the main positive drivers of 3D domain borders. In humans, our model identifies well-known architectural proteins CTCF and cohesin, as well as ZNF143 and Polycomb group proteins as positive drivers of domain borders. The model also reveals the existence of several negative drivers that counteract the presence of domain borders including P300, RXRA, BCL11A and ELK1.

Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation

PubMed Central

Mourad, Raphaël; Cuvier, Olivier

2016-01-01

Recent advances in long-range Hi-C contact mapping have revealed the importance of the 3D structure of chromosomes in gene expression. A current challenge is to identify the key molecular drivers of this 3D structure. Several genomic features, such as architectural proteins and functional elements, were shown to be enriched at topological domain borders using classical enrichment tests. Here we propose multiple logistic regression to identify those genomic features that positively or negatively influence domain border establishment or maintenance. The model is flexible, and can account for statistical interactions among multiple genomic features. Using both simulated and real data, we show that our model outperforms enrichment test and non-parametric models, such as random forests, for the identification of genomic features that influence domain borders. Using Drosophila Hi-C data at a very high resolution of 1 kb, our model suggests that, among architectural proteins, BEAF-32 and CP190 are the main positive drivers of 3D domain borders. In humans, our model identifies well-known architectural proteins CTCF and cohesin, as well as ZNF143 and Polycomb group proteins as positive drivers of domain borders. The model also reveals the existence of several negative drivers that counteract the presence of domain borders including P300, RXRA, BCL11A and ELK1. PMID:27203237

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.

PubMed

Manavalan, Balachandran; Shin, Tae Hwan; Lee, Gwang

2018-01-05

DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html.

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

PubMed Central

Manavalan, Balachandran; Shin, Tae Hwan; Lee, Gwang

2018-01-01

DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html PMID:29416743

Premotor and non-motor features of Parkinson’s disease

PubMed Central

Goldman, Jennifer G.; Postuma, Ron

2014-01-01

Purpose of review This review highlights recent advances in premotor and non-motor features in Parkinson’s disease, focusing on these issues in the context of prodromal and early stage Parkinson’s disease. Recent findings While Parkinson’s disease patients experience a wide range of non-motor symptoms throughout the disease course, studies demonstrate that non-motor features are not solely a late manifestation. Indeed, disturbances of smell, sleep, mood, and gastrointestinal function may herald Parkinson’s disease or related synucleinopathies and precede these neurodegenerative conditions by 5 or more years. In addition, other non-motor symptoms such as cognitive impairment are now recognized in incident or de novo Parkinson’s disease cohorts. Many of these non-motor features reflect disturbances in non-dopaminergic systems and early involvement of peripheral and central nervous systems including olfactory, enteric, and brainstem neurons as in Braak’s proposed pathological staging of Parkinson’s disease. Current research focuses on identifying potential biomarkers that may detect persons at risk for Parkinson’s disease and permit early intervention with neuroprotective or disease-modifying therapeutics. Summary Recent studies provide new insights on the frequency, pathophysiology, and importance of non-motor features in Parkinson’s disease as well as the recognition that these non-motor symptoms occur in premotor, early, and later phases of Parkinson’s disease. PMID:24978368

Problem- and case-based learning in science: an introduction to distinctions, values, and outcomes.

PubMed

Allchin, Douglas

2013-01-01

Case-based learning and problem-based learning have demonstrated great promise in reforming science education. Yet an instructor, in newly considering this suite of interrelated pedagogical strategies, faces a number of important instructional choices. Different features and their related values and learning outcomes are profiled here, including: the level of student autonomy; instructional focus on content, skills development, or nature-of-science understanding; the role of history, or known outcomes; scope, clarity, and authenticity of problems provided to students; extent of collaboration; complexity, in terms of number of interpretive perspectives; and, perhaps most importantly, the role of applying versus generating knowledge.

Problem- and Case-Based Learning in Science: An Introduction to Distinctions, Values, and Outcomes

PubMed Central

Allchin, Douglas

2013-01-01

Case-based learning and problem-based learning have demonstrated great promise in reforming science education. Yet an instructor, in newly considering this suite of interrelated pedagogical strategies, faces a number of important instructional choices. Different features and their related values and learning outcomes are profiled here, including: the level of student autonomy; instructional focus on content, skills development, or nature-of-science understanding; the role of history, or known outcomes; scope, clarity, and authenticity of problems provided to students; extent of collaboration; complexity, in terms of number of interpretive perspectives; and, perhaps most importantly, the role of applying versus generating knowledge. PMID:24006385

Interpersonal Factors in Understanding and Treating Posttraumatic Stress Disorder

PubMed Central

Markowitz, John C.; Milrod, Barbara; Bleiberg, Kathryn; Marshall, Randall D.

2010-01-01

Exposure to reminders of trauma underlies the theory and practice of most treatments for posttraumatic stress disorder (PTSD), yet exposure may not be the sole important treatment mechanism. Interpersonal features of PTSD influence its onset, chronicity, and possibly its treatment. The authors review interpersonal factors in PTSD, including the critical but underrecognized role of social support as both protective posttrauma and as a mechanism of recovery. They discuss interpersonal psychotherapy (IPT) as an alternative treatment for PTSD and present encouraging findings from two initial studies. Highlighting the potential importance of attachment and interpersonal relationships, the authors propose a mechanism to explain why improving relationships may ameliorate PTSD symptoms. PMID:19339847

Metabolomics Investigation of an Association of Induced Features and Corresponding Fungus during the Co-culture of Trametes versicolor and Ganoderma applanatum

PubMed Central

Xu, Xiao-Yan; Shen, Xiao-Ting; Yuan, Xiao-Jie; Zhou, Yuan-Ming; Fan, Huan; Zhu, Li-Ping; Du, Feng-Yu; Sadilek, Martin; Yang, Jie; Qiao, Bin; Yang, Song

2018-01-01

The co-culture of Trametes versicolor and Ganoderma applanatum is a model of intense basidiomycete interaction, which induces many newly synthesized or highly produced features. Currently, one of the major challenges is an identification of the origin of induced features during the co-culture. Herein, we report a 13C-dynamic labeling analysis used to determine an association of induced features and corresponding fungus even if the identities of metabolites were not available or almost nothing was known of biochemical aspects. After the co-culture of T. versicolor and G. applanatum for 10 days, the mycelium pellets of T. versicolor and G. applanatum were sterilely harvested and then mono-cultured in the liquid medium containing half fresh medium with 13C-labeled glucose as carbon source and half co-cultured supernatants collected on day 10. 13C-labeled metabolome analyzed by LC-MS revealed that 31 induced features including 3-phenyllactic acid and orsellinic acid were isotopically labeled in the mono-culture after the co-culture stimulation. Twenty features were derived from T. versicolor, 6 from G. applanatum, and 5 features were synthesized by both T. versicolor and G. applanatum. 13C-labeling further suggested that 12 features such as previously identified novel xyloside [N-(4-methoxyphenyl)formamide 2-O-beta-D-xyloside] were likely induced through the direct physical interaction of mycelia. Use of molecular network analysis combined with 13C-labeling provided an insight into the link between the generation of structural analogs and producing fungus. Compound 1 with m/z 309.0757, increased 15.4-fold in the co-culture and observed 13C incorporation in the mono-culture of both T. versicolor and G. applanatum, was purified and identified as a phenyl polyketide, 2,5,6-trihydroxy-4, 6-diphenylcyclohex-4-ene-1,3-dione. The biological activity study indicated that this compound has a potential to inhibit cell viability of leukemic cell line U937. The current work sets an important basis for further investigations including novel metabolites discovery and biosynthetic capacity improvement. PMID:29375514

Metabolomics Investigation of an Association of Induced Features and Corresponding Fungus during the Co-culture of Trametes versicolor and Ganoderma applanatum.

PubMed

Xu, Xiao-Yan; Shen, Xiao-Ting; Yuan, Xiao-Jie; Zhou, Yuan-Ming; Fan, Huan; Zhu, Li-Ping; Du, Feng-Yu; Sadilek, Martin; Yang, Jie; Qiao, Bin; Yang, Song

2017-01-01

The co-culture of Trametes versicolor and Ganoderma applanatum is a model of intense basidiomycete interaction, which induces many newly synthesized or highly produced features. Currently, one of the major challenges is an identification of the origin of induced features during the co-culture. Herein, we report a 13 C-dynamic labeling analysis used to determine an association of induced features and corresponding fungus even if the identities of metabolites were not available or almost nothing was known of biochemical aspects. After the co-culture of T. versicolor and G. applanatum for 10 days, the mycelium pellets of T. versicolor and G. applanatum were sterilely harvested and then mono-cultured in the liquid medium containing half fresh medium with 13 C-labeled glucose as carbon source and half co-cultured supernatants collected on day 10. 13 C-labeled metabolome analyzed by LC-MS revealed that 31 induced features including 3-phenyllactic acid and orsellinic acid were isotopically labeled in the mono-culture after the co-culture stimulation. Twenty features were derived from T. versicolor , 6 from G. applanatum , and 5 features were synthesized by both T. versicolor and G. applanatum . 13 C-labeling further suggested that 12 features such as previously identified novel xyloside [N-(4-methoxyphenyl)formamide 2-O-beta-D-xyloside] were likely induced through the direct physical interaction of mycelia. Use of molecular network analysis combined with 13 C-labeling provided an insight into the link between the generation of structural analogs and producing fungus. Compound 1 with m/z 309.0757, increased 15.4-fold in the co-culture and observed 13 C incorporation in the mono-culture of both T. versicolor and G. applanatum , was purified and identified as a phenyl polyketide, 2,5,6-trihydroxy-4, 6-diphenylcyclohex-4-ene-1,3-dione. The biological activity study indicated that this compound has a potential to inhibit cell viability of leukemic cell line U937. The current work sets an important basis for further investigations including novel metabolites discovery and biosynthetic capacity improvement.

Emotional recognition from the speech signal for a virtual education agent

NASA Astrophysics Data System (ADS)

Tickle, A.; Raghu, S.; Elshaw, M.

2013-06-01

This paper explores the extraction of features from the speech wave to perform intelligent emotion recognition. A feature extract tool (openSmile) was used to obtain a baseline set of 998 acoustic features from a set of emotional speech recordings from a microphone. The initial features were reduced to the most important ones so recognition of emotions using a supervised neural network could be performed. Given that the future use of virtual education agents lies with making the agents more interactive, developing agents with the capability to recognise and adapt to the emotional state of humans is an important step.

Spectral characteristics and feature selection of satellite remote sensing data for climate and anthropogenic changes assessment in Bucharest area

NASA Astrophysics Data System (ADS)

Zoran, Maria; Savastru, Roxana; Savastru, Dan; Tautan, Marina; Miclos, Sorin; Cristescu, Luminita; Carstea, Elfrida; Baschir, Laurentiu

2010-05-01

Urban systems play a vital role in social and economic development in all countries. Their environmental changes can be investigated on different spatial and temporal scales. Urban and peri-urban environment dynamics is of great interest for future planning and decision making as well as in frame of local and regional changes. Changes in urban land cover include changes in biotic diversity, actual and potential primary productivity, soil quality, runoff, and sedimentation rates, and cannot be well understood without the knowledge of land use change that drives them. The study focuses on the assessment of environmental features changes for Bucharest metropolitan area, Romania by satellite remote sensing and in-situ monitoring data. Rational feature selection from the varieties of spectral channels in the optical wavelengths of electromagnetic spectrum (VIS and NIR) is very important for effective analysis and information extraction of remote sensing data. Based on comprehensively analyses of the spectral characteristics of remote sensing data is possibly to derive environmental changes in urban areas. The information quantity contained in a band is an important parameter in evaluating the band. The deviation and entropy are often used to show information amount. Feature selection is one of the most important steps in recognition and classification of remote sensing images. Therefore, it is necessary to select features before classification. The optimal features are those that can be used to distinguish objects easily and correctly. Three factors—the information quantity of bands, the correlation between bands and the spectral characteristic (e.g. absorption specialty) of classified objects in test area Bucharest have been considered in our study. As, the spectral characteristic of an object is influenced by many factors, being difficult to define optimal feature parameters to distinguish all the objects in a whole area, a method of multi-level feature selection was suggested. On the basis of analyzing the information quantity of bands, correlation between different bands, spectral absorption characteristics of objects and object separability in bands, a fundamental method of optimum band selection and feature extraction from remote sensing data was discussed. Spectral signatures of different terrain features have been used to extract structural patterns aiming to separate surface units and to classify the general categories. The synergetic analysis and interpretation of the different satellite images (LANDSAT: TM, ETM; MODIS, IKONOS) acquired over a period of more than 20 years reveals significant aspects regarding impacts of climate and anthropogenic changes on urban/periurban environment. It was delimited residential zones of industrial zones which are very often a source of pollution. An important role has urban green cover assessment. Have been emphasized the particularities of the functional zones from different points of view: architectural, streets and urban surface traffic, some components of urban infrastructure as well as habitat quality. The growth of Bucharest urban area in Romania has been a result of a rapid process of industrialization, and also of the increase of urban population. Information on the spatial pattern and temporal dynamics of land cover and land use of urban areas is critical to address a wide range of practical problems relating to urban regeneration, urban sustainability and rational planning policy.

The features of self-assembling organic bilayers important to the formation of anisotropic inorganic materials in microgravity conditions

NASA Technical Reports Server (NTRS)

Talham, Daniel R.; Adair, James H.

2005-01-01

Materials with directional properties are opening new horizons in a variety of applications including chemistry, electronics, and optics. Structural, optical, and electrical properties can be greatly augmented by the fabrication of composite materials with anisotropic microstructures or with anisotropic particles uniformly dispersed in an isotropic matrix. Examples include structural composites, magnetic and optical recording media, photographic film, certain metal and ceramic alloys, and display technologies including flat panel displays. The new applications and the need for model particles in scientific investigations are rapidly out-distancing the ability to synthesize anisotropic particles with specific chemistries and narrowly distributed physical characteristics (e.g. size distribution, shape, and aspect ratio).

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

PubMed Central

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. PMID:24667482

Linking Spectral Features with Composition, Crystallinity, and Roughness Properties of Silica and Implications for Candidate Hydrothermal Systems on Mars

NASA Astrophysics Data System (ADS)

Hamilton, V. E.; McDowell, M. L.; Berger, J. A.; Cady, S. L.; Knauth, L. P.

2011-12-01

We have collected visible to near infrared reflectance (VNIR, ~0.4 - 2.5 um), thermal infrared emissivity (TIR, ~5 - 45 um), SEM, XRD, surface roughness, and petrographic data for 18 silica samples. These rocks (e.g., replacement chert, geyserite, opal-A/-CT) represent a variety of geologic formation environments, including hydrothermal, and have XRD-determined crystallinities ranging from <1 to >10 according to the quartz crystallinity index. Our findings are relevant to the interpretation of orbital and in situ spectral observations of crystalline or amorphous silica on the Martian surface, some of which may have formed in hydrothermal systems. Almost all of our samples' VNIR spectra contain discernible bands. The most common features are related to hydration (H2O and/or OH) of silica (e.g., at ~1.4, 1.9, and 2.2 um). The visibility and strength of these bands is not always constant between spectra from different areas of a sample. Other features include those of carbonate, phyllosilicate, and iron oxide impurities. All of our amorphous silica samples have hydration features in the VNIR, but we note that the absorptions around ~2.2 um can be very weak in amorphous samples relative to features at other wavelengths and relative to ~2.2-um features observed in Martian data, suggesting that some amorphous silica on Mars could go undetected. Deposits containing significant anhydrous, crystalline silica (chert) may be assumed to lack features in the VNIR, but many of our cherts have spectral features and could be misidentified as materials dominated by what is a minor contaminant. Thermal infrared spectra of chert and opaline silica differ from each other as a result of the loss of long-range Si-O order in increasingly amorphous samples. Our samples display a clear trend in TIR band shapes where features attributable to crystalline quartz and amorphous silica are blended in samples with intermediate crystallinities. Most diagnostic TIR spectral features observable in laboratory data typically are recognizable in hyperspectral remote sensing data. These features are more difficult to distinguish (or are not included) at multispectral resolutions, but in nearly all uncontaminated samples, the positions of Si-O emissivity minima shift towards longer wavelengths with decreasing crystallinity. Contaminating phases with strong VNIR spectral features are observed in some of the TIR spectra but have a negligible effect in others, suggesting that TIR spectroscopy helps constrain the abundances of these phases. In addition to compositional and crystallinity information, our laboratory data demonstrate that TIR spectra can be used to deduce important information on silica phases' texture and orientation. If used in combination, VNIR and TIR spectroscopy can detect and characterize silica phases, allowing us to estimate conditions of silica formation, e.g., high- or low-temperature aqueous systems.

Evaluating, Comparing, and Interpreting Protein Domain Hierarchies

PubMed Central

2014-01-01

Abstract Arranging protein domain sequences hierarchically into evolutionarily divergent subgroups is important for investigating evolutionary history, for speeding up web-based similarity searches, for identifying sequence determinants of protein function, and for genome annotation. However, whether or not a particular hierarchy is optimal is often unclear, and independently constructed hierarchies for the same domain can often differ significantly. This article describes methods for statistically evaluating specific aspects of a hierarchy, for probing the criteria underlying its construction and for direct comparisons between hierarchies. Information theoretical notions are used to quantify the contributions of specific hierarchical features to the underlying statistical model. Such features include subhierarchies, sequence subgroups, individual sequences, and subgroup-associated signature patterns. Underlying properties are graphically displayed in plots of each specific feature's contributions, in heat maps of pattern residue conservation, in “contrast alignments,” and through cross-mapping of subgroups between hierarchies. Together, these approaches provide a deeper understanding of protein domain functional divergence, reveal uncertainties caused by inconsistent patterns of sequence conservation, and help resolve conflicts between competing hierarchies. PMID:24559108

Antifogging abilities of model nanotextures

NASA Astrophysics Data System (ADS)

Mouterde, Timothée; Lehoucq, Gaëlle; Xavier, Stéphane; Checco, Antonio; Black, Charles T.; Rahman, Atikur; Midavaine, Thierry; Clanet, Christophe; Quéré, David

2017-06-01

Nanometre-scale features with special shapes impart a broad spectrum of unique properties to the surface of insects. These properties are essential for the animal’s survival, and include the low light reflectance of moth eyes, the oil repellency of springtail carapaces and the ultra-adhesive nature of palmtree bugs. Antireflective mosquito eyes and cicada wings are also known to exhibit some antifogging and self-cleaning properties. In all cases, the combination of small feature size and optimal shape provides exceptional surface properties. In this work, we investigate the underlying antifogging mechanism in model materials designed to mimic natural systems, and explain the importance of the texture’s feature size and shape. While exposure to fog strongly compromises the water-repellency of hydrophobic structures, this failure can be minimized by scaling the texture down to nanosize. This undesired effect even becomes non-measurable if the hydrophobic surface consists of nanocones, which generate antifogging efficiency close to unity and water departure of droplets smaller than 2 μm.

Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction.

PubMed

Huang, Ying; Chen, Shi-Yi; Deng, Feilong

2016-01-01

In silico analysis of DNA sequences is an important area of computational biology in the post-genomic era. Over the past two decades, computational approaches for ab initio prediction of gene structure from genome sequence alone have largely facilitated our understanding on a variety of biological questions. Although the computational prediction of protein-coding genes has already been well-established, we are also facing challenges to robustly find the non-coding RNA genes, such as miRNA and lncRNA. Two main aspects of ab initio gene prediction include the computed values for describing sequence features and used algorithm for training the discriminant function, and by which different combinations are employed into various bioinformatic tools. Herein, we briefly review these well-characterized sequence features in eukaryote genomes and applications to ab initio gene prediction. The main purpose of this article is to provide an overview to beginners who aim to develop the related bioinformatic tools.

New Science in Plain Sight: Optical Manifestations of Coupled Subauroral Features Documented by Citizen Scientists

NASA Astrophysics Data System (ADS)

MacDonald, E.; Heavner, M.; Kosar, B.; Case, N.; Donovan, E.; Spanswick, E.; Nishimura, Y.; Gallardo-Lacourt, B.

2017-12-01

Aurora has been observed and recorded by people for thousands of years. Recently, citizen scientists captured features of aurora-like arc events not previously described in the literature at subauroral latitudes. Amateur photo sequences show unusual flow, unstable composition changes, and field aligned structures. Observations from the Swarm satellite crossing the arc reveals thermal enhancement, density depletion, and strong westward ion flow. These signatures resemble features previously described from in situ observation however the optical manifestation is surprising and contains rich, unstable signatures as well. The relevant observations have presented important implications on a variety of open questions, including the fundamental definition of aurora, and limitations of jargon and subfield distinctions. This paper covers the discovery, its context, and the significant implications for the application of public participation measurement modes to the natural sciences whereby they can form a disruptive gap to expose new observing perspectives. Photo Credit: Notanee Bourassa, Alberta Aurora Chasers

A bioinspired redox relay that mimics radical interactions of the Tyr-His pairs of photosystem II

NASA Astrophysics Data System (ADS)

Megiatto, Jackson D., Jr.; Méndez-Hernández, Dalvin D.; Tejeda-Ferrari, Marely E.; Teillout, Anne-Lucie; Llansola-Portolés, Manuel J.; Kodis, Gerdenis; Poluektov, Oleg G.; Rajh, Tijana; Mujica, Vladimiro; Groy, Thomas L.; Gust, Devens; Moore, Thomas A.; Moore, Ana L.

2014-05-01

In water-oxidizing photosynthetic organisms, light absorption generates a powerfully oxidizing chlorophyll complex (P680•+) in the photosystem II reaction centre. This is reduced via an electron transfer pathway from the manganese-containing water-oxidizing catalyst, which includes an electron transfer relay comprising a tyrosine (Tyr)-histidine (His) pair that features a hydrogen bond between a phenol group and an imidazole group. By rapidly reducing P680•+, the relay is thought to mitigate recombination reactions, thereby ensuring a high quantum yield of water oxidation. Here, we show that an artificial reaction centre that features a benzimidazole-phenol model of the Tyr-His pair mimics both the short-internal hydrogen bond in photosystem II and, using electron paramagnetic resonance spectroscopy, the thermal relaxation that accompanies proton-coupled electron transfer. Although this artificial system is much less complex than the natural one, theory suggests that it captures the essential features that are important in the function of the relay.

Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization

PubMed Central

Cruz-Roa, Angel; Díaz, Gloria; Romero, Eduardo; González, Fabio A.

2011-01-01

Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively. PMID:22811960

Wavelet images and Chou's pseudo amino acid composition for protein classification.

PubMed

Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

2012-08-01

The last decade has seen an explosion in the collection of protein data. To actualize the potential offered by this wealth of data, it is important to develop machine systems capable of classifying and extracting features from proteins. Reliable machine systems for protein classification offer many benefits, including the promise of finding novel drugs and vaccines. In developing our system, we analyze and compare several feature extraction methods used in protein classification that are based on the calculation of texture descriptors starting from a wavelet representation of the protein. We then feed these texture-based representations of the protein into an Adaboost ensemble of neural network or a support vector machine classifier. In addition, we perform experiments that combine our feature extraction methods with a standard method that is based on the Chou's pseudo amino acid composition. Using several datasets, we show that our best approach outperforms standard methods. The Matlab code of the proposed protein descriptors is available at http://bias.csr.unibo.it/nanni/wave.rar .

Moral Hard‐Wiring and Moral Enhancement

PubMed Central

Persson, Ingmar

2017-01-01

Abstract We have argued for an urgent need for moral bioenhancement; that human moral psychology is limited in its ability to address current existential threats due to the evolutionary function of morality to maximize cooperation in small groups. We address here Powell and Buchanan's novel objection that there is an ‘inclusivist anomaly’: humans have the capacity to care beyond in‐groups. They propose that ‘exclusivist’ (group‐based) morality is sensitive to environmental cues that historically indicated out‐group threat. When this is not present, we are inclusivist. They conclude that moral bioenhancement is unnecessary or less effective than socio‐cultural interventions. We argue that Powell and Buchanan underestimate the hard‐wiring features of moral psychology; their appeal to adaptively plastic, conditionally expressed responses accounts for only a fragment of our moral psychology. In addition to restrictions on our altruistic concern that their account addresses – such as racism and sexism – there are ones it is ill‐suited to address: that our concern is stronger for kin and friends and for concrete individuals rather than for statistical lives; also our bias towards the near future. Hard‐wired features of our moral psychology that are not clearly restrictions in altruistic concern also include reciprocity, tit‐for‐tat, and others. Biomedical means are not the only, and maybe not the most important, means of moral enhancement. Socio‐cultural means are of great importance and there are currently no biomedical interventions for many hard‐wired features. Nevertheless research is desirable because the influence of these features is greater than our critics think. PMID:28300281

Moral Hard-Wiring and Moral Enhancement.

PubMed

Persson, Ingmar; Savulescu, Julian

2017-05-01

We have argued for an urgent need for moral bioenhancement; that human moral psychology is limited in its ability to address current existential threats due to the evolutionary function of morality to maximize cooperation in small groups. We address here Powell and Buchanan's novel objection that there is an 'inclusivist anomaly': humans have the capacity to care beyond in-groups. They propose that 'exclusivist' (group-based) morality is sensitive to environmental cues that historically indicated out-group threat. When this is not present, we are inclusivist. They conclude that moral bioenhancement is unnecessary or less effective than socio-cultural interventions. We argue that Powell and Buchanan underestimate the hard-wiring features of moral psychology; their appeal to adaptively plastic, conditionally expressed responses accounts for only a fragment of our moral psychology. In addition to restrictions on our altruistic concern that their account addresses - such as racism and sexism - there are ones it is ill-suited to address: that our concern is stronger for kin and friends and for concrete individuals rather than for statistical lives; also our bias towards the near future. Hard-wired features of our moral psychology that are not clearly restrictions in altruistic concern also include reciprocity, tit-for-tat, and others. Biomedical means are not the only, and maybe not the most important, means of moral enhancement. Socio-cultural means are of great importance and there are currently no biomedical interventions for many hard-wired features. Nevertheless research is desirable because the influence of these features is greater than our critics think. © 2017 The Authors Bioethics Published by John Wiley & Sons Ltd.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

PubMed Central

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

PubMed

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.