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.

The development of organized visual search

PubMed Central

Woods, Adam J.; Goksun, Tilbe; Chatterjee, Anjan; Zelonis, Sarah; Mehta, Anika; Smith, Sabrina E.

2013-01-01

Visual search plays an important role in guiding behavior. Children have more difficulty performing conjunction search tasks than adults. The present research evaluates whether developmental differences in children's ability to organize serial visual search (i.e., search organization skills) contribute to performance limitations in a typical conjunction search task. We evaluated 134 children between the ages of 2 and 17 on separate tasks measuring search for targets defined by a conjunction of features or by distinct features. Our results demonstrated that children organize their visual search better as they get older. As children's skills at organizing visual search improve they become more accurate at locating targets with conjunction of features amongst distractors, but not for targets with distinct features. Developmental limitations in children's abilities to organize their visual search of the environment are an important component of poor conjunction search in young children. In addition, our findings provide preliminary evidence that, like other visuospatial tasks, exposure to reading may influence children's spatial orientation to the visual environment when performing a visual search. PMID:23584560

Chinese character recognition based on Gabor feature extraction and CNN

NASA Astrophysics Data System (ADS)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

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.

Contrasting effects of feature-based statistics on the categorisation and identification of visual objects

PubMed Central

Taylor, Kirsten I.; Devereux, Barry J.; Acres, Kadia; Randall, Billi; Tyler, Lorraine K.

2013-01-01

Conceptual representations are at the heart of our mental lives, involved in every aspect of cognitive functioning. Despite their centrality, a long-standing debate persists as to how the meanings of concepts are represented and processed. Many accounts agree that the meanings of concrete concepts are represented by their individual features, but disagree about the importance of different feature-based variables: some views stress the importance of the information carried by distinctive features in conceptual processing, others the features which are shared over many concepts, and still others the extent to which features co-occur. We suggest that previously disparate theoretical positions and experimental findings can be unified by an account which claims that task demands determine how concepts are processed in addition to the effects of feature distinctiveness and co-occurrence. We tested these predictions in a basic-level naming task which relies on distinctive feature information (Experiment 1) and a domain decision task which relies on shared feature information (Experiment 2). Both used large-scale regression designs with the same visual objects, and mixed-effects models incorporating participant, session, stimulus-related and feature statistic variables to model the performance. We found that concepts with relatively more distinctive and more highly correlated distinctive relative to shared features facilitated basic-level naming latencies, while concepts with relatively more shared and more highly correlated shared relative to distinctive features speeded domain decisions. These findings demonstrate that the feature statistics of distinctiveness (shared vs. distinctive) and correlational strength, as well as the task demands, determine how concept meaning is processed in the conceptual system. PMID:22137770

"Anti-Michael addition" of Grignard reagents to sulfonylacetylenes: synthesis of alkynes.

PubMed

Esteban, Francisco; Boughani, Lazhar; García Ruano, José L; Fraile, Alberto; Alemán, José

2017-05-10

In this work, the addition of Grignard reagents to arylsulfonylacetylenes, which undergoes an "anti-Michael addition", resulting in their alkynylation under very mild conditions is described. The simplicity of the experimental procedure and the functional group tolerance are the main features of this methodology. This is an important advantage over the use of organolithium at -78 °C that we previously reported. Moreover, the synthesis of diynes and other examples showing functional group tolerance in this anti-Michael reaction is also presented.

Learning to Detect Vandalism in Social Content Systems: A Study on Wikipedia

NASA Astrophysics Data System (ADS)

Javanmardi, Sara; McDonald, David W.; Caruana, Rich; Forouzan, Sholeh; Lopes, Cristina V.

A challenge facing user generated content systems is vandalism, i.e. edits that damage content quality. The high visibility and easy access to social networks makes them popular targets for vandals. Detecting and removing vandalism is critical for these user generated content systems. Because vandalism can take many forms, there are many different kinds of features that are potentially useful for detecting it. The complex nature of vandalism, and the large number of potential features, make vandalism detection difficult and time consuming for human editors. Machine learning techniques hold promise for developing accurate, tunable, and maintainable models that can be incorporated into vandalism detection tools. We describe a method for training classifiers for vandalism detection that yields classifiers that are more accurate on the PAN 2010 corpus than others previously developed. Because of the high turnaround in social network systems, it is important for vandalism detection tools to run in real-time. To this aim, we use feature selection to find the minimal set of features consistent with high accuracy. In addition, because some features are more costly to compute than others, we use cost-sensitive feature selection to reduce the total computational cost of executing our models. In addition to the features previously used for spam detection, we introduce new features based on user action histories. The user history features contribute significantly to classifier performance. The approach we use is general and can easily be applied to other user generated content systems.

Deep-learning derived features for lung nodule classification with limited datasets

NASA Astrophysics Data System (ADS)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path

PubMed Central

Jeon, Semi; Yoon, Inhye; Jang, Jinbeum; Yang, Seungji; Kim, Jisung; Paik, Joonki

2017-01-01

Acquisition of stabilized video is an important issue for various type of digital cameras. This paper presents an adaptive camera path estimation method using robust feature detection to remove shaky artifacts in a video. The proposed algorithm consists of three steps: (i) robust feature detection using particle keypoints between adjacent frames; (ii) camera path estimation and smoothing; and (iii) rendering to reconstruct a stabilized video. As a result, the proposed algorithm can estimate the optimal homography by redefining important feature points in the flat region using particle keypoints. In addition, stabilized frames with less holes can be generated from the optimal, adaptive camera path that minimizes a temporal total variation (TV). The proposed video stabilization method is suitable for enhancing the visual quality for various portable cameras and can be applied to robot vision, driving assistant systems, and visual surveillance systems. PMID:28208622

Classification of radiolarian images with hand-crafted and deep features

NASA Astrophysics Data System (ADS)

Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

2017-12-01

Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

Pharmacophore mapping of arylbenzothiophene derivatives for MCF cell inhibition using classical and 3D space modeling approaches.

PubMed

Mukherjee, Subhendu; Nagar, Shuchi; Mullick, Sanchita; Mukherjee, Arup; Saha, Achintya

2008-01-01

Considering the worth of developing non-steroidal estrogen analogs, the present study explores the pharmacophore features of arylbenzothiophene derivatives for inhibitory activity to MCF-7 cells using classical QSAR and 3D space modeling approaches. The analysis shows that presence of phenolic hydroxyl group and ketonic linkage in the basic side chain of 2-arylbenzothiophene core of raloxifene derivatives are crucial. Additionally piperidine ring connected through ether linkage is favorable for inhibition of breast cancer cell line. These features for inhibitory activity are also highlighted through 3D space modeling approach that explored importance of critical inter features distance among HB-acceptor lipid, hydrophobic and HB-donor features in the arylbenzothiophene scaffold for activity.

Qualitative evaluation of pretreatment patient concerns in orthodontics.

PubMed

Twigge, Eugene; Roberts, Rachel M; Jamieson, Lisa; Dreyer, Craig W; Sampson, Wayne J

2016-07-01

A discrepancy exists between objective and subjective measures of orthodontic treatment need, highlighting the importance of patients' perceptions. Limited qualitative information is available regarding patients' perceptions and orthodontic concerns. For the first time, patient facial images and qualitative methodology were used to assess patients' orthodontic concerns, which are incorporated into and are important in treatment planning and consent. An interview-based, cross-sectional study of adolescent patients eligible to receive orthodontic treatment in a public dental hospital was conducted with 105 adolescents (42 boys, 63 girls) aged between 12 and 17 years. Each patient's face was video recorded, and 3 images were selected from each recording to assess the patient's orthodontic concerns. The initial chief concerns were compared with concerns articulated after the patients assessed their facial images. In addition, patient concerns were compared with occlusal features visible on smiling using the Dental Aesthetic Index and patient study casts. For 37% of the adolescent patients, smiling images helped to identify additional concerns. For 87%, their smiling images helped them to describe their concerns in more detail. In addition, a few patients did not articulate any concern about features measurable on the Dental Aesthetic Index that were visible on smiling. Showing adolescent patients images of their face and smile helped them to identify and better describe their concerns. Adolescents are not always overly concerned about visible and quantifiable malocclusion features. This might influence orthodontic treatment planning and consent. Copyright © 2016 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Outcomes of Diagnostic Exome Sequencing in Patients With Diagnosed or Suspected Autism Spectrum Disorders.

PubMed

Rossi, Mari; El-Khechen, Dima; Black, Mary Helen; Farwell Hagman, Kelly D; Tang, Sha; Powis, Zöe

2017-05-01

Exome sequencing has recently been proved to be a successful diagnostic method for complex neurodevelopmental disorders. However, the diagnostic yield of exome sequencing for autism spectrum disorders has not been extensively evaluated in large cohorts to date. We performed diagnostic exome sequencing in a cohort of 163 individuals with autism spectrum disorder (66.3%) or autistic features (33.7%). The diagnostic yield observed in patients in our cohort was 25.8% (42 of 163) for positive or likely positive findings in characterized disease genes, while a candidate genetic etiology was reported for an additional 3.3% (4 of 120) of patients. Among the positive findings in the patients with autism spectrum disorder or autistic features, 61.9% were the result of de novo mutations. Patients presenting with psychiatric conditions or ataxia or paraplegia in addition to autism spectrum disorder or autistic features were significantly more likely to receive positive results compared with patients without these clinical features (95.6% vs 27.1%, P < 0.0001; 83.3% vs 21.2%, P < 0.0001, respectively). The majority of the positive findings were in recently identified autism spectrum disorder genes, supporting the importance of diagnostic exome sequencing for patients with autism spectrum disorder or autistic features as the causative genes might evade traditional sequential or panel testing. These results suggest that diagnostic exome sequencing would be an efficient primary diagnostic method for patients with autism spectrum disorders or autistic features. Moreover, our data may aid clinicians to better determine which subset of patients with autism spectrum disorder with additional clinical features would benefit the most from diagnostic exome sequencing. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Contrasting effects of feature-based statistics on the categorisation and basic-level identification of visual objects.

PubMed

Taylor, Kirsten I; Devereux, Barry J; Acres, Kadia; Randall, Billi; Tyler, Lorraine K

2012-03-01

Conceptual representations are at the heart of our mental lives, involved in every aspect of cognitive functioning. Despite their centrality, a long-standing debate persists as to how the meanings of concepts are represented and processed. Many accounts agree that the meanings of concrete concepts are represented by their individual features, but disagree about the importance of different feature-based variables: some views stress the importance of the information carried by distinctive features in conceptual processing, others the features which are shared over many concepts, and still others the extent to which features co-occur. We suggest that previously disparate theoretical positions and experimental findings can be unified by an account which claims that task demands determine how concepts are processed in addition to the effects of feature distinctiveness and co-occurrence. We tested these predictions in a basic-level naming task which relies on distinctive feature information (Experiment 1) and a domain decision task which relies on shared feature information (Experiment 2). Both used large-scale regression designs with the same visual objects, and mixed-effects models incorporating participant, session, stimulus-related and feature statistic variables to model the performance. We found that concepts with relatively more distinctive and more highly correlated distinctive relative to shared features facilitated basic-level naming latencies, while concepts with relatively more shared and more highly correlated shared relative to distinctive features speeded domain decisions. These findings demonstrate that the feature statistics of distinctiveness (shared vs. distinctive) and correlational strength, as well as the task demands, determine how concept meaning is processed in the conceptual system. Copyright © 2011 Elsevier B.V. All rights reserved.

Sockeye: A 3D Environment for Comparative Genomics

PubMed Central

Montgomery, Stephen B.; Astakhova, Tamara; Bilenky, Mikhail; Birney, Ewan; Fu, Tony; Hassel, Maik; Melsopp, Craig; Rak, Marcin; Robertson, A. Gordon; Sleumer, Monica; Siddiqui, Asim S.; Jones, Steven J.M.

2004-01-01

Comparative genomics techniques are used in bioinformatics analyses to identify the structural and functional properties of DNA sequences. As the amount of available sequence data steadily increases, the ability to perform large-scale comparative analyses has become increasingly relevant. In addition, the growing complexity of genomic feature annotation means that new approaches to genomic visualization need to be explored. We have developed a Java-based application called Sockeye that uses three-dimensional (3D) graphics technology to facilitate the visualization of annotation and conservation across multiple sequences. This software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization. PMID:15123592

A closer look at prion strains: characterization and important implications.

PubMed

Solforosi, Laura; Milani, Michela; Mancini, Nicasio; Clementi, Massimo; Burioni, Roberto

2013-01-01

Prions are infectious proteins that are responsible for transmissible spongiform encephalopathies (TSEs) and consist primarily of scrapie prion protein (PrP (Sc) ), a pathogenic isoform of the host-encoded cellular prion protein (PrP (C) ). The absence of nucleic acids as essential components of the infectious prions is the most striking feature associated to these diseases. Additionally, different prion strains have been isolated from animal diseases despite the lack of DNA or RNA molecules. Mounting evidence suggests that prion-strain-specific features segregate with different PrP (Sc) conformational and aggregation states. Strains are of practical relevance in prion diseases as they can drastically differ in many aspects, such as incubation period, PrP (Sc) biochemical profile (e.g., electrophoretic mobility and glycoform ratio) and distribution of brain lesions. Importantly, such different features are maintained after inoculation of a prion strain into genetically identical hosts and are relatively stable across serial passages. This review focuses on the characterization of prion strains and on the wide range of important implications that the study of prion strains involves.

Tuning diagonal components of static linear and first nonlinear polarizabilities of doped quantum dots by Gaussian white noise

NASA Astrophysics Data System (ADS)

Ganguly, Jayanta; Ghosh, Manas

2015-07-01

We investigate the modulation of diagonal components of static linear (αxx, αyy) and first nonlinear (βxxx, βyyy) polarizabilities of quantum dots by Gaussian white noise. Quantum dot is doped with impurity represented by a Gaussian potential and repulsive in nature. The study reveals the importance of mode of application of noise (additive/multiplicative) on the polarizability components. The doped system is further exposed to a static external electric field of given intensity. As important observation we have found that the strength of additive noise becomes unable to influence the polarizability components. However, the multiplicative noise influences them conspicuously and gives rise to additional interesting features. Multiplicative noise even enhances the magnitude of the polarizability components immensely. The present investigation deems importance in view of the fact that noise seriously affects the optical properties of doped quantum dot devices.

Low-power coprocessor for Haar-like feature extraction with pixel-based pipelined architecture

NASA Astrophysics Data System (ADS)

Luo, Aiwen; An, Fengwei; Fujita, Yuki; Zhang, Xiangyu; Chen, Lei; Jürgen Mattausch, Hans

2017-04-01

Intelligent analysis of image and video data requires image-feature extraction as an important processing capability for machine-vision realization. A coprocessor with pixel-based pipeline (CFEPP) architecture is developed for real-time Haar-like cell-based feature extraction. Synchronization with the image sensor’s pixel frequency and immediate usage of each input pixel for the feature-construction process avoids the dependence on memory-intensive conventional strategies like integral-image construction or frame buffers. One 180 nm CMOS prototype can extract the 1680-dimensional Haar-like feature vectors, applied in the speeded up robust features (SURF) scheme, using an on-chip memory of only 96 kb (kilobit). Additionally, a low power dissipation of only 43.45 mW at 1.8 V supply voltage is achieved during VGA video procession at 120 MHz frequency with more than 325 fps. The Haar-like feature-extraction coprocessor is further evaluated by the practical application of vehicle recognition, achieving the expected high accuracy which is comparable to previous work.

Additive survival least square support vector machines: A simulation study and its application to cervical cancer prediction

NASA Astrophysics Data System (ADS)

Khotimah, Chusnul; Purnami, Santi Wulan; Prastyo, Dedy Dwi; Chosuvivatwong, Virasakdi; Sriplung, Hutcha

2017-11-01

Support Vector Machines (SVMs) has been widely applied for prediction in many fields. Recently, SVM is also developed for survival analysis. In this study, Additive Survival Least Square SVM (A-SURLSSVM) approach is used to analyze cervical cancer dataset and its performance is compared with the Cox model as a benchmark. The comparison is evaluated based on the prognostic index produced: concordance index (c-index), log rank, and hazard ratio. The higher prognostic index represents the better performance of the corresponding methods. This work also applied feature selection to choose important features using backward elimination technique based on the c-index criterion. The cervical cancer dataset consists of 172 patients. The empirical results show that nine out of the twelve features: age at marriage, age of first getting menstruation, age, parity, type of treatment, history of family planning, stadium, long-time of menstruation, and anemia status are selected as relevant features that affect the survival time of cervical cancer patients. In addition, the performance of the proposed method is evaluated through a simulation study with the different number of features and censoring percentages. Two out of three performance measures (c-index and hazard ratio) obtained from A-SURLSSVM consistently yield better results than the ones obtained from Cox model when it is applied on both simulated and cervical cancer data. Moreover, the simulation study showed that A-SURLSSVM performs better when the percentage of censoring data is small.

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

[Supernumerary chromosomes in the karyotype of the Siberian spruce, P. obovata].

PubMed

Muratova, E N; Vladimirova, O S

2001-01-01

Results of karyological study of ornamental forms of Picea obovata Ledeb. are presented. Typical chromosome number (2n) is 24, but some trees have one or two additional chromosomes (2n = 24 + 1B; 2n = 24 + 2B). Heritability of additional chromosomes, pollen fertility, morphological features of cones, and seed quality in trees with and without additional chromosomes were studied. System of B-chromosomes is of importance for population and species adaptation and possibly plays a role in adaptation of P. obovata under introduction.

Prosodic Features and Speech Naturalness in Individuals with Dysarthria

ERIC Educational Resources Information Center

Klopfenstein, Marie I.

2012-01-01

Despite the importance of speech naturalness to treatment outcomes, little research has been done on what constitutes speech naturalness and how to best maximize naturalness in relationship to other treatment goals like intelligibility. In addition, previous literature alludes to the relationship between prosodic aspects of speech and speech…

The Role of Attention in Item-Item Binding in Visual Working Memory

ERIC Educational Resources Information Center

Peterson, Dwight J.; Naveh-Benjamin, Moshe

2017-01-01

An important yet unresolved question regarding visual working memory (VWM) relates to whether or not binding processes within VWM require additional attentional resources compared with processing solely the individual components comprising these bindings. Previous findings indicate that binding of surface features (e.g., colored shapes) within VWM…

Borderline Personality Features, Anger, and Intimate Partner Violence: An Experimental Manipulation of Rejection.

PubMed

Armenti, Nicholas A; Babcock, Julia C

2018-04-01

Individuals with borderline personality features may be susceptible to react to situational stressors with negative and interpersonally maladaptive emotionality (e.g., anger) and aggression. The current study attempted to test two moderated mediation models to investigate dispositional risk factors associated with borderline personality features and intimate partner violence (IPV). Results from an experimental rejection induction paradigm were examined using moderated regression to observe contextual reactions to imagined romantic rejection from a current romantic partner among individuals with borderline personality features. An ethnically diverse sample of 218 undergraduates at a large public university in the southwestern United States was recruited. Participants responded to demographic questions and self-report measures, and engaged in an experimental rejection induction paradigm. Borderline personality features was positively associated with rejection sensitivity, physical assault, and psychological aggression. Contrary to initial hypotheses, rejection sensitivity did not serve as a mediator of the relations between borderline personality features and physical assault and psychological aggression. However, trait anger mediated the relation between borderline personality features and psychological aggression. As such, trait anger may be an important explanatory variable in the relation between borderline personality features and psychological aggression specifically. Results of the rejection induction paradigm indicated that, for individuals who were asked to imagine an ambiguous rejection, the relation between borderline personality features and state anger post-rejection was strengthened. For individuals who imagined a critical rejection, there was no significant relation between borderline personality features and state anger post-rejection. Findings suggest that trait anger may be an important dispositional factor in the link between borderline personality features and IPV. In addition, contextual factors, such as ambiguous rejection by an intimate partner, may be especially relevant in activating anger or aggression in individuals with borderline personality features.

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

PubMed Central

2016-01-01

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. PMID:27806075

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.

PubMed

Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V; Neil, Greg J; Black, Sue

2016-01-01

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.

PubMed

Qu, Kaiyang; Han, Ke; Wu, Song; Wang, Guohua; Wei, Leyi

2017-09-22

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

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.

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

PubMed

Hartzler, Andrea Lisabeth; BlueSpruce, June; Catz, Sheryl L; McClure, Jennifer B

2016-08-05

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. The aim of this study was to understand smokers' preferred design features of mHealth quit-smoking tools. 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. 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. 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.

Self-Assembling Block Copolymer Resist Mixtures towards Lithographic Resists for Sub-10 nm Features

NASA Astrophysics Data System (ADS)

Chandler, Curran; Daga, Vikram; Watkins, James

2009-03-01

Significant improvements in 193 nm photolithography have enabled the extension of device feature sizes beyond the 45 nm and 32 nm nodes, yet uncertainty lies beyond 22 nm features as no single replacement has emerged. Here we show that low molecular weight, nonionic block copolymer surfactant blends are capable of self-assembling into highly ordered domains with feature sizes on the order of 5 nm. These surfactants, most of which lack the required χN for microphase separation on their own, exhibit strong segregation and long-range order upon addition of a component capable of multi-point hydrogen bonding that is specific for one of the blocks in the copolymer. This has been demonstrated by our SAXS data for several Pluronic (PEO-b-PPO-b-PEO) and Brij (PEO-b-[CH2]nCH3) surfactants of various molecular weights and PEO volume fractions. Furthermore, we employ these highly-ordered systems as thin film, nanolithographic etch masks for the transfer of sub-10 nm patterns into silicon-based substrates. Small molecule, hydrogen bonding additives containing aromatic or silsesquioxane structure are also used to tune etch contrast between the blocks which is important for reducing line edge roughness (LER) of such small features.

Evolving Patient Compliance Trends: Integrating Clinical, Insurance, and Extrapolated Socioeconomic Data

PubMed Central

Klobusicky, Joseph J.; Aryasomayajula, Arun; Marko, Nicholas

2015-01-01

Efforts toward improving patient compliance in medication focus on either identifying trends in patient features or studying changes through an intervention. Our study seeks to provide an important link between these two approaches through defining trends of evolving compliance. In addition to using clinical covariates provided through insurance claims and health records, we also extracted census based data to provide socioeconomic covariates such as income and population density. Through creating quadrants based on periods of medicine intake, we derive several novel definitions of compliance. These definitions revealed additional compliance trends through considering refill histories later in a patient’s length of therapy. These results suggested that the link between patient features and compliance includes a temporal component, and should be considered in policymaking when identifying compliant subgroups. PMID:26958212

Clinical features of the myasthenic syndrome arising from mutations in GMPPB.

PubMed

Rodríguez Cruz, Pedro M; Belaya, Katsiaryna; Basiri, Keivan; Sedghi, Maryam; Farrugia, Maria Elena; Holton, Janice L; Liu, Wei Wei; Maxwell, Susan; Petty, Richard; Walls, Timothy J; Kennett, Robin; Pitt, Matthew; Sarkozy, Anna; Parton, Matt; Lochmüller, Hanns; Muntoni, Francesco; Palace, Jacqueline; Beeson, David

2016-08-01

Congenital myasthenic syndrome (CMS) due to mutations in GMPPB has recently been reported confirming the importance of glycosylation for the integrity of neuromuscular transmission. Review of case notes of patients with mutations in GMPPB to identify the associated clinical, neurophysiological, pathological and laboratory features. In addition, serum creatine kinase (CK) levels within the Oxford CMS cohort were retrospectively analysed to assess its usefulness in the differential diagnosis of this new entity. All patients had prominent limb-girdle weakness with minimal or absent craniobulbar manifestations. Presentation was delayed beyond infancy with proximal muscle weakness and most patients recall poor performance in sports during childhood. Neurophysiology showed abnormal neuromuscular transmission only in the affected muscles and myopathic changes. Muscle biopsy showed dystrophic features and reduced α-dystroglycan glycosylation. In addition, myopathic changes were present on muscle MRI. CK was significantly increased in serum compared to other CMS subtypes. Patients were responsive to pyridostigimine alone or combined with 3,4-diaminopyridine and/or salbutamol. Patients with GMPPB-CMS have phenotypic features aligned with CMS subtypes harbouring mutations within the early stages of the glycosylation pathway. Additional features shared with the dystroglycanopathies include myopathic features, raised CK levels and variable mild cognitive delay. This syndrome underlines that CMS can occur in the absence of classic myasthenic manifestations such as ptosis and ophthalmoplegia or facial weakness, and links myasthenic disorders with dystroglycanopathies. This report should facilitate the recognition of this disorder, which is likely to be underdiagnosed and can benefit from symptomatic treatment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Ensemble methods with simple features for document zone classification

NASA Astrophysics Data System (ADS)

Obafemi-Ajayi, Tayo; Agam, Gady; Xie, Bingqing

2012-01-01

Document layout analysis is of fundamental importance for document image understanding and information retrieval. It requires the identification of blocks extracted from a document image via features extraction and block classification. In this paper, we focus on the classification of the extracted blocks into five classes: text (machine printed), handwriting, graphics, images, and noise. We propose a new set of features for efficient classifications of these blocks. We present a comparative evaluation of three ensemble based classification algorithms (boosting, bagging, and combined model trees) in addition to other known learning algorithms. Experimental results are demonstrated for a set of 36503 zones extracted from 416 document images which were randomly selected from the tobacco legacy document collection. The results obtained verify the robustness and effectiveness of the proposed set of features in comparison to the commonly used Ocropus recognition features. When used in conjunction with the Ocropus feature set, we further improve the performance of the block classification system to obtain a classification accuracy of 99.21%.

Real estate value prediction using multivariate regression models

NASA Astrophysics Data System (ADS)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

A closer look at prion strains

PubMed Central

Solforosi, Laura; Milani, Michela; Mancini, Nicasio; Clementi, Massimo; Burioni, Roberto

2013-01-01

Prions are infectious proteins that are responsible for transmissible spongiform encephalopathies (TSEs) and consist primarily of scrapie prion protein (PrPSc), a pathogenic isoform of the host-encoded cellular prion protein (PrPC). The absence of nucleic acids as essential components of the infectious prions is the most striking feature associated to these diseases. Additionally, different prion strains have been isolated from animal diseases despite the lack of DNA or RNA molecules. Mounting evidence suggests that prion-strain-specific features segregate with different PrPSc conformational and aggregation states. Strains are of practical relevance in prion diseases as they can drastically differ in many aspects, such as incubation period, PrPSc biochemical profile (e.g., electrophoretic mobility and glycoform ratio) and distribution of brain lesions. Importantly, such different features are maintained after inoculation of a prion strain into genetically identical hosts and are relatively stable across serial passages. This review focuses on the characterization of prion strains and on the wide range of important implications that the study of prion strains involves. PMID:23357828

Teaching Undergraduate Software Engineering Using Open Source Development Tools

DTIC Science & Technology

2012-01-01

ware. Some example appliances are: a LAMP stack, Redmine, MySQL database, Moodle, Tom- cat on Apache, and Bugzilla. Some of the important features...Ada, C, C++, PHP , Py- thon, etc., and also supports a wide range of SDKs such as Google’s Android SDK and the Google Web Toolkit SDK. Additionally

"It's a Hurricane! It's a Hurricane!": Can Music Facilitate Social Constructive and Sociodramatic Play in a Preschool Classroom?

ERIC Educational Resources Information Center

Love, Angela; Burns, M. Susan

2006-01-01

Sustaining attention and successfully engaging with others in collaborative play are important accomplishments focused on in preschool classrooms and childcarecenters. In addition, music is frequently used in early childhood classrooms, and even recommended as an environmental feature to motivate and regulate children's behavior. Although pretend…

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.

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.

Alignment and bit extraction for secure fingerprint biometrics

NASA Astrophysics Data System (ADS)

Nagar, A.; Rane, S.; Vetro, A.

2010-01-01

Security of biometric templates stored in a system is important because a stolen template can compromise system security as well as user privacy. Therefore, a number of secure biometrics schemes have been proposed that facilitate matching of feature templates without the need for a stored biometric sample. However, most of these schemes suffer from poor matching performance owing to the difficulty of designing biometric features that remain robust over repeated biometric measurements. This paper describes a scheme to extract binary features from fingerprints using minutia points and fingerprint ridges. The features are amenable to direct matching based on binary Hamming distance, but are especially suitable for use in secure biometric cryptosystems that use standard error correcting codes. Given all binary features, a method for retaining only the most discriminable features is presented which improves the Genuine Accept Rate (GAR) from 82% to 90% at a False Accept Rate (FAR) of 0.1% on a well-known public database. Additionally, incorporating singular points such as a core or delta feature is shown to improve the matching tradeoff.

Gene/protein name recognition based on support vector machine using dictionary as features.

PubMed

Mitsumori, Tomohiro; Fation, Sevrani; Murata, Masaki; Doi, Kouichi; Doi, Hirohumi

2005-01-01

Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

Landscape Features Shape Genetic Structure in Threatened Northern Spotted Owls

USGS Publications Warehouse

Funk, W. Chris; Forsman, Eric D.; Mullins, Thomas D.; Haig, Susan M.

2008-01-01

Several recent studies have shown that landscape features can strongly affect spatial patterns of gene flow and genetic variation. Understanding landscape effects on genetic variation is important in conservation for defining management units and understanding movement patterns. The landscape may have little effect on gene flow, however, in highly mobile species such as birds. We tested for genetic breaks associated with landscape features in the northern spotted owl (Strix occidentalis caurina), a threatened subspecies associated with old forests in the U.S. Pacific Northwest and extreme southwestern Canada. We found little evidence for distinct genetic breaks in northern spotted owls using a large microsatellite dataset (352 individuals from across the subspecies' range genotyped at 10 loci). Nonetheless, dry low-elevation valleys and the Cascade and Olympic Mountains restrict gene flow, while the Oregon Coast Range facilitates it. The wide Columbia River is not a barrier to gene flow. In addition, inter-individual genetic distance and latitude were negatively related, likely reflecting northward colonization following Pleistocene glacial recession. Our study shows that landscape features may play an important role in shaping patterns of genetic variation in highly vagile taxa such as birds.

Assessing clutter reduction in parallel coordinates using image processing techniques

NASA Astrophysics Data System (ADS)

Alhamaydh, Heba; Alzoubi, Hussein; Almasaeid, Hisham

2018-01-01

Information visualization has appeared as an important research field for multidimensional data and correlation analysis in recent years. Parallel coordinates (PCs) are one of the popular techniques to visual high-dimensional data. A problem with the PCs technique is that it suffers from crowding, a clutter which hides important data and obfuscates the information. Earlier research has been conducted to reduce clutter without loss in data content. We introduce the use of image processing techniques as an approach for assessing the performance of clutter reduction techniques in PC. We use histogram analysis as our first measure, where the mean feature of the color histograms of the possible alternative orderings of coordinates for the PC images is calculated and compared. The second measure is the extracted contrast feature from the texture of PC images based on gray-level co-occurrence matrices. The results show that the best PC image is the one that has the minimal mean value of the color histogram feature and the maximal contrast value of the texture feature. In addition to its simplicity, the proposed assessment method has the advantage of objectively assessing alternative ordering of PC visualization.

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

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

Compositional features of cotton plant biomass fractions characterized by attenuated total reflection Fourier transform infrared spectroscopy

USDA-ARS?s Scientific Manuscript database

Cotton is one of the most important and widely grown crops in the world. In addition to natural textile fiber production as a primary purpose, it yields a high grade vegetable oil for human consumption and also carbohydrate fiber and protein byproducts for animal feed. In this work, attenuated total...

A Graphical Procedure for the Simultaneous Determination of the Distribution Constant of Iodine and the Stability Constants of Trihalide Anions.

ERIC Educational Resources Information Center

Kahwa, I. A.

1984-01-01

Discusses a graphical procedure which allows the distribution constant of iodine to be determined simultaneously with the trihalide anion stability constant. In addition, the procedure extends the experimental chemistry from distribution equilibria to important thermodynamic and bonding features. Advantages of using the procedure are also…

Adult and juvenile dermatomyositis: are the distinct clinical features explained by our current understanding of serological subgroups and pathogenic mechanisms?

PubMed Central

2013-01-01

Adult and juvenile dermatomyositis share the hallmark features of pathognomic skin rash and muscle inflammation, but are heterogeneous disorders with a range of additional disease features and complications. The frequency of important clinical features such as calcinosis, interstitial lung disease and malignancy varies markedly between adult and juvenile disease. These differences may reflect different disease triggers between children and adults, but whilst various viral and other environmental triggers have been implicated, results are so far conflicting. Myositis-specific autoantibodies can be detected in both adults and children with idiopathic inflammatory myopathies. They are associated with specific disease phenotypes and complications, and divide patients into clinically homogenous subgroups. Interestingly, whilst the same autoantibodies are found in both adults and children, the disease features remain different within autoantibody subgroups, particularly with regard to life-threatening disease associations, such as malignancy and rapidly progressive interstitial lung disease. Our understanding of the mechanisms that underlie these differences is limited by a lack of studies directly comparing adults and children. Dermatomyositis is an autoimmune disease, which is believed to develop as a result of an environmental trigger in a genetically predisposed individual. Age-specific host immune responses and muscle physiology may be additional complicating factors that have significant impact on disease presentation. Further study into this area may produce new insights into disease pathogenesis. PMID:23566358

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

A Human-Autonomy Teaming Approach for a Flight-Following Task

NASA Technical Reports Server (NTRS)

Brandt, Summer L.; Lachter, Joel; Russell, Ricky; Shively, R. Jay

2017-01-01

Human involvement with increasingly autonomous systems must adjust to allow for a more dynamic relationship involving cooperation and teamwork. As part of an ongoing project to develop a framework for human autonomy teaming (HAT) in aviation, a study was conducted to evaluate proposed tenets of HAT. Participants performed a flight-following task at a ground station both with and without HAT features enabled. Overall, participants preferred the ground station with HAT features enabled over the station without the HAT features. Participants reported that the HAT displays and automation were preferred for keeping up with operationally important issues. Additionally, participants reported that the HAT displays and automation provided enough situation awareness to complete the task, reduced the necessary workload and were efficient. Overall, there was general agreement that HAT features supported teaming with the automation. These results will be used to refine and expand our proposed framework for human-autonomy teaming.

Deep learning of support vector machines with class probability output networks.

PubMed

Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

2015-04-01

Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated. Copyright © 2014 Elsevier Ltd. All rights reserved.

Spectral-spatial classification of hyperspectral image using three-dimensional convolution network

NASA Astrophysics Data System (ADS)

Liu, Bing; Yu, Xuchu; Zhang, Pengqiang; Tan, Xiong; Wang, Ruirui; Zhi, Lu

2018-01-01

Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.

An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation

NASA Astrophysics Data System (ADS)

Lim, Meng-Hui; Teoh, Andrew Beng Jin

2011-12-01

Biometric discretization derives a binary string for each user based on an ordered set of biometric features. This representative string ought to be discriminative, informative, and privacy protective when it is employed as a cryptographic key in various security applications upon error correction. However, it is commonly believed that satisfying the first and the second criteria simultaneously is not feasible, and a tradeoff between them is always definite. In this article, we propose an effective fixed bit allocation-based discretization approach which involves discriminative feature extraction, discriminative feature selection, unsupervised quantization (quantization that does not utilize class information), and linearly separable subcode (LSSC)-based encoding to fulfill all the ideal properties of a binary representation extracted for cryptographic applications. In addition, we examine a number of discriminative feature-selection measures for discretization and identify the proper way of setting an important feature-selection parameter. Encouraging experimental results vindicate the feasibility of our approach.

Impaired visual search in rats reveals cholinergic contributions to feature binding in visuospatial attention.

PubMed

Botly, Leigh C P; De Rosa, Eve

2012-10-01

The visual search task established the feature integration theory of attention in humans and measures visuospatial attentional contributions to feature binding. We recently demonstrated that the neuromodulator acetylcholine (ACh), from the nucleus basalis magnocellularis (NBM), supports the attentional processes required for feature binding using a rat digging-based task. Additional research has demonstrated cholinergic contributions from the NBM to visuospatial attention in rats. Here, we combined these lines of evidence and employed visual search in rats to examine whether cortical cholinergic input supports visuospatial attention specifically for feature binding. We trained 18 male Long-Evans rats to perform visual search using touch screen-equipped operant chambers. Sessions comprised Feature Search (no feature binding required) and Conjunctive Search (feature binding required) trials using multiple stimulus set sizes. Following acquisition of visual search, 8 rats received bilateral NBM lesions using 192 IgG-saporin to selectively reduce cholinergic afferentation of the neocortex, which we hypothesized would selectively disrupt the visuospatial attentional processes needed for efficient conjunctive visual search. As expected, relative to sham-lesioned rats, ACh-NBM-lesioned rats took significantly longer to locate the target stimulus on Conjunctive Search, but not Feature Search trials, thus demonstrating that cholinergic contributions to visuospatial attention are important for feature binding in rats.

Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals.

PubMed

Ebrahimi, Farideh; Setarehdan, Seyed-Kamaledin; Ayala-Moyeda, Jose; Nazeran, Homer

2013-10-01

The conventional method for sleep staging is to analyze polysomnograms (PSGs) recorded in a sleep lab. The electroencephalogram (EEG) is one of the most important signals in PSGs but recording and analysis of this signal presents a number of technical challenges, especially at home. Instead, electrocardiograms (ECGs) are much easier to record and may offer an attractive alternative for home sleep monitoring. The heart rate variability (HRV) signal proves suitable for automatic sleep staging. Thirty PSGs from the Sleep Heart Health Study (SHHS) database were used. Three feature sets were extracted from 5- and 0.5-min HRV segments: time-domain features, nonlinear-dynamics features and time-frequency features. The latter was achieved by using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods. Normalized energies in important frequency bands of HRV signals were computed using time-frequency methods. ANOVA and t-test were used for statistical evaluations. Automatic sleep staging was based on HRV signal features. The ANOVA followed by a post hoc Bonferroni was used for individual feature assessment. Most features were beneficial for sleep staging. A t-test was used to compare the means of extracted features in 5- and 0.5-min HRV segments. The results showed that the extracted features means were statistically similar for a small number of features. A separability measure showed that time-frequency features, especially EMD features, had larger separation than others. There was not a sizable difference in separability of linear features between 5- and 0.5-min HRV segments but separability of nonlinear features, especially EMD features, decreased in 0.5-min HRV segments. HRV signal features were classified by linear discriminant (LD) and quadratic discriminant (QD) methods. Classification results based on features from 5-min segments surpassed those obtained from 0.5-min segments. The best result was obtained from features using 5-min HRV segments classified by the LD classifier. A combination of linear/nonlinear features from HRV signals is effective in automatic sleep staging. Moreover, time-frequency features are more informative than others. In addition, a separability measure and classification results showed that HRV signal features, especially nonlinear features, extracted from 5-min segments are more discriminative than those from 0.5-min segments in automatic sleep staging. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Sustainability Literacy of Older People in Retirement Villages

PubMed Central

Xia, Bo; Zuo, Jian; Skitmore, Martin; Buys, Laurie; Hu, Xin

2014-01-01

With many developed countries experiencing the aging of the population, older people play a large role in contributing to environmental problems but also to environmental solutions. The purpose of this research is to understand the awareness and behavior of current older people living in retirement villages towards sustainability development. To achieve this, a sustainability literacy survey was conducted with 65 older residents of a private retirement village located 10 Km outside the Brisbane, Australia's central business district (CBD). Most of residents recognized the importance of environment protection and would like to lead a more environmentally friendly lifestyle. In addition, the majority were willing to pay higher prices for a living environment with sustainable features. The importance of positive social communications was emphasized with most residents having established good relationships with others in the village. The findings provide an important insight into consumer perspectives regarding the sustainable features that should and can be incorporated into the village planning and development. PMID:25587448

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.

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

PubMed

Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

2018-02-01

Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks

PubMed Central

Kerkhofs, Johan; Geris, Liesbet

2015-01-01

Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour. PMID:26067297

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

NASA Astrophysics Data System (ADS)

Weinmann, Martin; Jutzi, Boris; Hinz, Stefan; Mallet, Clément

2015-07-01

3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.

Realistic Free-Spins Features Increase Preference for Slot Machines.

PubMed

Taylor, Lorance F; Macaskill, Anne C; Hunt, Maree J

2017-06-01

Despite increasing research into how the structural characteristics of slot machines influence gambling behaviour there have been no experimental investigations into the effect of free-spins bonus features-a structural characteristic that is commonly central to the design of slot machines. This series of three experiments investigated the free-spins feature using slot machine simulations to determine whether participants allocate more wagers to a machine with free spins, and, which components of free-spins features drive this preference. In each experiment, participants were exposed to two computer-simulated slot machines-one with a free-spins feature or similar bonus feature and one without. Participants then completed a testing phase where they could freely switch between the two machines. In Experiment 1, participants did not prefer the machine with a simple free-spins feature. In Experiment 2 the free-spins feature incorporated additional elements such as sounds, animations, and an increased win frequency; participants preferred to gamble on this machine. The Experiment 3 "bonus feature" machine resembled the free spins machine in Experiment 2 except spins were not free; participants showed a clear preference for this machine also. These findings indicate that (1) free-spins features have a major influence over machine choice and (2) the "freeness" of the free-spins bonus features is not an important driver of preference, contrary to self-report and interview research with gamblers.

The role of attention in item-item binding in visual working memory.

PubMed

Peterson, Dwight J; Naveh-Benjamin, Moshe

2017-09-01

An important yet unresolved question regarding visual working memory (VWM) relates to whether or not binding processes within VWM require additional attentional resources compared with processing solely the individual components comprising these bindings. Previous findings indicate that binding of surface features (e.g., colored shapes) within VWM is not demanding of resources beyond what is required for single features. However, it is possible that other types of binding, such as the binding of complex, distinct items (e.g., faces and scenes), in VWM may require additional resources. In 3 experiments, we examined VWM item-item binding performance under no load, articulatory suppression, and backward counting using a modified change detection task. Binding performance declined to a greater extent than single-item performance under higher compared with lower levels of concurrent load. The findings from each of these experiments indicate that processing item-item bindings within VWM requires a greater amount of attentional resources compared with single items. These findings also highlight an important distinction between the role of attention in item-item binding within VWM and previous studies of long-term memory (LTM) where declines in single-item and binding test performance are similar under divided attention. The current findings provide novel evidence that the specific type of binding is an important determining factor regarding whether or not VWM binding processes require attention. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Variation and Defect Tolerance for Nano Crossbars

NASA Astrophysics Data System (ADS)

Tunc, Cihan

With the extreme shrinking in CMOS technology, quantum effects and manufacturing issues are getting more crucial. Hence, additional shrinking in CMOS feature size seems becoming more challenging, difficult, and costly. On the other hand, emerging nanotechnology has attracted many researchers since additional scaling down has been demonstrated by manufacturing nanowires, Carbon nanotubes as well as molecular switches using bottom-up manufacturing techniques. In addition to the progress in manufacturing, developments in architecture show that emerging nanoelectronic devices will be promising for the future system designs. Using nano crossbars, which are composed of two sets of perpendicular nanowires with programmable intersections, it is possible to implement logic functions. In addition, nano crossbars present some important features as regularity, reprogrammability, and interchangeability. Combining these features, researchers have presented different effective architectures. Although bottom-up nanofabrication can greatly reduce manufacturing costs, due to low controllability in the manufacturing process, some critical issues occur. Bottom- up nanofabrication process results in high variation compared to conventional top- down lithography used in CMOS technology. In addition, an increased failure rate is expected. Variation and defect tolerance methods used for conventional CMOS technology seem inadequate for adapting to emerging nano technology because the variation and the defect rate for emerging nano technology is much more than current CMOS technology. Therefore, variations and defect tolerance methods for emerging nano technology are necessary for a successful transition. In this work, in order to tolerate variations for crossbars, we introduce a framework that is established based on reprogrammability and interchangeability features of nano crossbars. This framework is shown to be applicable for both FET-based and diode-based nano crossbars. We present a characterization testing method which requires minimal number of test vectors. We formulate the variation optimization problem using Simulated Annealing with different optimization goals. Furthermore, we extend the framework for defect tolerance. Experimental results and comparison of proposed framework with exhaustive methods confirm its effectiveness for both variation and defect tolerance.

[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.

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.

LncRNA, a new component of expanding RNA-protein regulatory network important for animal sperm development.

PubMed

Zhang, Chenwang; Gao, Liuze; Xu, Eugene Yujun

2016-11-01

Spermatogenesis is one of the fundamental processes of sexual reproduction, present in almost all metazoan animals. Like many other reproductive traits, developmental features and traits of spermatogenesis are under strong selective pressure to change, both at morphological and underlying molecular levels. Yet evidence suggests that some fundamental features of spermatogenesis may be ancient and conserved among metazoan species. Identifying the underlying conserved molecular mechanisms could reveal core components of metazoan spermatogenic machinery and provide novel insight into causes of human infertility. Conserved RNA-binding proteins and their interacting RNA network emerge to be a common theme important for animal sperm development. We review research on the recent addition to the RNA family - Long non-coding RNA (lncRNA) and its roles in spermatogenesis in the context of the expanding RNA-protein network. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Structures composing protein domains.

PubMed

Kubrycht, Jaroslav; Sigler, Karel; Souček, Pavel; Hudeček, Jiří

2013-08-01

This review summarizes available data concerning intradomain structures (IS) such as functionally important amino acid residues, short linear motifs, conserved or disordered regions, peptide repeats, broadly occurring secondary structures or folds, etc. IS form structural features (units or elements) necessary for interactions with proteins or non-peptidic ligands, enzyme reactions and some structural properties of proteins. These features have often been related to a single structural level (e.g. primary structure) mostly requiring certain structural context of other levels (e.g. secondary structures or supersecondary folds) as follows also from some examples reported or demonstrated here. In addition, we deal with some functionally important dynamic properties of IS (e.g. flexibility and different forms of accessibility), and more special dynamic changes of IS during enzyme reactions and allosteric regulation. Selected notes concern also some experimental methods, still more necessary tools of bioinformatic processing and clinically interesting relationships. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Corresponding-states behavior of SPC/E-based modified (bent and hybrid) water models

NASA Astrophysics Data System (ADS)

Weiss, Volker C.

2017-02-01

The remarkable and sometimes anomalous properties of water can be traced back at the molecular level to the tetrahedral coordination of molecules due to the ability of a water molecule to form four hydrogen bonds to its neighbors; this feature allows for the formation of a network that greatly influences the thermodynamic behavior. Computer simulations are becoming increasingly important for our understanding of water. Molecular models of water, such as SPC/E, are needed for this purpose, and they have proved to capture many important features of real water. Modifications of the SPC/E model have been proposed, some changing the H-O-H angle (bent models) and others increasing the importance of dispersion interactions (hybrid models), to study the structural features that set water apart from other polar fluids and from simple fluids such as argon. Here, we focus on the properties at liquid-vapor equilibrium and study the coexistence curve, the interfacial tension, and the vapor pressure in a corresponding-states approach. In particular, we calculate Guggenheim's ratio for the reduced apparent enthalpy of vaporization and Guldberg's ratio for the reduced normal boiling point. This analysis offers additional insight from a more macroscopic, thermodynamic perspective and augments that which has already been learned at the molecular level from simulations. In the hybrid models, the relative importance of dispersion interactions is increased, which turns the modified water into a Lennard-Jones-like fluid. Consequently, in a corresponding-states framework, the typical behavior of simple fluids, such as argon, is seen to be approached asymptotically. For the bent models, decreasing the bond angle turns the model essentially into a polar diatomic fluid in which the particles form linear molecular arrangements; as a consequence, characteristic features of the corresponding-states behavior of hydrogen halides emerge.

Helioviewer: A Web 2.0 Tool for Visualizing Heterogeneous Heliophysics Data

NASA Astrophysics Data System (ADS)

Hughitt, V. K.; Ireland, J.; Lynch, M. J.; Schmeidel, P.; Dimitoglou, G.; Müeller, D.; Fleck, B.

2008-12-01

Solar physics datasets are becoming larger, richer, more numerous and more distributed. Feature/event catalogs (describing objects of interest in the original data) are becoming important tools in navigating these data. In the wake of this increasing influx of data and catalogs there has been a growing need for highly sophisticated tools for accessing and visualizing this wealth of information. Helioviewer is a novel tool for integrating and visualizing disparate sources of solar and Heliophysics data. Taking advantage of the newly available power of modern web application frameworks, Helioviewer merges image and feature catalog data, and provides for Heliophysics data a familiar interface not unlike Google Maps or MapQuest. In addition to streamlining the process of combining heterogeneous Heliophysics datatypes such as full-disk images and coronagraphs, the inclusion of visual representations of automated and human-annotated features provides the user with an integrated and intuitive view of how different factors may be interacting on the Sun. Currently, Helioviewer offers images from The Extreme ultraviolet Imaging Telescope (EIT), The Large Angle and Spectrometric COronagraph experiment (LASCO) and the Michelson Doppler Imager (MDI) instruments onboard The Solar and Heliospheric Observatory (SOHO), as well as The Transition Region and Coronal Explorer (TRACE). Helioviewer also incorporates feature/event information from the LASCO CME List, NOAA Active Regions, CACTus CME and Type II Radio Bursts feature/event catalogs. The project is undergoing continuous development with many more data sources and additional functionality planned for the near future.

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.

Feature extraction and classification algorithms for high dimensional data

NASA Technical Reports Server (NTRS)

Lee, Chulhee; Landgrebe, David

1993-01-01

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

Multi-source remotely sensed data fusion for improving land cover classification

NASA Astrophysics Data System (ADS)

Chen, Bin; Huang, Bo; Xu, Bing

2017-02-01

Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.

A new approach to modeling the influence of image features on fixation selection in scenes

PubMed Central

Nuthmann, Antje; Einhäuser, Wolfgang

2015-01-01

Which image characteristics predict where people fixate when memorizing natural images? To answer this question, we introduce a new analysis approach that combines a novel scene-patch analysis with generalized linear mixed models (GLMMs). Our method allows for (1) directly describing the relationship between continuous feature value and fixation probability, and (2) assessing each feature's unique contribution to fixation selection. To demonstrate this method, we estimated the relative contribution of various image features to fixation selection: luminance and luminance contrast (low-level features); edge density (a mid-level feature); visual clutter and image segmentation to approximate local object density in the scene (higher-level features). An additional predictor captured the central bias of fixation. The GLMM results revealed that edge density, clutter, and the number of homogenous segments in a patch can independently predict whether image patches are fixated or not. Importantly, neither luminance nor contrast had an independent effect above and beyond what could be accounted for by the other predictors. Since the parcellation of the scene and the selection of features can be tailored to the specific research question, our approach allows for assessing the interplay of various factors relevant for fixation selection in scenes in a powerful and flexible manner. PMID:25752239

Neocentromeres and epigenetically inherited features of centromeres

PubMed Central

Burrack, Laura S.; Berman, Judith

2012-01-01

Neocentromeres are ectopic sites where new functional kinetochores assemble and permit chromosome segregation. Neocentromeres usually form following genomic alterations that remove or disrupt centromere function. The ability to form neocentromeres is conserved in eukaryotes ranging from fungi to mammals. Neocentromeres that rescue chromosome fragments in cells with gross chromosomal rearrangements are found in several types of human cancers, and in patients with developmental disabilities. In this review, we discuss the importance of neocentromeres to human health and evaluate recently developed model systems to study neocentromere formation, maintenance, and function in chromosome segregation. Additionally, studies of neocentromeres provide insight into native centromeres; analysis of neocentromeres found in human clinical samples and induced in model organisms distinguishes features of centromeres that are dependent on centromere DNA from features that are epigenetically inherited together with the formation of a functional kinetochore. PMID:22723125

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.

Online writer identification using alphabetic information clustering

NASA Astrophysics Data System (ADS)

Tan, Guo Xian; Viard-Gaudin, Christian; Kot, Alex C.

2009-01-01

Writer identification is a topic of much renewed interest today because of its importance in applications such as writer adaptation, routing of documents and forensic document analysis. Various algorithms have been proposed to handle such tasks. Of particular interests are the approaches that use allographic features [1-3] to perform a comparison of the documents in question. The allographic features are used to define prototypes that model the unique handwriting styles of the individual writers. This paper investigates a novel perspective that takes alphabetic information into consideration when the allographic features are clustered into prototypes at the character level. We hypothesize that alphabetic information provides additional clues which help in the clustering of allographic prototypes. An alphabet information coefficient (AIC) has been introduced in our study and the effect of this coefficient is presented. Our experiments showed an increase of writer identification accuracy from 66.0% to 87.0% when alphabetic information was used in conjunction with allographic features on a database of 200 reference writers.

Inline Measurement of Particle Concentrations in Multicomponent Suspensions using Ultrasonic Sensor and Least Squares Support Vector Machines.

PubMed

Zhan, Xiaobin; Jiang, Shulan; Yang, Yili; Liang, Jian; Shi, Tielin; Li, Xiwen

2015-09-18

This paper proposes an ultrasonic measurement system based on least squares support vector machines (LS-SVM) for inline measurement of particle concentrations in multicomponent suspensions. Firstly, the ultrasonic signals are analyzed and processed, and the optimal feature subset that contributes to the best model performance is selected based on the importance of features. Secondly, the LS-SVM model is tuned, trained and tested with different feature subsets to obtain the optimal model. In addition, a comparison is made between the partial least square (PLS) model and the LS-SVM model. Finally, the optimal LS-SVM model with the optimal feature subset is applied to inline measurement of particle concentrations in the mixing process. The results show that the proposed method is reliable and accurate for inline measuring the particle concentrations in multicomponent suspensions and the measurement accuracy is sufficiently high for industrial application. Furthermore, the proposed method is applicable to the modeling of the nonlinear system dynamically and provides a feasible way to monitor industrial processes.

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.

Peroxisome protein import: a complex journey.

PubMed

Baker, Alison; Lanyon-Hogg, Thomas; Warriner, Stuart L

2016-06-15

The import of proteins into peroxisomes possesses many unusual features such as the ability to import folded proteins, and a surprising diversity of targeting signals with differing affinities that can be recognized by the same receptor. As understanding of the structure and function of many components of the protein import machinery has grown, an increasingly complex network of factors affecting each step of the import pathway has emerged. Structural studies have revealed the presence of additional interactions between cargo proteins and the PEX5 receptor that affect import potential, with a subtle network of cargo-induced conformational changes in PEX5 being involved in the import process. Biochemical studies have also indicated an interdependence of receptor-cargo import with release of unloaded receptor from the peroxisome. Here, we provide an update on recent literature concerning mechanisms of protein import into peroxisomes. © 2016 The Author(s).

The reflectivity, wettability and scratch durability of microsurface features molded in the injection molding process using a dynamic tool tempering system

NASA Astrophysics Data System (ADS)

Kuhn, Sascha; Burr, August; Kübler, Michael; Deckert, Matthias; Bleesen, Christoph

2011-02-01

In this paper the replication qualities of periodically and randomly arranged micro-features molded in the injection molding process and their effects on surface properties are studied. The features are molded in PC, PMMA and PP at different mold wall temperatures in order to point out the necessity and profitability of a variotherm mold wall temperature control system. A one-dimensional heat conduction model is proposed to predict the cycle times of the variotherm injection molding processes. With regard to these processes, the molding results are compared to the molded surface feature heights using an atomic force microscope. In addition, the effects of the molded surface features on macroscopic surfaces are characterized in terms of light reflection using a spectrometer and in terms of water wettability by measuring the static contact angle. Furthermore, due to the sensitivity of the surface features on the molded parts, their durability is compared in a scratch test with a diamond tip. This leads to successful implementation in applications in which the optical appearance, in terms of gloss and reflection, and the water repellence, in terms of drag flow and adhesion, are of importance.

Population responses in primary auditory cortex simultaneously represent the temporal envelope and periodicity features in natural speech.

PubMed

Abrams, Daniel A; Nicol, Trent; White-Schwoch, Travis; Zecker, Steven; Kraus, Nina

2017-05-01

Speech perception relies on a listener's ability to simultaneously resolve multiple temporal features in the speech signal. Little is known regarding neural mechanisms that enable the simultaneous coding of concurrent temporal features in speech. Here we show that two categories of temporal features in speech, the low-frequency speech envelope and periodicity cues, are processed by distinct neural mechanisms within the same population of cortical neurons. We measured population activity in primary auditory cortex of anesthetized guinea pig in response to three variants of a naturally produced sentence. Results show that the envelope of population responses closely tracks the speech envelope, and this cortical activity more closely reflects wider bandwidths of the speech envelope compared to narrow bands. Additionally, neuronal populations represent the fundamental frequency of speech robustly with phase-locked responses. Importantly, these two temporal features of speech are simultaneously observed within neuronal ensembles in auditory cortex in response to clear, conversation, and compressed speech exemplars. Results show that auditory cortical neurons are adept at simultaneously resolving multiple temporal features in extended speech sentences using discrete coding mechanisms. Copyright © 2017 Elsevier B.V. All rights reserved.

MRM-Lasso: A Sparse Multiview Feature Selection Method via Low-Rank Analysis.

PubMed

Yang, Wanqi; Gao, Yang; Shi, Yinghuan; Cao, Longbing

2015-11-01

Learning about multiview data involves many applications, such as video understanding, image classification, and social media. However, when the data dimension increases dramatically, it is important but very challenging to remove redundant features in multiview feature selection. In this paper, we propose a novel feature selection algorithm, multiview rank minimization-based Lasso (MRM-Lasso), which jointly utilizes Lasso for sparse feature selection and rank minimization for learning relevant patterns across views. Instead of simply integrating multiple Lasso from view level, we focus on the performance of sample-level (sample significance) and introduce pattern-specific weights into MRM-Lasso. The weights are utilized to measure the contribution of each sample to the labels in the current view. In addition, the latent correlation across different views is successfully captured by learning a low-rank matrix consisting of pattern-specific weights. The alternating direction method of multipliers is applied to optimize the proposed MRM-Lasso. Experiments on four real-life data sets show that features selected by MRM-Lasso have better multiview classification performance than the baselines. Moreover, pattern-specific weights are demonstrated to be significant for learning about multiview data, compared with view-specific weights.

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.

Fluorescence Intrinsic Characterization of Excitation-Emission Matrix Using Multi-Dimensional Ensemble Empirical Mode Decomposition

PubMed Central

Chang, Chi-Ying; Chang, Chia-Chi; Hsiao, Tzu-Chien

2013-01-01

Excitation-emission matrix (EEM) fluorescence spectroscopy is a noninvasive method for tissue diagnosis and has become important in clinical use. However, the intrinsic characterization of EEM fluorescence remains unclear. Photobleaching and the complexity of the chemical compounds make it difficult to distinguish individual compounds due to overlapping features. Conventional studies use principal component analysis (PCA) for EEM fluorescence analysis, and the relationship between the EEM features extracted by PCA and diseases has been examined. The spectral features of different tissue constituents are not fully separable or clearly defined. Recently, a non-stationary method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to propose a fluorescence spectroscopy system for EEM measurements and to describe a method for extracting the intrinsic characteristics of EEM by MEEMD. The results indicate that, although PCA provides the principal factor for the spectral features associated with chemical compounds, MEEMD can provide additional intrinsic features with more reliable mapping of the chemical compounds. MEEMD has the potential to extract intrinsic fluorescence features and improve the detection of biochemical changes. PMID:24240806

Tiled vector data model for the geographical features of symbolized maps.

PubMed

Li, Lin; Hu, Wei; Zhu, Haihong; Li, You; Zhang, Hang

2017-01-01

Electronic maps (E-maps) provide people with convenience in real-world space. Although web map services can display maps on screens, a more important function is their ability to access geographical features. An E-map that is based on raster tiles is inferior to vector tiles in terms of interactive ability because vector maps provide a convenient and effective method to access and manipulate web map features. However, the critical issue regarding rendering tiled vector maps is that geographical features that are rendered in the form of map symbols via vector tiles may cause visual discontinuities, such as graphic conflicts and losses of data around the borders of tiles, which likely represent the main obstacles to exploring vector map tiles on the web. This paper proposes a tiled vector data model for geographical features in symbolized maps that considers the relationships among geographical features, symbol representations and map renderings. This model presents a method to tailor geographical features in terms of map symbols and 'addition' (join) operations on the following two levels: geographical features and map features. Thus, these maps can resolve the visual discontinuity problem based on the proposed model without weakening the interactivity of vector maps. The proposed model is validated by two map data sets, and the results demonstrate that the rendered (symbolized) web maps present smooth visual continuity.

Assessing the Energy Consumption of Smartphone Applications

NASA Astrophysics Data System (ADS)

Abousaleh, Mustafa M.

Mobile devices are increasingly becoming essential in people's lives. The advancement in technology and mobility factor are allowing users to utilize mobile devices for communication, entertainment, financial planning, fitness tracking, etc. As a result, mobile applications are also becoming important factors contributing to user utility. However, battery capacity is the limiting factor impacting the quality of user experience. Hence, it is imperative to understand how much energy impact do mobile apps have on the system relative to other device activities. This thesis presents a systematic studying of the energy impact of mobile apps features. Time-series electrical current measurements are collected from 4 different modern smartphones. Statistical analysis methodologies are used to calculate the energy impact of each app feature by identifying and extracting mobile app-feature events from the overall current signal. In addition, the app overhead energy costs are also computed. Total energy consumption equations for each component is developed and an overall total energy consumption equation is presented. Minutes Lost (ML) of normal phone operations due to the energy consumption of the mobile app functionality is computed for cases where the mobile app is simulated to run on the various devices for 30 minutes. Tutela Technologies Inc. mobile app, NAT, is used for this study. NAT has two main features: QoS and Throughput. The impact of the QoS feature is indistinguishable, i.e. ML is zero, relative to other phone activities. The ML with only the TP feature enabled is on average 2.1 minutes. Enabling the GPS increases the ML on average to 11.5 minutes. Displaying the app GUI interface in addition to running the app features and enabling the GPS results in an average ML of 12.4 minutes. Amongst the various mobile app features and components studied, the GPS consumes the highest amount of energy. It is estimated that the GPS increases the ML by about 448%.

Is adermatoglyphia an additional feature of Kindler Syndrome?

PubMed

Almeida, Hiram Larangeira de; Goetze, Fernanda Mendes; Fong, Kenneth; Lai-Cheong, Joey; McGrath, John

2015-01-01

A typical feature of Kindler Syndrome is skin fragility; this condition in currently classified as a form of epidermolysis bullosa. We describe a rarely reported feature of two cases, one sporadic and one familial; both patients noticed acquired adermatoglyphia. The loss of dermatoglyphics could be an additional feature of this syndrome.

Smart cards--the key to trustworthy health information systems.

PubMed Central

Neame, R.

1997-01-01

Some 20 years after they were first developed, "smart cards" are set to play a crucial part in healthcare systems. Last year about a billion were supplied, mainly for use in the financial sector, but their special features make them of particular strategic importance for the health sector, where they offer a ready made solution to some key problems of security and confidentiality. This article outlines what smart cards are and why they are so important in managing health information. I discuss some of the unique features of smart cards that are of special importance in the development of secure and trustworthy health information systems. Smart cards would enable individuals' identities to be authenticated and communications to be secured and would provide the mechanisms for implementing strong security, differential access to data, and definitive audit trails. Patient cards can also with complete security carry personal details, data on current health problems and medications, emergency care data, and pointers to where medical records for the patient can be found. Provider cards can in addition carry authorisations and information on computer set up. PMID:9055719

Top-down attention based on object representation and incremental memory for knowledge building and inference.

PubMed

Kim, Bumhwi; Ban, Sang-Woo; Lee, Minho

2013-10-01

Humans can efficiently perceive arbitrary visual objects based on an incremental learning mechanism with selective attention. This paper proposes a new task specific top-down attention model to locate a target object based on its form and color representation along with a bottom-up saliency based on relativity of primitive visual features and some memory modules. In the proposed model top-down bias signals corresponding to the target form and color features are generated, which draw the preferential attention to the desired object by the proposed selective attention model in concomitance with the bottom-up saliency process. The object form and color representation and memory modules have an incremental learning mechanism together with a proper object feature representation scheme. The proposed model includes a Growing Fuzzy Topology Adaptive Resonance Theory (GFTART) network which plays two important roles in object color and form biased attention; one is to incrementally learn and memorize color and form features of various objects, and the other is to generate a top-down bias signal to localize a target object by focusing on the candidate local areas. Moreover, the GFTART network can be utilized for knowledge inference which enables the perception of new unknown objects on the basis of the object form and color features stored in the memory during training. Experimental results show that the proposed model is successful in focusing on the specified target objects, in addition to the incremental representation and memorization of various objects in natural scenes. In addition, the proposed model properly infers new unknown objects based on the form and color features of previously trained objects. Copyright © 2013 Elsevier Ltd. All rights reserved.

Impact of the rate of the additive process of forming a heavy structure deforming in creep on the development of its technological stresses

NASA Astrophysics Data System (ADS)

Parshin, Dmitry A.

2018-05-01

The additive process of forming a semicircular arched structure by means of layer-by-layer addition of material to its inner surface is simulated. The impact of this process running mode on the development of the technological stresses fields in the structure being formed under the action of gravity under properties of the material creep and aging is examined. In the framework of the linear mechanics of accreted solids a mathematical model of the process under study is offered and numerical experiments are conducted. It is shown that the stress-strain state of the additively formed heavy objects decisively depends on their formation mode. Various practically important trends and features of this dependence are studied.

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.

Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks

PubMed Central

Barkallah, Eya; Freulard, Johan; Otis, Martin J. -D.; Ngomo, Suzy; Ayena, Johannes C.; Desrosiers, Christian

2017-01-01

Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture. PMID:28862665

Application of Multi-Threshold NULL Convention Logic to Adaptive Beamforming Circuits for Ultra-Low Power

DTIC Science & Technology

2016-03-31

Abstract: With the decrease of transistor feature sizes into the ultra-deep submicron range, leakage power becomes an important design challenge for...MTNCL design showed substantial improvements in terms of active energy and leakage power compared to the equivalent synchronous design. Keywords...switching could use a large portion of power. Additionally, leakage power has come to dominate power consumption as process sizes shrink. Adaptive

An Open Architecture for Defense Virtual Environment Training Systems

DTIC Science & Technology

2003-09-01

Additionally, in the process, preventing the loss of life is also an important result. VRTEs can provide needed training that might otherwise not be...training is directly valuable in mission accomplishment and in preventing loss of life. "One of the biggest problems in both the military and...simplified; unique bones motion offers lifelike bouncing and twisting. 43 o Complete skeletal and muscle control features. o Inverse Kinematics

Minimum Uncertainty Coherent States Attached to Nondegenerate Parametric Amplifiers

NASA Astrophysics Data System (ADS)

Dehghani, A.; Mojaveri, B.

2015-06-01

Exact analytical solutions for the two-mode nondegenerate parametric amplifier have been obtained by using the transformation from the two-dimensional harmonic oscillator Hamiltonian. Some important physical properties such as quantum statistics and quadrature squeezing of the corresponding states are investigated. In addition, these states carry classical features such as Poissonian statistics and minimize the Heisenberg uncertainty relation of a pair of the coordinate and the momentum operators.

Practical Radiobiology for Proton Therapy Planning

NASA Astrophysics Data System (ADS)

Jones, Bleddyn

2017-12-01

Practical Radiobiology for Proton Therapy Planning covers the principles, advantages and potential pitfalls that occur in proton therapy, especially its radiobiological modelling applications. This book is intended to educate, inform and to stimulate further research questions. Additionally, it will help proton therapy centres when designing new treatments or when unintended errors or delays occur. The clear descriptions of useful equations for high LET particle beam applications, worked examples of many important clinical situations, and discussion of how proton therapy may be optimized are all important features of the text. This important book blends the relevant physics, biology and medical aspects of this multidisciplinary subject. Part of Series in Physics and Engineering in Medicine and Biology.

Advanced applications of scatterometry based optical metrology

NASA Astrophysics Data System (ADS)

Dixit, Dhairya; Keller, Nick; Kagalwala, Taher; Recchia, Fiona; Lifshitz, Yevgeny; Elia, Alexander; Todi, Vinit; Fronheiser, Jody; Vaid, Alok

2017-03-01

The semiconductor industry continues to drive patterning solutions that enable devices with higher memory storage capacity, faster computing performance, and lower cost per transistor. These developments in the field of semiconductor manufacturing along with the overall minimization of the size of transistors require continuous development of metrology tools used for characterization of these complex 3D device architectures. Optical scatterometry or optical critical dimension (OCD) is one of the most prevalent inline metrology techniques in semiconductor manufacturing because it is a quick, precise and non-destructive metrology technique. However, at present OCD is predominantly used to measure the feature dimensions such as line-width, height, side-wall angle, etc. of the patterned nano structures. Use of optical scatterometry for characterizing defects such as pitch-walking, overlay, line edge roughness, etc. is fairly limited. Inspection of process induced abnormalities is a fundamental part of process yield improvement. It provides process engineers with important information about process errors, and consequently helps optimize materials and process parameters. Scatterometry is an averaging technique and extending it to measure the position of local process induced defectivity and feature-to-feature variation is extremely challenging. This report is an overview of applications and benefits of using optical scatterometry for characterizing defects such as pitch-walking, overlay and fin bending for advanced technology nodes beyond 7nm. Currently, the optical scatterometry is based on conventional spectroscopic ellipsometry and spectroscopic reflectometry measurements, but generalized ellipsometry or Mueller matrix spectroscopic ellipsometry data provides important, additional information about complex structures that exhibit anisotropy and depolarization effects. In addition the symmetry-antisymmetry properties associated with Mueller matrix (MM) elements provide an excellent means of measuring asymmetry present in the structure. The useful additional information as well as symmetry-antisymmetry properties of MM elements is used to characterize fin bending, overlay defects and design improvements in the OCD test structures are used to boost OCDs' sensitivity to pitch-walking. In addition, the validity of the OCD based results is established by comparing the results to the top down critical dimensionscanning electron microscope (CD-SEM) and cross-sectional transmission electron microscope (TEM) images.

Spectroscopy and atomic force microscopy of biomass.

PubMed

Tetard, L; Passian, A; Farahi, R H; Kalluri, U C; Davison, B H; Thundat, T

2010-05-01

Scanning probe microscopy has emerged as a powerful approach to a broader understanding of the molecular architecture of cell walls, which may shed light on the challenge of efficient cellulosic ethanol production. We have obtained preliminary images of both Populus and switchgrass samples using atomic force microscopy (AFM). The results show distinctive features that are shared by switchgrass and Populus. These features may be attributable to the lignocellulosic cell wall composition, as the collected images exhibit the characteristic macromolecular globule structures attributable to the lignocellulosic systems. Using both AFM and a single case of mode synthesizing atomic force microscopy (MSAFM) to characterize Populus, we obtained images that clearly show the cell wall structure. The results are of importance in providing a better understanding of the characteristic features of both mature cells as well as developing plant cells. In addition, we present spectroscopic investigation of the same samples.

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.

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

A rhythm-based authentication scheme for smart media devices.

PubMed

Lee, Jae Dong; Jeong, Young-Sik; Park, Jong Hyuk

2014-01-01

In recent years, ubiquitous computing has been rapidly emerged in our lives and extensive studies have been conducted in a variety of areas related to smart devices, such as tablets, smartphones, smart TVs, smart refrigerators, and smart media devices, as a measure for realizing the ubiquitous computing. In particular, smartphones have significantly evolved from the traditional feature phones. Increasingly higher-end smartphone models that can perform a range of functions are now available. Smart devices have become widely popular since they provide high efficiency and great convenience for not only private daily activities but also business endeavors. Rapid advancements have been achieved in smart device technologies to improve the end users' convenience. Consequently, many people increasingly rely on smart devices to store their valuable and important data. With this increasing dependence, an important aspect that must be addressed is security issues. Leaking of private information or sensitive business data due to loss or theft of smart devices could result in exorbitant damage. To mitigate these security threats, basic embedded locking features are provided in smart devices. However, these locking features are vulnerable. In this paper, an original security-locking scheme using a rhythm-based locking system (RLS) is proposed to overcome the existing security problems of smart devices. RLS is a user-authenticated system that addresses vulnerability issues in the existing locking features and provides secure confidentiality in addition to convenience.

Distance error correction for time-of-flight cameras

NASA Astrophysics Data System (ADS)

Fuersattel, Peter; Schaller, Christian; Maier, Andreas; Riess, Christian

2017-06-01

The measurement accuracy of time-of-flight cameras is limited due to properties of the scene and systematic errors. These errors can accumulate to multiple centimeters which may limit the applicability of these range sensors. In the past, different approaches have been proposed for improving the accuracy of these cameras. In this work, we propose a new method that improves two important aspects of the range calibration. First, we propose a new checkerboard which is augmented by a gray-level gradient. With this addition it becomes possible to capture the calibration features for intrinsic and distance calibration at the same time. The gradient strip allows to acquire a large amount of distance measurements for different surface reflectivities, which results in more meaningful training data. Second, we present multiple new features which are used as input to a random forest regressor. By using random regression forests, we circumvent the problem of finding an accurate model for the measurement error. During application, a correction value for each individual pixel is estimated with the trained forest based on a specifically tailored feature vector. With our approach the measurement error can be reduced by more than 40% for the Mesa SR4000 and by more than 30% for the Microsoft Kinect V2. In our evaluation we also investigate the impact of the individual forest parameters and illustrate the importance of the individual features.

A Rhythm-Based Authentication Scheme for Smart Media Devices

PubMed Central

Lee, Jae Dong; Park, Jong Hyuk

2014-01-01

In recent years, ubiquitous computing has been rapidly emerged in our lives and extensive studies have been conducted in a variety of areas related to smart devices, such as tablets, smartphones, smart TVs, smart refrigerators, and smart media devices, as a measure for realizing the ubiquitous computing. In particular, smartphones have significantly evolved from the traditional feature phones. Increasingly higher-end smartphone models that can perform a range of functions are now available. Smart devices have become widely popular since they provide high efficiency and great convenience for not only private daily activities but also business endeavors. Rapid advancements have been achieved in smart device technologies to improve the end users' convenience. Consequently, many people increasingly rely on smart devices to store their valuable and important data. With this increasing dependence, an important aspect that must be addressed is security issues. Leaking of private information or sensitive business data due to loss or theft of smart devices could result in exorbitant damage. To mitigate these security threats, basic embedded locking features are provided in smart devices. However, these locking features are vulnerable. In this paper, an original security-locking scheme using a rhythm-based locking system (RLS) is proposed to overcome the existing security problems of smart devices. RLS is a user-authenticated system that addresses vulnerability issues in the existing locking features and provides secure confidentiality in addition to convenience. PMID:25110743

Is adermatoglyphia an additional feature of Kindler Syndrome?*

PubMed Central

de Almeida Jr, Hiram Larangeira; Goetze, Fernanda Mendes; Fong, Kenneth; Lai-Cheong, Joey; McGrath, John

2015-01-01

A typical feature of Kindler Syndrome is skin fragility; this condition in currently classified as a form of epidermolysis bullosa. We describe a rarely reported feature of two cases, one sporadic and one familial; both patients noticed acquired adermatoglyphia. The loss of dermatoglyphics could be an additional feature of this syndrome. PMID:26375235

Systematic review of the influence of foraging habitat on red-cockaded woodpecker reproductive success.

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

Garabedian, James E.

Relationships between foraging habitat and reproductive success provide compelling evidence of the contribution of specific vegetative features to foraging habitat quality, a potentially limiting factor for many animal populations. For example, foraging habitat quality likely will gain importance in the recovery of the threatened red-cockaded woodpecker Picoides borealis (RCW) in the USA as immediate nesting constraints are mitigated. Several researchers have characterized resource selection by foraging RCWs, but emerging research linking reproductive success (e.g. clutch size, nestling and fledgling production, and group size) and foraging habitat features has yet to be synthesized. Therefore, we reviewed peer-refereed scientific literature and technicalmore » resources (e.g. books, symposia proceedings, and technical reports) that examined RCW foraging ecology, foraging habitat, or demography to evaluate evidence for effects of the key foraging habitat features described in the species’ recovery plan on group reproductive success. Fitness-based habitat models suggest foraging habitat with low to intermediate pine Pinus spp. densities, presence of large and old pines, minimal midstory development, and herbaceous groundcover support more productive RCW groups. However, the relationships between some foraging habitat features and RCW reproductive success are not well supported by empirical data. In addition, few regression models account for > 30% of variation in reproductive success, and unstandardized multiple and simple linear regression coefficient estimates typically range from -0.100 to 0.100, suggesting ancillary variables and perhaps indirect mechanisms influence reproductive success. These findings suggest additional research is needed to address uncertainty in relationships between foraging habitat features and RCW reproductive success and in the mechanisms underlying those relationships.« less

Vessel Classification in Cosmo-Skymed SAR Data Using Hierarchical Feature Selection

NASA Astrophysics Data System (ADS)

Makedonas, A.; Theoharatos, C.; Tsagaris, V.; Anastasopoulos, V.; Costicoglou, S.

2015-04-01

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

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.

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.

Brainstorming about next-generation computer-based documentation: an AMIA clinical working group survey.

PubMed

Johnson, Kevin B; Ravich, William J; Cowan, John A

2004-09-01

Computer-based software to record histories, physical exams, and progress or procedure notes, known as computer-based documentation (CBD) software, has been touted as an important addition to the electronic health record. The functionality of CBD systems has remained static over the past 30 years, which may have contributed to the limited adoption of these tools. Early users of this technology, who have tried multiple products, may have insight into important features to be considered in next-generation CBD systems. We conducted a cross-sectional, observational study of the clinical working group membership of the American Medical Informatics Association (AMIA) to generate a set of features that might improve adoption of next-generation systems. The study was conducted online over a 4-month period; 57% of the working group members completed the survey. As anticipated, CBD tool use was higher (53%) in this population than in the US physician offices. The most common methods of data entry employed keyboard and mouse, with agreement that these modalities worked well. Many respondents had experience with pre-printed data collection forms before interacting with a CBD system. Respondents noted that CBD improved their ability to document large amounts of information, allowed timely sharing of information, enhanced patient care, and enhanced medical information with other clinicians (all P < 0.001). Respondents also noted some important but absent features in CBD, including the ability to add images, get help, and generate billing information. The latest generation of CBD systems is being used successfully by early adopters, who find that these tools confer many advantages over the approaches to documentation that they replaced. These users provide insights that may improve successive generations of CBD tools. Additional surveys of CBD non-users and failed adopters will be necessary to provide other useful insights that can address barriers to the adoption of CBD by less computer literate physicians.

Design for Additive Bio-Manufacturing: From Patient-Specific Medical Devices to Rationally Designed Meta-Biomaterials.

PubMed

Zadpoor, Amir A

2017-07-25

Recent advances in additive manufacturing (AM) techniques in terms of accuracy, reliability, the range of processable materials, and commercial availability have made them promising candidates for production of functional parts including those used in the biomedical industry. The complexity-for-free feature offered by AM means that very complex designs become feasible to manufacture, while batch-size-indifference enables fabrication of fully patient-specific medical devices. Design for AM (DfAM) approaches aim to fully utilize those features for development of medical devices with substantially enhanced performance and biomaterials with unprecedented combinations of favorable properties that originate from complex geometrical designs at the micro-scale. This paper reviews the most important approaches in DfAM particularly those applicable to additive bio-manufacturing including image-based design pipelines, parametric and non-parametric designs, metamaterials, rational and computationally enabled design, topology optimization, and bio-inspired design. Areas with limited research have been identified and suggestions have been made for future research. The paper concludes with a brief discussion on the practical aspects of DfAM and the potential of combining AM with subtractive and formative manufacturing processes in so-called hybrid manufacturing processes.

Design for Additive Bio-Manufacturing: From Patient-Specific Medical Devices to Rationally Designed Meta-Biomaterials

PubMed Central

Zadpoor, Amir A.

2017-01-01

Recent advances in additive manufacturing (AM) techniques in terms of accuracy, reliability, the range of processable materials, and commercial availability have made them promising candidates for production of functional parts including those used in the biomedical industry. The complexity-for-free feature offered by AM means that very complex designs become feasible to manufacture, while batch-size-indifference enables fabrication of fully patient-specific medical devices. Design for AM (DfAM) approaches aim to fully utilize those features for development of medical devices with substantially enhanced performance and biomaterials with unprecedented combinations of favorable properties that originate from complex geometrical designs at the micro-scale. This paper reviews the most important approaches in DfAM particularly those applicable to additive bio-manufacturing including image-based design pipelines, parametric and non-parametric designs, metamaterials, rational and computationally enabled design, topology optimization, and bio-inspired design. Areas with limited research have been identified and suggestions have been made for future research. The paper concludes with a brief discussion on the practical aspects of DfAM and the potential of combining AM with subtractive and formative manufacturing processes in so-called hybrid manufacturing processes. PMID:28757572

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.

The Evaluation of High School Geography 9 and High School Geography 11 Text Books with Some Formulas of Readability

ERIC Educational Resources Information Center

Gecit, Yilmaz

2010-01-01

The purpose of this study is to evaluate readability of 9th and 11th grade geography text-books currently used in schools. As known, one of the most fundamental features in a text-book is the readability of the text by students. In addition, it is also very important that the fluency and suitability of books match age level. In this study, the…

Functional characterization of neotropical snakes peripheral blood leukocytes subsets: Linking flow cytometry cell features, microscopy images and serum corticosterone levels.

PubMed

de Carvalho, Marcelo Pires Nogueira; Queiroz-Hazarbassanov, Nicolle Gilda Teixeira; de Oliveira Massoco, Cristina; Sant'Anna, Sávio Stefanini; Lourenço, Mariana Mathias; Levin, Gabriel; Sogayar, Mari Cleide; Grego, Kathleen Fernandes; Catão-Dias, José Luiz

2017-09-01

Reptiles are the unique ectothermic amniotes, providing the key link between ectothermic anamniotes fish and amphibians, and endothermic birds and mammals; becoming an important group to study with the aim of providing significant knowledge into the evolutionary history of vertebrate immunity. Classification systems for reptiles' leukocytes have been described by their appearance rather than function, being still inconsistent. With the advent of modern techniques and the establishment of analytical protocols for snakes' blood by flow cytometry, we bring a qualitative and quantitative assessment of innate activities presented by snakes' peripheral blood leukocytes, thereby linking flow cytometric features with fluorescent and light microscopy images. Moreover, since corticosterone is an important immunomodulator in reptiles, hormone levels of all blood samples were measured. We provide novel and additional information which should contribute to better understanding of the development of the immune system of reptiles and vertebrates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Importance of cellulase cocktails favoring hydrolysis of cellulose.

PubMed

Victoria, Juliet; Odaneth, Annamma; Lali, Arvind

2017-07-03

Depolymerization of lignocellulosic biomass is catalyzed by groups of enzymes whose action is influenced by substrate features and the composition of cellulase preparation. Cellulases contain a mixture of variety of enzymes, whose proportions dictate the saccharification of biomass. In the current study, four cellulase preparation varying in their composition were used to hydrolyze two types of alkali-treated biomass (aqueous ammonia-treated rice straw and sodium hydroxide-treated rice straw) to study the effect on catalytic rate, saccharification yields, and sugar release profile. We found that substrate features affected the extent of saccharification but had minimal effect on the sugar release pattern. In addition, complete hydrolysis to glucose was observed with enzyme preparation having at least a cellobiase units (CBU)/carboxymethyl cellulose (CMC) ratio (>0.15), while a modified enzyme ratio can be used for oligosaccharide synthesis. Thus, cellulase preparation with defined ratios of the three main enzymes can improve the saccharification which is of utmost importance in defining the success of lignocellulose-based economies.

Feature Selection for Wheat Yield Prediction

NASA Astrophysics Data System (ADS)

Ruß, Georg; Kruse, Rudolf

Carrying out effective and sustainable agriculture has become an important issue in recent years. Agricultural production has to keep up with an everincreasing population by taking advantage of a field’s heterogeneity. Nowadays, modern technology such as the global positioning system (GPS) and a multitude of developed sensors enable farmers to better measure their fields’ heterogeneities. For this small-scale, precise treatment the term precision agriculture has been coined. However, the large amounts of data that are (literally) harvested during the growing season have to be analysed. In particular, the farmer is interested in knowing whether a newly developed heterogeneity sensor is potentially advantageous or not. Since the sensor data are readily available, this issue should be seen from an artificial intelligence perspective. There it can be treated as a feature selection problem. The additional task of yield prediction can be treated as a multi-dimensional regression problem. This article aims to present an approach towards solving these two practically important problems using artificial intelligence and data mining ideas and methodologies.

JOHNSON NOISE THERMOMETRY FOR DRIFT-FREE MEASUREMENTS

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

Britton Jr, Charles L; Ezell, N Dianne Bull; Roberts, Michael

In order for Johnson Noise Thermometry (JNT) to be beneficial to SMR designers, it must offer advantages beyond the current state-of-the-art technology. Comparisons to traditional RTDs and thermocouples will involve life-cycle costs, installation footprint, reliability, and accuracy. With JNT, there is additional equipment beyond what is required for the traditional RTD measurement. Therefore, the JNT-RTD system will involve additional complexity and this additional complexity must be justified. Operators will want to know that the measurement is reliable and trustworthy. It is also important that the sensor involve little, if any, additional ongoing maintenance work and that it has a lowmore » probability of causing any malfunction of the primary measurement channel. If these features can be successfully demonstrated, the JNT-RTD system could potentially save money and increase plant reliability.« less

Imaging pigment chemistry in melanocytic conjunctival lesions with pump-probe microscopy

NASA Astrophysics Data System (ADS)

Wilson, Jesse W.; Vajzovic, Lejla; Robles, Francisco E.; Cummings, Thomas J.; Mruthyunjaya, Prithvi; Warren, Warren S.

2013-03-01

We extend nonlinear pump-probe microscopy, recently demonstrated to image the microscopic distribution of eumelanin and pheomelanin in unstained skin biopsy sections, to the case of melanocytic conjunctival lesions. The microscopic distribution of pigmentation chemistry serves as a functional indicator of melanocyte activity. In these conjunctival specimens (benign nevi, primary acquired melanoses, and conjunctival melanoma), we have observed pump-probe spectroscopic signatures of eumelanin, pheomelanin, hemoglobin, and surgical ink, in addition to important structural features that differentiate benign from malignant lesions. We will also discuss prospects for an in vivo `optical biopsy' to provide additional information before having to perform invasive procedures.

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.

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.

Homomorphic encryption-based secure SIFT for privacy-preserving feature extraction

NASA Astrophysics Data System (ADS)

Hsu, Chao-Yung; Lu, Chun-Shien; Pei, Soo-Chang

2011-02-01

Privacy has received much attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario, where the server is resource-abundant and is capable of finishing the designated tasks, it is envisioned that secure media retrieval and search with privacy-preserving will be seriously treated. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first to address the problem of secure SIFT feature extraction and representation in the encrypted domain. Since all the operations in SIFT must be moved to the encrypted domain, we propose a homomorphic encryption-based secure SIFT method for privacy-preserving feature extraction and representation based on Paillier cryptosystem. In particular, homomorphic comparison is a must for SIFT feature detection but is still a challenging issue for homomorphic encryption methods. To conquer this problem, we investigate a quantization-like secure comparison strategy in this paper. Experimental results demonstrate that the proposed homomorphic encryption-based SIFT performs comparably to original SIFT on image benchmarks, while preserving privacy additionally. We believe that this work is an important step toward privacy-preserving multimedia retrieval in an environment, where privacy is a major concern.

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

NASA Astrophysics Data System (ADS)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

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.

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

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

What vehicle features are considered important when buying an automobile? An examination of driver preferences by age and gender.

PubMed

Vrkljan, Brenda H; Anaby, Dana

2011-02-01

Certain vehicle features can help drivers avoid collisions and/or protect occupants in the event of a crash, and therefore, might play an important role when deciding which vehicle to purchase. The objective of this study was to examine the importance attributed to key vehicle features (including safety) that drivers consider when buying a car and its association with age and gender. A sample of 2,002 Canadian drivers aged 18 years and older completed a survey that asked them to rank the importance of eight vehicle features if they were to purchase a vehicle (storage, mileage, safety, price, comfort, performance, design, and reliability). ANOVA tests were performed to: (a) determine if there were differences in the level of importance between features and; (b) examine the effect of age and gender on the importance attributed to these features. Of the features examined, safety and reliability were the most highly rated in terms of importance, whereas design and performance had the lowest rating. Differences in safety and performance across age groups were dependent on gender. This effect was most evident in the youngest and oldest age groups. Safety and reliability were considered the most important features. Age and gender play a significant role in explaining the importance of certain features. Targeted efforts for translating safety-related information to the youngest and oldest consumers should be emphasized due to their high collision, injury, and fatality rates. Copyright © 2011 National Safety Council and Elsevier Ltd. All rights reserved.

Can PPG be used for HRV analysis?

PubMed

Pinheiro, N; Couceiro, R; Henriques, J; Muehlsteff, J; Quintal, I; Goncalves, L; Carvalho, P

2016-08-01

Heart rate variability (HRV) represents one of the most promising markers of the autonomic nervous system (ANS) regulation. However, it requires the acquisition of the ECG signal in order to reliably detect the RR intervals, which is not always easily and comfortably available in personal health applications. Additionally, due to progress in single spot optical sensors, photoplethysmography (PPG) is an interesting alternative for heartbeat interval measurements, since it is a more convenient and a less intrusive measurement technique. Driven by the technological advances in such sensors, wrist-worn devices are becoming a commodity, and the interest in the assessment of HRV indexes from the PPG analysis (pulse rate variability - PRV) is rising. In this study, we investigate the hypothesis of using PRV features as surrogates for HRV indexes, in three different contexts: healthy subjects at rest, healthy subjects after physical exercise and subjects with cardiovascular diseases (CVD). Additionally, we also evaluate which are the characteristic points better suited for PRV analysis in these contexts, i.e. the PPG waveform characteristic points leading to the PRV features that present the best estimates of HRV (correlation and error analysis). The achieved results suggest that the PRV can be often used as an alternative for HRV analysis in healthy subjects, with significant correlations above 82%, for both time and frequency features. Contrarily, in the post-exercise and CVD subjects, time and (most importantly) frequency domain features shall be used with caution (mean correlations ranging from 68% to 88%).

Improved explanation of human intelligence using cortical features with second order moments and regression.

PubMed

Park, Hyunjin; Yang, Jin-ju; Seo, Jongbum; Choi, Yu-yong; Lee, Kun-ho; Lee, Jong-min

2014-04-01

Cortical features derived from magnetic resonance imaging (MRI) provide important information to account for human intelligence. Cortical thickness, surface area, sulcal depth, and mean curvature were considered to explain human intelligence. One region of interest (ROI) of a cortical structure consisting of thousands of vertices contained thousands of measurements, and typically, one mean value (first order moment), was used to represent a chosen ROI, which led to a potentially significant loss of information. We proposed a technological improvement to account for human intelligence in which a second moment (variance) in addition to the mean value was adopted to represent a chosen ROI, so that the loss of information would be less severe. Two computed moments for the chosen ROIs were analyzed with partial least squares regression (PLSR). Cortical features for 78 adults were measured and analyzed in conjunction with the full-scale intelligence quotient (FSIQ). Our results showed that 45% of the variance of the FSIQ could be explained using the combination of four cortical features using two moments per chosen ROI. Our results showed improvement over using a mean value for each ROI, which explained 37% of the variance of FSIQ using the same set of cortical measurements. Our results suggest that using additional second order moments is potentially better than using mean values of chosen ROIs for regression analysis to account for human intelligence. Copyright © 2014 Elsevier Ltd. All rights reserved.

Physical Features of Visual Images Affect Macaque Monkey’s Preference for These Images

PubMed Central

Funahashi, Shintaro

2016-01-01

Animals exhibit different degrees of preference toward various visual stimuli. In addition, it has been shown that strongly preferred stimuli can often act as a reward. The aim of the present study was to determine what features determine the strength of the preference for visual stimuli in order to examine neural mechanisms of preference judgment. We used 50 color photographs obtained from the Flickr Material Database (FMD) as original stimuli. Four macaque monkeys performed a simple choice task, in which two stimuli selected randomly from among the 50 stimuli were simultaneously presented on a monitor and monkeys were required to choose either stimulus by eye movements. We considered that the monkeys preferred the chosen stimulus if it continued to look at the stimulus for an additional 6 s and calculated a choice ratio for each stimulus. Each monkey exhibited a different choice ratio for each of the original 50 stimuli. They tended to select clear, colorful and in-focus stimuli. Complexity and clarity were stronger determinants of preference than colorfulness. Images that included greater amounts of spatial frequency components were selected more frequently. These results indicate that particular physical features of the stimulus can affect the strength of a monkey’s preference and that the complexity, clarity and colorfulness of the stimulus are important determinants of this preference. Neurophysiological studies would be needed to examine whether these features of visual stimuli produce more activation in neurons that participate in this preference judgment. PMID:27853424

Visual Pattern Analysis in Histopathology Images Using Bag of Features

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Caicedo, Juan C.; González, Fabio A.

This paper presents a framework to analyse visual patterns in a collection of medical images in a two stage procedure. First, a set of representative visual patterns from the image collection is obtained by constructing a visual-word dictionary under a bag-of-features approach. Second, an analysis of the relationships between visual patterns and semantic concepts in the image collection is performed. The most important visual patterns for each semantic concept are identified using correlation analysis. A matrix visualization of the structure and organization of the image collection is generated using a cluster analysis. The experimental evaluation was conducted on a histopathology image collection and results showed clear relationships between visual patterns and semantic concepts, that in addition, are of easy interpretation and understanding.

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.

Phenotypic and genetic heterogeneity within biofilms with particular emphasis on persistence and antimicrobial tolerance.

PubMed

Sadiq, Faizan A; Flint, Steve; Li, YanJun; Ou, Kai; Yuan, Lei; He, Guo Qing

2017-09-01

Phenotypic changes or phase variation within biofilms is an important feature of bacterial dormant life. Enhanced resistance to antimicrobials is one of the distinct features displayed by a fraction of cells within biofilms. It is believed that persisters are mainly responsible for this phenotypic heterogeneity. However, there is still an unresolved debate on the formation of persisters. In this short review, we highlight all known genomic and proteomic changes encountered by bacterial cells within biofilms. We have also described all phenotypic changes displayed by bacterial cells within biofilms with particular emphasis on enhanced antimicrobial tolerance of biofilms with particular reference to persisters. In addition, all currently known models of persistence have been succinctly discussed.

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.

A semisupervised support vector regression method to estimate biophysical parameters from remotely sensed images

NASA Astrophysics Data System (ADS)

Castelletti, Davide; Demir, Begüm; Bruzzone, Lorenzo

2014-10-01

This paper presents a novel semisupervised learning (SSL) technique defined in the context of ɛ-insensitive support vector regression (SVR) to estimate biophysical parameters from remotely sensed images. The proposed SSL method aims to mitigate the problems of small-sized biased training sets without collecting any additional samples with reference measures. This is achieved on the basis of two consecutive steps. The first step is devoted to inject additional priors information in the learning phase of the SVR in order to adapt the importance of each training sample according to distribution of the unlabeled samples. To this end, a weight is initially associated to each training sample based on a novel strategy that defines higher weights for the samples located in the high density regions of the feature space while giving reduced weights to those that fall into the low density regions of the feature space. Then, in order to exploit different weights for training samples in the learning phase of the SVR, we introduce a weighted SVR (WSVR) algorithm. The second step is devoted to jointly exploit labeled and informative unlabeled samples for further improving the definition of the WSVR learning function. To this end, the most informative unlabeled samples that have an expected accurate target values are initially selected according to a novel strategy that relies on the distribution of the unlabeled samples in the feature space and on the WSVR function estimated at the first step. Then, we introduce a restructured WSVR algorithm that jointly uses labeled and unlabeled samples in the learning phase of the WSVR algorithm and tunes their importance by different values of regularization parameters. Experimental results obtained for the estimation of single-tree stem volume show the effectiveness of the proposed SSL method.

From tiger to panda: animal head detection.

PubMed

Zhang, Weiwei; Sun, Jian; Tang, Xiaoou

2011-06-01

Robust object detection has many important applications in real-world online photo processing. For example, both Google image search and MSN live image search have integrated human face detector to retrieve face or portrait photos. Inspired by the success of such face filtering approach, in this paper, we focus on another popular online photo category--animal, which is one of the top five categories in the MSN live image search query log. As a first attempt, we focus on the problem of animal head detection of a set of relatively large land animals that are popular on the internet, such as cat, tiger, panda, fox, and cheetah. First, we proposed a new set of gradient oriented feature, Haar of Oriented Gradients (HOOG), to effectively capture the shape and texture features on animal head. Then, we proposed two detection algorithms, namely Bruteforce detection and Deformable detection, to effectively exploit the shape feature and texture feature simultaneously. Experimental results on 14,379 well labeled animals images validate the superiority of the proposed approach. Additionally, we apply the animal head detector to improve the image search result through text based online photo search result filtering.

LBT Distributed Archive: Status and Features

NASA Astrophysics Data System (ADS)

Knapic, C.; Smareglia, R.; Thompson, D.; Grede, G.

2011-07-01

After the first release of the LBT Distributed Archive, this successful collaboration is continuing within the LBT corporation. The IA2 (Italian Center for Astronomical Archive) team had updated the LBT DA with new features in order to facilitate user data retrieval while abiding by VO standards. To facilitate the integration of data from any new instruments, we have migrated to a new database, developed new data distribution software, and enhanced features in the LBT User Interface. The DBMS engine has been changed to MySQL. Consequently, the data handling software now uses java thread technology to update and synchronize the main storage archives on Mt. Graham and in Tucson, as well as archives in Trieste and Heidelberg, with all metadata and proprietary data. The LBT UI has been updated with additional features allowing users to search by instrument and some of the more important characteristics of the images. Finally, instead of a simple cone search service over all LBT image data, new instrument specific SIAP and cone search services have been developed. They will be published in the IVOA framework later this fall.

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.

The impact of jackpots on EGM gambling behavior: a review.

PubMed

Rockloff, Matthew J; Hing, Nerilee

2013-12-01

This paper reviews literature on how jackpots influence Electronic Gaming Machine (EGM) gambling behaviour. Most of the available evidence addresses the motivational effect of the mere presence of EGM jackpots on play, as actual wins are relatively rare for individual gamblers. The review identifies a distinction between rational, biased and irrational motivations that attract people to EGM jackpots. The evidence suggests that EGM jackpots should generate additional consumption on EGMs above machines that do not have such lottery-like features. Rational motivations are likely to lead to consumer surplus, whereas biased and irrational motivations are likely to contribute to excessive consumption. Moreover, there is evidence that excessive gambling consumption is strongly associated with gambling-related harm. Future research should identify how the structural features of different types of jackpots; such as progressive, deterministic, hidden, mystery, linked and wide-area jackpots; may differentially appeal to rational, biased and irrational gambling motivations. Jackpots are common feature of EGM games, and therefore it is important to have a better understanding of how jackpot features influence play on the machines.

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.

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

Reverberation effects on directionality and response of stationary monopole and dipole sources in a wind tunnel

NASA Technical Reports Server (NTRS)

Baumeister, K. J.

1985-01-01

Analytical solutions for the three dimensional inhomogeneous wave equation with flow in a hardwall rectangular wind tunnel and in the free field are presented for a stationary monopole noise source. Dipole noise sources are calculated by combining two monopoles 180 deg out of phase. Numerical calculations for the modal content, spectral response and directivity for both monopole and dipole sources are presented. In addition, the effect of tunnel alterations, such as the addition of a mounting plate, on the tunnels reverberant response are considered. In the frequency range of practical importance for the turboprop response, important features of the free field directivity can be approximated in a hardwall wind tunnel with flow if the major lobe of the noise source is not directed upstream. However, for an omnidirectional source, such as a monopole, the hardwall wind tunnel and free field response are not comparable.

Allele quantification using molecular inversion probes (MIP)

PubMed Central

Wang, Yuker; Moorhead, Martin; Karlin-Neumann, George; Falkowski, Matthew; Chen, Chunnuan; Siddiqui, Farooq; Davis, Ronald W.; Willis, Thomas D.; Faham, Malek

2005-01-01

Detection of genomic copy number changes has been an important research area, especially in cancer. Several high-throughput technologies have been developed to detect these changes. Features that are important for the utility of technologies assessing copy number changes include the ability to interrogate regions of interest at the desired density as well as the ability to differentiate the two homologs. In addition, assessing formaldehyde fixed and paraffin embedded (FFPE) samples allows the utilization of the vast majority of cancer samples. To address these points we demonstrate the use of molecular inversion probe (MIP) technology to the study of copy number. MIP is a high-throughput genotyping technology capable of interrogating >20 000 single nucleotide polymorphisms in the same tube. We have shown the ability of MIP at this multiplex level to provide copy number measurements while obtaining the allele information. In addition we have demonstrated a proof of principle for copy number analysis in FFPE samples. PMID:16314297

Fullerene-like chemistry at the interior carbon atoms of an alkene-centered C26H12 geodesic polyarene.

PubMed

Bronstein, Hindy E; Scott, Lawrence T

2008-01-04

The title compound (1) undergoes 1,2-addition reactions of both electrophilic and nucleophilic reagents preferentially at the "interior" carbon atoms of the central 6:6-bond to give fullerene-type adducts 2, 3, 4, and 5. Such fullerene-like chemistry is unprecedented for a topologically 2-dimensional polycyclic aromatic hydrocarbon and qualifies this geodesic polyarene as a "bridge" between the old flat world of polycyclic aromatic hydrocarbons (PAHs) and the new round world of fullerenes. The relief of pyramidalization strain, as in the addition reactions of fullerenes, presumably contributes to the atypical mode of reactivity seen in 1. Molecular orbital calculations, however, reveal features of the nonalternant pi system in 1 that may also play an important role. Thus, the fullerene-like chemistry of 1 may be driven by two or more factors, the relative importances of which are difficult to discern.

Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text

PubMed Central

Xin, Yu; Hochberg, Ephraim; Joshi, Rohit; Uzuner, Ozlem; Szolovits, Peter

2015-01-01

Objective Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability. Methods The authors introduce a novel framework named subgraph augmented non-negative tensor factorization (SANTF). In addition to relying on atomic features (e.g., words in clinical narrative text), SANTF automatically mines higher-order features (e.g., relations of lymphoid cells expressing antigens) from clinical narrative text by converting sentences into a graph representation and identifying important subgraphs. The authors compose a tensor using patients, higher-order features, and atomic features as its respective modes. We then apply non-negative tensor factorization to cluster patients, and simultaneously identify latent groups of higher-order features that link to patient clusters, as in clinical guidelines where a panel of immunophenotypic features and laboratory results are used to specify diagnostic criteria. Results and Conclusion SANTF demonstrated over 10% improvement in averaged F-measure on patient clustering compared to widely used non-negative matrix factorization (NMF) and k-means clustering methods. Multiple baselines were established by modeling patient data using patient-by-features matrices with different feature configurations and then performing NMF or k-means to cluster patients. Feature analysis identified latent groups of higher-order features that lead to medical insights. We also found that the latent groups of atomic features help to better correlate the latent groups of higher-order features. PMID:25862765

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.

Species-environment relationships and potential for distribution modelling in coastal waters

NASA Astrophysics Data System (ADS)

Snickars, M.; Gullström, M.; Sundblad, G.; Bergström, U.; Downie, A.-L.; Lindegarth, M.; Mattila, J.

2014-01-01

Due to increasing pressure on the marine environment there is a growing need to understand species-environment relationships. To provide background for prioritising among variables (predictors) for use in distribution models, the relevance of predictors for benthic species was reviewed using the coastal Baltic Sea as a case-study area. Significant relationships for three response groups (fish, macroinvertebrates, macrovegetation) and six predictor categories (bottom topography, biotic features, hydrography, wave exposure, substrate and spatiotemporal variability) were extracted from 145 queried peer-reviewed field-studies covering three decades and six subregions. In addition, the occurrence of interaction among predictors was analysed. Hydrography was most often found in significant relationships, had low level of interaction with other predictors, but also had the most non-significant relationships. Depth and wave exposure were important in all subregions and are readily available, increasing their applicability for cross-regional modelling efforts. Otherwise, effort to model species distributions may prove challenging at larger scale as the relevance of predictors differed among both response groups and regions. Fish and hard bottom macrovegetation have the largest modelling potential, as they are structured by a set of predictors that at the same time are accurately mapped. A general importance of biotic features implies that these need to be accounted for in distribution modelling, but the mapping of most biotic features is challenging, which currently lowers the applicability. The presence of interactions suggests that predictive methods allowing for interactive effects are preferable. Detailing these complexities is important for future distribution modelling.

Predicting couple therapy outcomes based on speech acoustic features

PubMed Central

Nasir, Md; Baucom, Brian Robert; Narayanan, Shrikanth

2017-01-01

Automated assessment and prediction of marital outcome in couples therapy is a challenging task but promises to be a potentially useful tool for clinical psychologists. Computational approaches for inferring therapy outcomes using observable behavioral information obtained from conversations between spouses offer objective means for understanding relationship dynamics. In this work, we explore whether the acoustics of the spoken interactions of clinically distressed spouses provide information towards assessment of therapy outcomes. The therapy outcome prediction task in this work includes detecting whether there was a relationship improvement or not (posed as a binary classification) as well as discerning varying levels of improvement or decline in the relationship status (posed as a multiclass recognition task). We use each interlocutor’s acoustic speech signal characteristics such as vocal intonation and intensity, both independently and in relation to one another, as cues for predicting the therapy outcome. We also compare prediction performance with one obtained via standardized behavioral codes characterizing the relationship dynamics provided by human experts as features for automated classification. Our experiments, using data from a longitudinal clinical study of couples in distressed relations, showed that predictions of relationship outcomes obtained directly from vocal acoustics are comparable or superior to those obtained using human-rated behavioral codes as prediction features. In addition, combining direct signal-derived features with manually coded behavioral features improved the prediction performance in most cases, indicating the complementarity of relevant information captured by humans and machine algorithms. Additionally, considering the vocal properties of the interlocutors in relation to one another, rather than in isolation, showed to be important for improving the automatic prediction. This finding supports the notion that behavioral outcome, like many other behavioral aspects, is closely related to the dynamics and mutual influence of the interlocutors during their interaction and their resulting behavioral patterns. PMID:28934302

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.

Conserved patterns of incomplete reporting in pre-vaccine era childhood diseases

PubMed Central

Gunning, Christian E.; Erhardt, Erik; Wearing, Helen J.

2014-01-01

Incomplete observation is an important yet often neglected feature of observational ecological timeseries. In particular, observational case report timeseries of childhood diseases have played an important role in the formulation of mechanistic dynamical models of populations and metapopulations. Yet to our knowledge, no comprehensive study of childhood disease reporting probabilities (commonly referred to as reporting rates) has been conducted to date. Here, we provide a detailed analysis of measles and whooping cough reporting probabilities in pre-vaccine United States cities and states, as well as measles in cities of England and Wales. Overall, we find the variability between locations and diseases greatly exceeds that between methods or time periods. We demonstrate a strong relationship within location between diseases and within disease between geographical areas. In addition, we find that demographic covariates such as ethnic composition and school attendance explain a non-trivial proportion of reporting probability variation. Overall, our findings show that disease reporting is both variable and non-random and that completeness of reporting is influenced by disease identity, geography and socioeconomic factors. We suggest that variations in incomplete observation can be accounted for and that doing so can reveal ecologically important features that are otherwise obscured. PMID:25232131

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.

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

Secure Service Proxy: A CoAP(s) Intermediary for a Securer and Smarter Web of Things

PubMed Central

Van den Abeele, Floris; Moerman, Ingrid; Demeester, Piet

2017-01-01

As the IoT continues to grow over the coming years, resource-constrained devices and networks will see an increase in traffic as everything is connected in an open Web of Things. The performance- and function-enhancing features are difficult to provide in resource-constrained environments, but will gain importance if the WoT is to be scaled up successfully. For example, scalable open standards-based authentication and authorization will be important to manage access to the limited resources of constrained devices and networks. Additionally, features such as caching and virtualization may help further reduce the load on these constrained systems. This work presents the Secure Service Proxy (SSP): a constrained-network edge proxy with the goal of improving the performance and functionality of constrained RESTful environments. Our evaluations show that the proposed design reaches its goal by reducing the load on constrained devices while implementing a wide range of features as different adapters. Specifically, the results show that the SSP leads to significant savings in processing, network traffic, network delay and packet loss rates for constrained devices. As a result, the SSP helps to guarantee the proper operation of constrained networks as these networks form an ever-expanding Web of Things. PMID:28696393

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.

Secure Service Proxy: A CoAP(s) Intermediary for a Securer and Smarter Web of Things.

PubMed

Van den Abeele, Floris; Moerman, Ingrid; Demeester, Piet; Hoebeke, Jeroen

2017-07-11

As the IoT continues to grow over the coming years, resource-constrained devices and networks will see an increase in traffic as everything is connected in an open Web of Things. The performance- and function-enhancing features are difficult to provide in resource-constrained environments, but will gain importance if the WoT is to be scaled up successfully. For example, scalable open standards-based authentication and authorization will be important to manage access to the limited resources of constrained devices and networks. Additionally, features such as caching and virtualization may help further reduce the load on these constrained systems. This work presents the Secure Service Proxy (SSP): a constrained-network edge proxy with the goal of improving the performance and functionality of constrained RESTful environments. Our evaluations show that the proposed design reaches its goal by reducing the load on constrained devices while implementing a wide range of features as different adapters. Specifically, the results show that the SSP leads to significant savings in processing, network traffic, network delay and packet loss rates for constrained devices. As a result, the SSP helps to guarantee the proper operation of constrained networks as these networks form an ever-expanding Web of Things.

A Grignard-like Organic Reaction in Water

NASA Astrophysics Data System (ADS)

Breton, Gary W.; Hughey, Christine A.

1998-01-01

The addition of a Grignard reagent to a carbonyl-containing compound to form an alcohol is an important reaction to demonstrate in organic chemistry laboratory courses. However, the reaction presents several practical problems for the lab instructor including the need for anhydrous solvents (e.g., ether), dry glassware, and the occasional problem of slow reaction initiation. We have scaled, and tested, a known Grignard-like reaction between allyl bromide and benzaldehyde mediated by zinc metal in aqueous media. The procedure retains the desirable features of the traditional Grignard reaction, while eliminating some of the commonly encountered difficulties. Thus, addition of allyl bromide (1.2 eq) to benzaldehyde and zinc in a two-phase mixture of THF and saturated aqueous NH4Cl afforded addition product 1-phenyl-3-buten-1-ol in 70-85% yields.

The IRM fluxgate magnetometer

NASA Technical Reports Server (NTRS)

Luehr, H.; Kloecker, N.; Oelschlaegel, W.; Haeusler, B.; Acuna, M.

1985-01-01

This report describes the three-axis fluxgate magnetometer instrument on board the AMPTE IRM spacecraft. Important features of the instrument are its wide dynamic range (0.1-60,000 nT), a high resolution (16-bit analog to digital conversion) and the capability to operate automatically or via telecommand in two gain states. In addition, the wave activity is monitored in all three components up to 50 Hz. Inflight checkout proved the nominal functioning of the instrument in all modes.

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.

76 FR 10482 - Special Conditions: Boeing Model 787-8 Airplane; Overhead Flightcrew-Rest Compartment Occupiable...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-25

... Boeing Model 787-8 airplane. This airplane will have novel or unusual design features associated with an... standards for this design feature. These special conditions contain the additional safety standards that the... airworthiness standards. Additional special conditions will be issued for other novel or unusual design features...

Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features

PubMed Central

Zhang, Yaoyun; Jiang, Min; Wang, Jingqi; Xu, Hua

2016-01-01

Semantic role labeling (SRL), which extracts shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding clinical narratives. Since semantic roles are formed by syntactic constituents in the sentence, an effective parser, as well as an effective syntactic feature set are essential to build a practical SRL system. Our study initiates a formal evaluation and comparison of SRL performance on a clinical text corpus MiPACQ, using three state-of-the-art parsers, the Stanford parser, the Berkeley parser, and the Charniak parser. First, the original parsers trained on the open domain syntactic corpus Penn Treebank were employed. Next, those parsers were retrained on the clinical Treebank of MiPACQ for further comparison. Additionally, state-of-the-art syntactic features from open domain SRL were also examined for clinical text. Experimental results showed that retraining the parsers on clinical Treebank improved the performance significantly, with an optimal F1 measure of 71.41% achieved by the Berkeley parser. PMID:28269926

Interpretation of fingerprint image quality features extracted by self-organizing maps

NASA Astrophysics Data System (ADS)

Danov, Ivan; Olsen, Martin A.; Busch, Christoph

2014-05-01

Accurate prediction of fingerprint quality is of significant importance to any fingerprint-based biometric system. Ensuring high quality samples for both probe and reference can substantially improve the system's performance by lowering false non-matches, thus allowing finer adjustment of the decision threshold of the biometric system. Furthermore, the increasing usage of biometrics in mobile contexts demands development of lightweight methods for operational environment. A novel two-tier computationally efficient approach was recently proposed based on modelling block-wise fingerprint image data using Self-Organizing Map (SOM) to extract specific ridge pattern features, which are then used as an input to a Random Forests (RF) classifier trained to predict the quality score of a propagated sample. This paper conducts an investigative comparative analysis on a publicly available dataset for the improvement of the two-tier approach by proposing additionally three feature interpretation methods, based respectively on SOM, Generative Topographic Mapping and RF. The analysis shows that two of the proposed methods produce promising results on the given dataset.

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 .

The Function of Embryonic Stem Cell-expressed RAS (E-RAS), a Unique RAS Family Member, Correlates with Its Additional Motifs and Its Structural Properties.

PubMed

Nakhaei-Rad, Saeideh; Nakhaeizadeh, Hossein; Kordes, Claus; Cirstea, Ion C; Schmick, Malte; Dvorsky, Radovan; Bastiaens, Philippe I H; Häussinger, Dieter; Ahmadian, Mohammad Reza

2015-06-19

E-RAS is a member of the RAS family specifically expressed in embryonic stem cells, gastric tumors, and hepatic stellate cells. Unlike classical RAS isoforms (H-, N-, and K-RAS4B), E-RAS has, in addition to striking and remarkable sequence deviations, an extended 38-amino acid-long unique N-terminal region with still unknown functions. We investigated the molecular mechanism of E-RAS regulation and function with respect to its sequence and structural features. We found that N-terminal extension of E-RAS is important for E-RAS signaling activity. E-RAS protein most remarkably revealed a different mode of effector interaction as compared with H-RAS, which correlates with deviations in the effector-binding site of E-RAS. Of all these residues, tryptophan 79 (arginine 41 in H-RAS), in the interswitch region, modulates the effector selectivity of RAS proteins from H-RAS to E-RAS features. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

External Standards or Standard Addition? Selecting and Validating a Method of Standardization

NASA Astrophysics Data System (ADS)

Harvey, David T.

2002-05-01

A common feature of many problem-based laboratories in analytical chemistry is a lengthy independent project involving the analysis of "real-world" samples. Students research the literature, adapting and developing a method suitable for their analyte, sample matrix, and problem scenario. Because these projects encompass the complete analytical process, students must consider issues such as obtaining a representative sample, selecting a method of analysis, developing a suitable standardization, validating results, and implementing appropriate quality assessment/quality control practices. Most textbooks and monographs suitable for an undergraduate course in analytical chemistry, however, provide only limited coverage of these important topics. The need for short laboratory experiments emphasizing important facets of method development, such as selecting a method of standardization, is evident. The experiment reported here, which is suitable for an introductory course in analytical chemistry, illustrates the importance of matrix effects when selecting a method of standardization. Students also learn how a spike recovery is used to validate an analytical method, and obtain a practical experience in the difference between performing an external standardization and a standard addition.

Segmentation of photospheric magnetic elements corresponding to coronal features to understand the EUV and UV irradiance variability

NASA Astrophysics Data System (ADS)

Zender, J. J.; Kariyappa, R.; Giono, G.; Bergmann, M.; Delouille, V.; Damé, L.; Hochedez, J.-F.; Kumara, S. T.

2017-09-01

Context. The magnetic field plays a dominant role in the solar irradiance variability. Determining the contribution of various magnetic features to this variability is important in the context of heliospheric studies and Sun-Earth connection. Aims: We studied the solar irradiance variability and its association with the underlying magnetic field for a period of five years (January 2011-January 2016). We used observations from the Large Yield Radiometer (LYRA), the Sun Watcher with Active Pixel System detector and Image Processing (SWAP) on board PROBA2, the Atmospheric Imaging Assembly (AIA), and the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). Methods: The Spatial Possibilistic Clustering Algorithm (SPoCA) is applied to the extreme ultraviolet (EUV) observations obtained from the AIA to segregate coronal features by creating segmentation maps of active regions (ARs), coronal holes (CHs) and the quiet sun (QS). Further, these maps are applied to the full-disk SWAP intensity images and the full-disk (FD) HMI line-of-sight (LOS) magnetograms to isolate the SWAP coronal features and photospheric magnetic counterparts, respectively. We then computed full-disk and feature-wise averages of EUV intensity and line of sight (LOS) magnetic flux density over ARs/CHs/QS/FD. The variability in these quantities is compared with that of LYRA irradiance values. Results: Variations in the quantities resulting from the segmentation, namely the integrated intensity and the total magnetic flux density of ARs/CHs/QS/FD regions, are compared with the LYRA irradiance variations. We find that the EUV intensity over ARs/CHs/QS/FD is well correlated with the underlying magnetic field. In addition, variations in the full-disk integrated intensity and magnetic flux density values are correlated with the LYRA irradiance variations. Conclusions: Using the segmented coronal features observed in the EUV wavelengths as proxies to isolate the underlying magnetic structures is demonstrated in this study. Sophisticated feature identification and segmentation tools are important in providing more insights into the role of various magnetic features in both the short- and long-term changes in the solar irradiance. The movie associated to Fig. 2 is available at http://www.aanda.org

How Structure Defines Affinity in Protein-Protein Interactions

PubMed Central

Erijman, Ariel; Rosenthal, Eran; Shifman, Julia M.

2014-01-01

Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins. PMID:25329579

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update.

PubMed

Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

2016-04-15

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the "good" models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm.

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

PubMed Central

Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

2016-01-01

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm. PMID:27092505

Enantioselective Total Syntheses of FR901464 and Spliceostatin A and Evaluation of Splicing Activity of Key Derivatives

PubMed Central

2015-01-01

FR901464 (1) and spliceostatin A (2) are potent inhibitors of spliceosomes. These compounds have shown remarkable anticancer activity against multiple human cancer cell lines. Herein, we describe efficient, enantioselective syntheses of FR901464, spliceostatin A, six corresponding diastereomers and an evaluation of their splicing activity. Syntheses of spliceostatin A and FR901464 were carried out in the longest linear sequence of 9 and 10 steps, respectively. To construct the highly functionalized tetrahydropyran A-ring, we utilized CBS reduction, Achmatowicz rearrangement, Michael addition, and reductive amination as key steps. The remarkable diastereoselectivity of the Michael addition was specifically demonstrated with different substrates under various reaction conditions. The side chain B was prepared from an optically active alcohol, followed by acetylation and hydrogenation over Lindlar’s catalyst. The other densely functionalized tetrahydropyran C-ring was derived from readily available (R)-isopropylidene glyceraldehyde through a route featuring 1,2-addition, cyclic ketalization, and regioselective epoxidation. These fragments were coupled together at a late stage through amidation and cross-metathesis in a convergent manner. Six key diastereomers were then synthesized to probe the importance of specific stereochemical features of FR901464 and spliceostatin A, with respect to their in vitro splicing activity. PMID:24873648

[Fibrohistiocytic tumors of the skin: a heterogeneous group of superficially located mesenchymal neoplasms].

PubMed

Mentzel, T

2015-02-01

So-called fibrohistiocytic tumors of the skin comprise a heterogeneous spectrum of superficially located neoplasms that often show fibroblastic and/or myofibroblastic differentiation. In this review clinicopathologically important variants of dermatofibroma and dermatofibrosarcoma protuberans and their differential diagnoses are discussed in detail. In addition, the clinicopathological features of atypical fibroxanthoma, angiomatoid fibrous histiocytoma, plexiform fibrohistiocytic tumors and pleomorphic dermal sarcoma are presented. Entities that have to be considered in the differential diagnosis are also mentioned.

Nonstate Security Threats in Africa: Challenges for U.S. Engagement

DTIC Science & Technology

2010-12-01

PRISM 2, no. 1 FeatuReS | 67 East Asia has led to a spike in elephant poach - ing. In June 2002, Singapore seized 6.2 metric tons of ivory (worth...country with no functioning cen- tral government since the 1980s.23 In addition to the impact on the lives and families of sailors who are taken...imports by 2015,25 and militant attacks impacting West African oil already affect the price of oil on global stock markets. The role played by

Different types of osteochondrodysplasia in a consecutive series of newborns.

PubMed

Gustavson, K H; Jorulf, H

1975-10-01

Among 14816 consecutive live births there were 7 cases of osteochondrodysplasia (incidence 1:2117). In addition there was 1 case of stippled epiphyses, possibly induced by anticonvulsive drugs taken by the mother during the pregnancy, and one case of cerebro-hepato-renal syndrome of Zellweger with roentgenological features similar to those of hypochondrodysplasia. None of 102 stillborns seen at the same time had osteochondrodysplasia. For genetic counselling an exact diagnosis is mandatory, radiological examination of the skeleton is often of decisive importance.

Section 905(B) WRDA 86, Reconnaissance Study of Ecosystem Restoration for the Clinton River and Anchor Bay Watersheds, Macomb County and St. Clair County, Michigan

DTIC Science & Technology

2012-07-01

water quality, potential for growth of invasive species , and fish and wildlife habitat . Clinton River and Anchor Bay Watersheds Reconnaissance...However, the study area provides important habitat for many rare species , with the most abundant being wooded areas. In addition, the study area features...animal life and provide spawning grounds for fish. These areas provide habitat for numerous species , including rare species such as black- crowned

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.

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640

Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements

PubMed Central

Blumrosen, Gaddi; Luttwak, Ami

2013-01-01

Acquisition of patient kinematics in different environments plays an important role in the detection of risk situations such as fall detection in elderly patients, in rehabilitation of patients with injuries, and in the design of treatment plans for patients with neurological diseases. Received Signal Strength Indicator (RSSI) measurements in a Body Area Network (BAN), capture the signal power on a radio link. The main aim of this paper is to demonstrate the potential of utilizing RSSI measurements in assessment of human kinematic features, and to give methods to determine these features. RSSI measurements can be used for tracking different body parts' displacements on scales of a few centimeters, for classifying motion and gait patterns instead of inertial sensors, and to serve as an additional reference to other sensors, in particular inertial sensors. Criteria and analytical methods for body part tracking, kinematic motion feature extraction, and a Kalman filter model for aggregation of RSSI and inertial sensor were derived. The methods were verified by a set of experiments performed in an indoor environment. In the future, the use of RSSI measurements can help in continuous assessment of various kinematic features of patients during their daily life activities and enhance medical diagnosis accuracy with lower costs. PMID:23979481

Human body parts tracking and kinematic features assessment based on RSSI and inertial sensor measurements.

PubMed

Blumrosen, Gaddi; Luttwak, Ami

2013-08-23

Acquisition of patient kinematics in different environments plays an important role in the detection of risk situations such as fall detection in elderly patients, in rehabilitation of patients with injuries, and in the design of treatment plans for patients with neurological diseases. Received Signal Strength Indicator (RSSI) measurements in a Body Area Network (BAN), capture the signal power on a radio link. The main aim of this paper is to demonstrate the potential of utilizing RSSI measurements in assessment of human kinematic features, and to give methods to determine these features. RSSI measurements can be used for tracking different body parts' displacements on scales of a few centimeters, for classifying motion and gait patterns instead of inertial sensors, and to serve as an additional reference to other sensors, in particular inertial sensors. Criteria and analytical methods for body part tracking, kinematic motion feature extraction, and a Kalman filter model for aggregation of RSSI and inertial sensor were derived. The methods were verified by a set of experiments performed in an indoor environment. In the future, the use of RSSI measurements can help in continuous assessment of various kinematic features of patients during their daily life activities and enhance medical diagnosis accuracy with lower costs.

Advanced alerting features: displaying new relevant data and retracting alerts.

PubMed Central

Kuperman, G. J.; Hiltz, F. L.; Teich, J. M.

1997-01-01

We added two advanced features to our automated alerting system. The first feature identifies and displays, at the time an alert is reviewed, relevant data filed between the login time of a specimen leading to an alerting result and the time the alert is reviewed. Relevant data is defined as data of the same kind as generated the alert. The other feature retracts alerts when the alerting value is edited and no longer satisfies the alerting criteria. We evaluated the two features for a 14-week period (new relevant data) and a 6-week period (retraction). Of a total of 1104 alerts in the 14-week evaluation, 286 (25.9%) had new relevant data displayed at alert review time. Of the 286, 75.2% were due to additions of comments to the original piece of alerting data; 24.1% were due to new or pending laboratory results of the same type that generated the alert. Two alerts (out of 490) were retracted in a 6 week period. We conclude that in our system, new clinically relevant data is often added between the time of specimen login and the time that an alerting result from that specimen is reviewed. Retractions occur rarely but are important to detect and communicate. PMID:9357625

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

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

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.

Geological and Structural Patterns on Titan Enhanced Through Cassini's SAR PCA and High-Resolution Radiometry

NASA Astrophysics Data System (ADS)

Paganelli, F.; Schubert, G.; Lopes, R. M. C.; Malaska, M.; Le Gall, A. A.; Kirk, R. L.

2016-12-01

The current SAR data coverage on Titan encompasses several areas in which multiple radar passes are present and overlapping, providing additional information to aid the interpretation of geological and structural features. We exploit the different combinations of look direction and variable incidence angle to examine Cassini Synthetic Aperture RADAR (SAR) data using the Principal Component Analysis (PCA) technique and high-resolution radiometry, as a tool to aid in the interpretation of geological and structural features. Look direction and variable incidence angle is of particular importance in the analysis of variance in the images, which aid in the perception and identification of geological and structural features, as extensively demonstrated in Earth and planetary examples. The PCA enhancement technique uses projected non-ortho-rectified SAR imagery in order to maintain the inherent differences in scattering and geometric properties due to the different look directions, while enhancing the geometry of surface features. The PC2 component provides a stereo view of the areas in which complex surface features and structural patterns can be enhanced and outlined. We focus on several areas of interest, in older and recently acquired flybys, in which evidence of geological and structural features can be enhanced and outlined in the PC1 and PC2 components. Results of this technique provide enhanced geometry and insights into the interpretation of the observed geological and structural features, thus allowing a better understanding towards the geology and tectonics on Titan.

The interaction of feature and space based orienting within the attention set.

PubMed

Lim, Ahnate; Sinnett, Scott

2014-01-01

The processing of sensory information relies on interacting mechanisms of sustained attention and attentional capture, both of which operate in space and on object features. While evidence indicates that exogenous attentional capture, a mechanism previously understood to be automatic, can be eliminated while concurrently performing a demanding task, we reframe this phenomenon within the theoretical framework of the "attention set" (Most et al., 2005). Consequently, the specific prediction that cuing effects should reappear when feature dimensions of the cue overlap with those in the attention set (i.e., elements of the demanding task) was empirically tested and confirmed using a dual-task paradigm involving both sustained attention and attentional capture, adapted from Santangelo et al. (2007). Participants were required to either detect a centrally presented target presented in a stream of distractors (the primary task), or respond to a spatially cued target (the secondary task). Importantly, the spatial cue could either share features with the target in the centrally presented primary task, or not share any features. Overall, the findings supported the attention set hypothesis showing that a spatial cuing effect was only observed when the peripheral cue shared a feature with objects that were already in the attention set (i.e., the primary task). However, this finding was accompanied by differential attentional orienting dependent on the different types of objects within the attention set, with feature-based orienting occurring for target-related objects, and additional spatial-based orienting for distractor-related objects.

The interaction of feature and space based orienting within the attention set

PubMed Central

Lim, Ahnate; Sinnett, Scott

2014-01-01

The processing of sensory information relies on interacting mechanisms of sustained attention and attentional capture, both of which operate in space and on object features. While evidence indicates that exogenous attentional capture, a mechanism previously understood to be automatic, can be eliminated while concurrently performing a demanding task, we reframe this phenomenon within the theoretical framework of the “attention set” (Most et al., 2005). Consequently, the specific prediction that cuing effects should reappear when feature dimensions of the cue overlap with those in the attention set (i.e., elements of the demanding task) was empirically tested and confirmed using a dual-task paradigm involving both sustained attention and attentional capture, adapted from Santangelo et al. (2007). Participants were required to either detect a centrally presented target presented in a stream of distractors (the primary task), or respond to a spatially cued target (the secondary task). Importantly, the spatial cue could either share features with the target in the centrally presented primary task, or not share any features. Overall, the findings supported the attention set hypothesis showing that a spatial cuing effect was only observed when the peripheral cue shared a feature with objects that were already in the attention set (i.e., the primary task). However, this finding was accompanied by differential attentional orienting dependent on the different types of objects within the attention set, with feature-based orienting occurring for target-related objects, and additional spatial-based orienting for distractor-related objects. PMID:24523682

Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach.

PubMed

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Changsen; Liu, Feixiang

2017-02-15

Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction. Spatial optimization is implemented by channel selection and finding discriminative spatial filters adaptively on each time-frequency segment. A novel Discernibility of Feature Sets (DFS) criteria is designed for spatial filter optimization. Besides, discriminative features located in multiple time-frequency segments are selected automatically by the proposed sparse time-frequency segment common spatial pattern (STFSCSP) method which exploits sparse regression for significant features selection. Finally, a weight determined by the sparse coefficient is assigned for each selected CSP feature and we propose a Weighted Naïve Bayesian Classifier (WNBC) for classification. Experimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance. The proposed method gives significantly better classification accuracies in comparison with several competing methods in the literature. The proposed approach is a promising candidate for future BCI systems. Copyright © 2016 Elsevier B.V. All rights reserved.

An online network tool for quality information to answer questions about occupational safety and health: usability and applicability.

PubMed

Rhebergen, Martijn D F; Hulshof, Carel T J; Lenderink, Annet F; van Dijk, Frank J H

2010-10-22

Common information facilities do not always provide the quality information needed to answer questions on health or health-related issues, such as Occupational Safety and Health (OSH) matters. Barriers may be the accessibility, quantity and readability of information. Online Question & Answer (Q&A) network tools, which link questioners directly to experts can overcome some of these barriers. When designing and testing online tools, assessing the usability and applicability is essential. Therefore, the purpose of this study is to assess the usability and applicability of a new online Q&A network tool for answers on OSH questions. We applied a cross-sectional usability test design. Eight occupational health experts and twelve potential questioners from the working population (workers) were purposively selected to include a variety of computer- and internet-experiences. During the test, participants were first observed while executing eight tasks that entailed important features of the tool. In addition, they were interviewed. Through task observations and interviews we assessed applicability, usability (effectiveness, efficiency and satisfaction) and facilitators and barriers in use. Most features were usable, though several could be improved. Most tasks were executed effectively. Some tasks, for example searching stored questions in categories, were not executed efficiently and participants were less satisfied with the corresponding features. Participants' recommendations led to improvements. The tool was found mostly applicable for additional information, to observe new OSH trends and to improve contact between OSH experts and workers. Hosting and support by a trustworthy professional organization, effective implementation campaigns, timely answering and anonymity were seen as important use requirements. This network tool is a promising new strategy for offering company workers high quality information to answer OSH questions. Q&A network tools can be an addition to existing information facilities in the field of OSH, but also to other healthcare fields struggling with how to answer questions from people in practice with high quality information. In the near future, we will focus on the use of the tool and its effects on information and knowledge dissemination.

Molecular engineering of phosphole-based conjugated materials

NASA Astrophysics Data System (ADS)

Ren, Yi

The work in this thesis focuses on the molecular engineering of phosphorus-based conjugated materials. In the first part (Chapters Two and Three), new phosphorus-based conjugated systems were designed and synthesized to study the effect of the heteroelement on the electronic properties of the π-conjugated systems. The second part (Chapters Four and Five) deals with the self-assembly features of specifically designed phosphorus-based conjugated systems. In Chapter Two, electron-poor and electron-rich aromatic substituents were introduced to the dithienophosphole core in order to balance the electron-accepting and electron-donating character of the systems. Furthermore, an intriguing intramolecular charge transfer process could be observed between two dithienophosphole cores in a bridged bisphosphole-system. In Chapter Three, a secondary heteroelement (Si, P, S) was incorporated in the phosphorus-based conjugated systems. Extensive structure-property studies revealed that the secondary heteroelement can effectively manipulate the communication in phosphinine-based systems. The study of a heterotetracene system allowed for selectively applying distinct heteroatom (S/P) chemistries, which offers a powerful tool for the modification of the electronic structure of the system. More importantly, the heteroatom-specific electronic nature (S/P) can be utilized to selectively control different photophysical aspects (energy gap and fluorescence quantum yield). Furthermore, additional molecular engineering of the heterotetracene provided access to well-defined 1D microstructures, which opened the door for designing multi-functional self-assembled phosphorus-based materials. In Chapter Four, the self-organizing phosphole-lipid system is introduced, which combines the features of phospholipids with the electronics of phospholes. Its amphiphilic nature induces intriguing self-assembly features - liquid crystal and soft crystal architectures, both exhibiting well-organized lamellar structure at a wide range of temperatures. Importantly, its dynamic structure endows the phosphole-lipid system with intriguing external stimuli-responsive features allowing for the modification of the emission of the system without further chemical modification. Chapter Five describes how further molecular engineering allowed for access to a series of new highly fluorescent phosphole-lipid organogels. Remarkably, the external-stimuli responsive features of the system can be amplified in a donor-acceptor system accessible through changes in long distance fluorescence resonance energy transfer processes. In addition, the first fluorescent liquid phospholes could also be accessed in the context of the work on the new phosphole-lipid system.

Multivariate analysis of full-term neonatal polysomnographic data.

PubMed

Gerla, V; Paul, K; Lhotska, L; Krajca, V

2009-01-01

Polysomnography (PSG) is one of the most important noninvasive methods for studying maturation of the child brain. Sleep in infants is significantly different from sleep in adults. This paper addresses the problem of computer analysis of neonatal polygraphic signals. We applied methods designed for differentiating three important neonatal behavioral states: quiet sleep, active sleep, and wakefulness. The proportion of these states is a significant indicator of the maturity of the newborn brain in clinical practice. In this study, we used data provided by the Institute for Care of Mother and Child, Prague (12 newborn infants of similar postconceptional age). The data were scored by an experienced physician to four states (wake, quiet sleep, active sleep, movement artifact). For accurate classification, it was necessary to determine the most informative features. We used a method based on power spectral density (PSD) applied to each EEG channel. We also used features derived from electrooculogram (EOG), electromyogram (EMG), ECG, and respiration [pneumogram (PNG)] signals. The most informative feature was the measure of regularity of respiration from the PNG signal. We designed an algorithm for interpreting these characteristics. This algorithm was based on Markov models. The results of automatic detection of sleep states were compared to the "sleep profiles" determined visually. We evaluated both the success rate and the true positive rate of the classification, and statistically significant agreement of the two scorings was found. Two variants, for learning and for testing, were applied, namely learning from the data of all 12 newborns and tenfold cross-validation, and learning from the data of 11 newborns and testing on the data from the 12th newborn. We utilized information obtained from several biological signals (EEG, ECG, PNG, EMG, EOG) for our final classification. We reached the final success rate of 82.5%. The true positive rate was 81.8% and the false positive rate was 6.1%. The most important step in the whole process is feature extraction and feature selection. In this process, we used visualization as an additional tool that helped us to decide which features to select. Proper selection of features may significantly influence the success rate of the classification. We made a visual comparison of the computed features with the manual scoring provided by the expert. A hidden Markov model was used for classification. The advantage of this model is that it determines the future behavior of the process by its present state. In this way, it preserves information about temporal development.

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.

Developing a framework for assessment of the environmental determinants of walking and cycling.

PubMed

Pikora, Terri; Giles-Corti, Billie; Bull, Fiona; Jamrozik, Konrad; Donovan, Rob

2003-04-01

The focus for interventions and research on physical activity has moved away from vigorous activity to moderate-intensity activities, such as walking. In addition, a social ecological approach to physical activity research and practice is recommended. This approach considers the influence of the environment and policies on physical activity. Although there is limited empirical published evidence related to the features of the physical environment that influence physical activity, urban planning and transport agencies have developed policies and strategies that have the potential to influence whether people walk or cycle in their neighbourhood. This paper presents the development of a framework of the potential environmental influences on walking and cycling based on published evidence and policy literature, interviews with experts and a Delphi study. The framework includes four features: functional, safety, aesthetic and destination; as well as the hypothesised factors that contribute to each of these features of the environment. In addition, the Delphi experts determined the perceived relative importance of these factors. Based on these factors, a data collection tool will be developed and the frameworks will be tested through the collection of environmental information on neighbourhoods, where data on the walking and cycling patterns have been collected previously. Identifying the environmental factors that influence walking and cycling will allow the inclusion of a public health perspective as well as those of urban planning and transport in the design of built environments.

A hybrid LIBS-Raman system combined with chemometrics: an efficient tool for plastic identification and sorting.

PubMed

Shameem, K M Muhammed; Choudhari, Khoobaram S; Bankapur, Aseefhali; Kulkarni, Suresh D; Unnikrishnan, V K; George, Sajan D; Kartha, V B; Santhosh, C

2017-05-01

Classification of plastics is of great importance in the recycling industry as the littering of plastic wastes increases day by day as a result of its extensive use. In this paper, we demonstrate the efficacy of a combined laser-induced breakdown spectroscopy (LIBS)-Raman system for the rapid identification and classification of post-consumer plastics. The atomic information and molecular information of polyethylene terephthalate, polyethylene, polypropylene, and polystyrene were studied using plasma emission spectra and scattered signal obtained in the LIBS and Raman technique, respectively. The collected spectral features of the samples were analyzed using statistical tools (principal component analysis, Mahalanobis distance) to categorize the plastics. The analyses of the data clearly show that elemental information and molecular information obtained from these techniques are efficient for classification of plastics. In addition, the molecular information collected via Raman spectroscopy exhibits clearly distinct features for the transparent plastics (100% discrimination), whereas the LIBS technique shows better spectral feature differences for the colored samples. The study shows that the information obtained from these complementary techniques allows the complete classification of the plastic samples, irrespective of the color or additives. This work further throws some light on the fact that the potential limitations of any of these techniques for sample identification can be overcome by the complementarity of these two techniques. Graphical Abstract ᅟ.

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.

Application-Dedicated Selection of Filters (ADSF) using covariance maximization and orthogonal projection.

PubMed

Hadoux, Xavier; Kumar, Dinesh Kant; Sarossy, Marc G; Roger, Jean-Michel; Gorretta, Nathalie

2016-05-19

Visible and near-infrared (Vis-NIR) spectra are generated by the combination of numerous low resolution features. Spectral variables are thus highly correlated, which can cause problems for selecting the most appropriate ones for a given application. Some decomposition bases such as Fourier or wavelet generally help highlighting spectral features that are important, but are by nature constraint to have both positive and negative components. Thus, in addition to complicating the selected features interpretability, it impedes their use for application-dedicated sensors. In this paper we have proposed a new method for feature selection: Application-Dedicated Selection of Filters (ADSF). This method relaxes the shape constraint by enabling the selection of any type of user defined custom features. By considering only relevant features, based on the underlying nature of the data, high regularization of the final model can be obtained, even in the small sample size context often encountered in spectroscopic applications. For larger scale deployment of application-dedicated sensors, these predefined feature constraints can lead to application specific optical filters, e.g., lowpass, highpass, bandpass or bandstop filters with positive only coefficients. In a similar fashion to Partial Least Squares, ADSF successively selects features using covariance maximization and deflates their influences using orthogonal projection in order to optimally tune the selection to the data with limited redundancy. ADSF is well suited for spectroscopic data as it can deal with large numbers of highly correlated variables in supervised learning, even with many correlated responses. Copyright © 2016 Elsevier B.V. All rights reserved.

Anatomy and clinical significance of the uncinate process and uncovertebral joint: A comprehensive review.

PubMed

Hartman, Jeffrey

2014-04-01

The uncinate process and its associated uncovertebral articulation are features unique to the cervical spine. This review examines the morphology of these unique structures with particular emphasis on the regional anatomy, development and clinical significance. Five electronic databases were utilized in the literature search and additional relevant citations were retrieved from the references. A total of 74 citations were included for review. This literature review found that the uncinate processes and uncovertebral articulations are rudimentary at birth and develop and evolve with age. With degeneration they become clinically apparent with compression of related structures; most importantly affecting the spinal nerve root and vertebral artery. The articulations have also been found to precipitate torticollis when edematous and be acutely damaged in severe head and neck injuries. The uncinate processes are also important in providing stability and guiding the motion of the cervical spine. This review is intended to re-examine an often overlooked region of the cervical spine as not only an interesting anatomical feature but also a clinically relevant one. Copyright © 2014 Wiley Periodicals, Inc.

Chimeric Mice with Competent Hematopoietic Immunity Reproduce Key Features of Severe Lassa Fever.

PubMed

Oestereich, Lisa; Lüdtke, Anja; Ruibal, Paula; Pallasch, Elisa; Kerber, Romy; Rieger, Toni; Wurr, Stephanie; Bockholt, Sabrina; Pérez-Girón, José V; Krasemann, Susanne; Günther, Stephan; Muñoz-Fontela, César

2016-05-01

Lassa fever (LASF) is a highly severe viral syndrome endemic to West African countries. Despite the annual high morbidity and mortality caused by LASF, very little is known about the pathophysiology of the disease. Basic research on LASF has been precluded due to the lack of relevant small animal models that reproduce the human disease. Immunocompetent laboratory mice are resistant to infection with Lassa virus (LASV) and, to date, only immunodeficient mice, or mice expressing human HLA, have shown some degree of susceptibility to experimental infection. Here, transplantation of wild-type bone marrow cells into irradiated type I interferon receptor knockout mice (IFNAR-/-) was used to generate chimeric mice that reproduced important features of severe LASF in humans. This included high lethality, liver damage, vascular leakage and systemic virus dissemination. In addition, this model indicated that T cell-mediated immunopathology was an important component of LASF pathogenesis that was directly correlated with vascular leakage. Our strategy allows easy generation of a suitable small animal model to test new vaccines and antivirals and to dissect the basic components of LASF pathophysiology.

Self-catalyzed photo-initiated RAFT polymerization for fabrication of fluorescent polymeric nanoparticles with aggregation-induced emission feature.

PubMed

Zeng, Guangjian; Liu, Meiying; Jiang, Ruming; Huang, Qiang; Huang, Long; Wan, Qing; Dai, Yanfeng; Wen, Yuanqing; Zhang, Xiaoyong; Wei, Yen

2018-02-01

In recent years, the fluorescent polymeric nanoparticles (FPNs) with aggregation-induced emission (AIE) feature have been extensively exploited in various biomedical fields owing to their advantages, such as low toxicity, biodegradation, excellent biocompatibility, good designability and optical properties. Therefore, development of a facile, efficient and well designable strategy should be of great importance for the biomedical applications of these AIE-active FPNs. In this work, a novel method for the fabrication of AIE-active FPNs has been developed through the self-catalyzed photo-initiated reversible addition fragmentation chain transfer (RAFT) polymerization using an AIE dye containing chain transfer agent (CTA), which could initiate the RAFT polymerization under light irradiation. The results suggested that the final AIE-active FPNs (named as TPE-poly(St-PEGMA)) showed great potential for biomedical applications owing to their optical and biological properties. More importantly, the method described in the work is rather simple and effective and can be further extended to prepare many other different AIE-active FPNs owing to the good monomer adoptability of RAFT polymerization. Copyright © 2017 Elsevier B.V. All rights reserved.

What to Build for Middle-Agers to Come? Attractive and Necessary Functions of Exercise-Promotion Mobile Phone Apps: A Cross-Sectional Study

PubMed Central

Chien, Yu-Tai; Chen, Yu-Jen; Hsiung, Hsiao-Fang; Chen, Hsiao-Jung; Hsieh, Meng-Hua; Wu, Wen-Jie

2017-01-01

Background Physical activity is important for middle-agers to maintain health both in middle age and in old age. Although thousands of exercise-promotion mobile phone apps are available for download, current literature offers little understanding regarding which design features can enhance middle-aged adults’ quality perception toward exercise-promotion apps and which factor may influence such perception. Objectives The aims of this study were to understand (1) which design features of exercise-promotion apps can enhance quality perception of middle-agers, (2) whether their needs are matched by current functions offered in app stores, and (3) whether physical activity (PA) and mobile phone self-efficacy (MPSE) influence quality perception. Methods A total of 105 middle-agers participated and filled out three scales: the International Physical Activity Questionnaire (IPAQ), the MPSE scale, and the need for design features questionnaire. The design features were developed based on the Coventry, Aberdeen, and London—Refined (CALO-RE) taxonomy. Following the Kano quality model, the need for design features questionnaire asked participants to classify design features into five categories: attractive, one-dimensional, must-be, indifferent, and reverse. The quality categorization was conducted based on a voting approach and the categorization results were compared with the findings of a prevalence study to realize whether needs match current availability. In total, 52 multinomial logistic regression models were analyzed to evaluate the effects of PA level and MPSE on quality perception of design features. Results The Kano analysis on the total sample revealed that visual demonstration of exercise instructions is the only attractive design feature, whereas the other 51 design features were perceived with indifference. Although examining quality perception by PA level, 21 features are recommended to low level, 6 features to medium level, but none to high-level PA. In contrast, high-level MPSE is recommended with 14 design features, medium level with 6 features, whereas low-level participants are recommended with 1 feature. The analysis suggests that the implementation of demanded features could be low, as the average prevalence of demanded design features is 20% (4.3/21). Surprisingly, social comparison and social support, most implemented features in current apps, were categorized into the indifferent category. The magnitude of effect is larger for MPSE because it effects quality perception of more design features than PA. Delving into the 52 regression models revealed that high MPSE more likely induces attractive or one- dimensional categorization, suggesting the importance of technological self-efficacy on eHealth care promotion. Conclusions This study is the first to propose middle-agers’ needs in relation to mobile phone exercise-promotion. In addition to the tailor-made recommendations, suggestions are offered to app designers to enhance the performance of persuasive features. An interesting finding on change of quality perception attributed to MPSE is proposed as future research. PMID:28546140

Cigarette Design Features: Effects on Emission Levels, User Perception, and Behavior.

PubMed

Talhout, Reinskje; Richter, Patricia A; Stepanov, Irina; Watson, Christina V; Watson, Clifford H

2018-01-01

This paper describes the effects of non-tobacco, physical cigarette design features on smoke emissions, product appeal, and smoking behaviors - 3 factors that determine smoker's exposure and related health risks. We reviewed available evidence for the impact of filter ventilation, new filter types, and cigarettes dimensions on toxic emissions, smoker's perceptions, and behavior. For evidence sources we used scientific literature and websites providing product characteristics and marketing information. Whereas filter ventilation results in lower machine-generated emissions, it also leads to perceptions of lighter taste and relative safety in smokers who can unwittingly employ more intense smoking behavior to obtain the desired amount of nicotine and sensory appeal. Filter additives that modify smoke emissions can also modify sensory cues, resulting in changes in smoking behavior. Flavor capsules increase the cigarette's appeal and novelty, and lead to misperceptions of reduced harm. Slim cigarettes have lower yields of some smoke emissions, but smoking behavior can be more intense than with standard cigarettes. Physical design features significantly impact machine-measured emission yields in cigarette smoke, product appeal, smoking behaviors, and exposures in smokers. The influence of current and emerging design features is important in understanding the effectiveness of regulatory actions to reduce smoking-related harm.

Systems for deep brain stimulation: review of technical features.

PubMed

Amon, A; Alesch, F

2017-09-01

The use of deep brain stimulation (DBS) is an important treatment option for movement disorders and other medical conditions. Today, three major manufacturers provide implantable systems for DBS. Although the underlying principle is basically the same for all available systems, the differences in the technical features vary considerably. This article outlines aspects regarding the technical features of DBS systems. The differences between voltage and current sources are addressed and their effect on stimulation is shown. To maintain clinical benefit and minimize side effects the stimulation field has to be adapted to the requirements of the patient. Shaping of the stimulation field can be achieved by the electrode design and polarity configuration. Furthermore, the electric signal consisting of stimulation rate, stimulation amplitude and pulse width affect the stimulation field. Interleaving stimulation is an additional concept, which permits improved treatment outcomes. Therefore, the electrode design, the polarity, the electric signal, and the concept of interleaving stimulation are presented. The investigated systems can be also categorized as rechargeable and non-rechargeable, which is briefly discussed. Options for interconnecting different system components from various manufacturers are presented. The present paper summarizes the technical features and their combination possibilities, which can have a major impact on the therapeutic effect.

Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping

NASA Astrophysics Data System (ADS)

Tamiminia, H.; Homayouni, S.; Safari, A.

2015-12-01

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

Feature-constrained surface reconstruction approach for point cloud data acquired with 3D laser scanner

NASA Astrophysics Data System (ADS)

Wang, Yongbo; Sheng, Yehua; Lu, Guonian; Tian, Peng; Zhang, Kai

2008-04-01

Surface reconstruction is an important task in the field of 3d-GIS, computer aided design and computer graphics (CAD & CG), virtual simulation and so on. Based on available incremental surface reconstruction methods, a feature-constrained surface reconstruction approach for point cloud is presented. Firstly features are extracted from point cloud under the rules of curvature extremes and minimum spanning tree. By projecting local sample points to the fitted tangent planes and using extracted features to guide and constrain the process of local triangulation and surface propagation, topological relationship among sample points can be achieved. For the constructed models, a process named consistent normal adjustment and regularization is adopted to adjust normal of each face so that the correct surface model is achieved. Experiments show that the presented approach inherits the convenient implementation and high efficiency of traditional incremental surface reconstruction method, meanwhile, it avoids improper propagation of normal across sharp edges, which means the applicability of incremental surface reconstruction is greatly improved. Above all, appropriate k-neighborhood can help to recognize un-sufficient sampled areas and boundary parts, the presented approach can be used to reconstruct both open and close surfaces without additional interference.

A Human-Autonomy Teaming Approach for a Flight-Following Task

NASA Technical Reports Server (NTRS)

Brandt, Summer L.; Russell, Ricky; Lachter, Joel; Shively, Robert

2017-01-01

Managing aircraft is becoming more complex with increasingly sophisticated automation responsible for more flight tasks. With this increased complexity, it is becoming more difficult for operators to understand what the automation is doing and why. Human involvement with increasingly autonomous systems must adjust to allow for a more dynamic relationship involving cooperation and teamwork. As part of an ongoing project to develop a framework for human-autonomy teaming (HAT) in aviation, a part-task study was conducted to demonstrate, evaluate and refine proposed critical aspects of HAT. These features were built into an automated recommender system on a ground station available from previous studies. Participants performed a flight-following task once with the original ground station (i.e., No HAT condition) and once with the HAT features enabled (i.e., HAT condition). Behavioral and subjective measures were collected; subjective measures are presented here. Overall, participants preferred the ground station with HAT features enabled compared to the station without the HAT features. Participants reported that the HAT displays and automation were preferred for keeping up with operationally important issues. Additionally, participants reported that the HAT displays and automation provided enough situation awareness to complete the task and reduced workload relative to the No HAT baseline.

The importance of becoming double-stranded: Innate immunity and the kinetic model of HIV-1 central plus strand synthesis

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

Poeschla, Eric, E-mail: poeschla.eric@mayo.edu

Central initiation of plus strand synthesis is a conserved feature of lentiviruses and certain other retroelements. This complication of the standard reverse transcription mechanism produces a transient “central DNA flap” in the viral cDNA, which has been proposed to mediate its subsequent nuclear import. This model has assumed that the important feature is the flapped DNA structure itself rather than the process that produces it. Recently, an alternative kinetic model was proposed. It posits that central plus strand synthesis functions to accelerate conversion to the double-stranded state, thereby helping HIV-1 to evade single-strand DNA-targeting antiviral restrictions such as APOBEC3 proteins,more » and perhaps to avoid innate immune sensor mechanisms. The model is consistent with evidence that lentiviruses must often synthesize their cDNAs when dNTP concentrations are limiting and with data linking reverse transcription and uncoating. There may be additional kinetic advantages for the artificial genomes of lentiviral gene therapy vectors. - Highlights: • Two main functional models for HIV central plus strand synthesis have been proposed. • In one, a transient central DNA flap in the viral cDNA mediates HIV-1 nuclear import. • In the other, multiple kinetic consequences are emphasized. • One is defense against APOBEC3G, which deaminates single-stranded DNA. • Future questions pertain to antiviral restriction, uncoating and nuclear import.« less

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

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.

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.

Life events and borderline personality features: the influence of gene-environment interaction and gene-environment correlation.

PubMed

Distel, M A; Middeldorp, C M; Trull, T J; Derom, C A; Willemsen, G; Boomsma, D I

2011-04-01

Traumatic life events are generally more common in patients with borderline personality disorder (BPD) than in non-patients or patients with other personality disorders. This study investigates whether exposure to life events moderates the genetic architecture of BPD features. As the presence of genotype-environment correlation (rGE) can lead to spurious findings of genotype-environment interaction (G × E), we also test whether BPD features increase the likelihood of exposure to life events. The extent to which an individual is at risk to develop BPD was assessed with the Personality Assessment Inventory - Borderline features scale (PAI-BOR). Life events under study were a divorce/break-up, traffic accident, violent assault, sexual assault, robbery and job loss. Data were available for 5083 twins and 1285 non-twin siblings. Gene-environment interaction and correlation were assessed by using structural equation modelling (SEM) and the co-twin control design. There was evidence for both gene-environment interaction and correlation. Additive genetic influences on BPD features interacted with the exposure to sexual assault, with genetic variance being lower in exposed individuals. In individuals who had experienced a divorce/break-up, violent assault, sexual assault or job loss, environmental variance for BPD features was higher, leading to a lower heritability of BPD features in exposed individuals. Gene-environment correlation was present for some life events. The genes that influence BPD features thus also increased the likelihood of being exposed to certain life events. To our knowledge, this study is the first to test the joint effect of genetic and environmental influences and the exposure to life events on BPD features in the general population. Our results indicate the importance of both genetic vulnerability and life events.

Anomalous Cases of Astronaut Helmet Detection

NASA Technical Reports Server (NTRS)

Dolph, Chester; Moore, Andrew J.; Schubert, Matthew; Woodell, Glenn

2015-01-01

An astronaut's helmet is an invariant, rigid image element that is well suited for identification and tracking using current machine vision technology. Future space exploration will benefit from the development of astronaut detection software for search and rescue missions based on EVA helmet identification. However, helmets are solid white, except for metal brackets to attach accessories such as supplementary lights. We compared the performance of a widely used machine vision pipeline on a standard-issue NASA helmet with and without affixed experimental feature-rich patterns. Performance on the patterned helmet was far more robust. We found that four different feature-rich patterns are sufficient to identify a helmet and determine orientation as it is rotated about the yaw, pitch, and roll axes. During helmet rotation the field of view changes to frames containing parts of two or more feature-rich patterns. We took reference images in these locations to fill in detection gaps. These multiple feature-rich patterns references added substantial benefit to detection, however, they generated the majority of the anomalous cases. In these few instances, our algorithm keys in on one feature-rich pattern of the multiple feature-rich pattern reference and makes an incorrect prediction of the location of the other feature-rich patterns. We describe and make recommendations on ways to mitigate anomalous cases in which detection of one or more feature-rich patterns fails. While the number of cases is only a small percentage of the tested helmet orientations, they illustrate important design considerations for future spacesuits. In addition to our four successful feature-rich patterns, we present unsuccessful patterns and discuss the cause of their poor performance from a machine vision perspective. Future helmets designed with these considerations will enable automated astronaut detection and thereby enhance mission operations and extraterrestrial search and rescue.

A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).

PubMed

Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

2014-01-01

Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

SHBG Is an Important Factor in Stemness Induction of Cells by DHT In Vitro and Associated with Poor Clinical Features of Prostate Carcinomas

PubMed Central

Ma, Yuanyuan; Liang, Dongming; Liu, Jian; Wen, Jian-Guo; Servoll, Einar; Waaler, Gudmund; Sæter, Thorstein; Axcrona, Karol; Vlatkovic, Ljiljana; Axcrona, Ulrika; Paus, Elisabeth; Yang, Yue; Zhang, Zhiqian; Kvalheim, Gunnar; Nesland, Jahn M.; Suo, Zhenhe

2013-01-01

Androgen plays a vital role in prostate cancer development. However, it is not clear whether androgens influence stem-like properties of prostate cancer, a feature important for prostate cancer progression. In this study, we show that upon DHT treatment in vitro, prostate cancer cell lines LNCaP and PC-3 were revealed with higher clonogenic potential and higher expression levels of stemness related factors CD44, CD90, Oct3/4 and Nanog. Moreover, sex hormone binding globulin (SHBG) was also simultaneously upregulated in these cells. When the SHBG gene was blocked by SHBG siRNA knock-down, the induction of Oct3/4, Nanog, CD44 and CD90 by DHT was also correspondingly blocked in these cells. Immunohistochemical evaluation of clinical samples disclosed weakly positive, and areas negative for SHBG expression in the benign prostate tissues, while most of the prostate carcinomas were strongly positive for SHBG. In addition, higher levels of SHBG expression were significantly associated with higher Gleason score, more seminal vesicle invasions and lymph node metastases. Collectively, our results show a role of SHBG in upregulating stemness of prostate cancer cells upon DHT exposure in vitro, and SHBG expression in prostate cancer samples is significantly associated with poor clinicopathological features, indicating a role of SHBG in prostate cancer progression. PMID:23936228

Electronic Materials Science

NASA Astrophysics Data System (ADS)

Irene, Eugene A.

2005-02-01

A thorough introduction to fundamental principles and applications From its beginnings in metallurgy and ceramics, materials science now encompasses such high- tech fields as microelectronics, polymers, biomaterials, and nanotechnology. Electronic Materials Science presents the fundamentals of the subject in a detailed fashion for a multidisciplinary audience. Offering a higher-level treatment than an undergraduate textbook provides, this text benefits students and practitioners not only in electronics and optical materials science, but also in additional cutting-edge fields like polymers and biomaterials. Readers with a basic understanding of physical chemistry or physics will appreciate the text's sophisticated presentation of today's materials science. Instructive derivations of important formulae, usually omitted in an introductory text, are included here. This feature offers a useful glimpse into the foundations of how the discipline understands such topics as defects, phase equilibria, and mechanical properties. Additionally, concepts such as reciprocal space, electron energy band theory, and thermodynamics enter the discussion earlier and in a more robust fashion than in other texts. Electronic Materials Science also features: An orientation towards industry and academia drawn from the author's experience in both arenas Information on applications in semiconductors, optoelectronics, photocells, and nanoelectronics Problem sets and important references throughout Flexibility for various pedagogical needs Treating the subject with more depth than any other introductory text, Electronic Materials Science prepares graduate and upper-level undergraduate students for advanced topics in the discipline and gives scientists in associated disciplines a clear review of the field and its leading technologies.

Selection of the battery pack parameters for an electric vehicle based on performance requirements

NASA Astrophysics Data System (ADS)

Koniak, M.; Czerepicki, A.

2017-06-01

Each type of vehicle has specific power requirements. Some require a rapid charging, other make long distances between charges, but a common feature is the longest battery life time. Additionally, the battery is influenced by factors such as temperature, depth of discharge and the operation current. The article contain the parameters of chemical cells that should be taken into account during the design of the battery for a specific application. This is particularly important because the batteries are not properly matched and can wear prematurely and cause an additional costs. The method of selecting the correct cell type should take previously discussed features and operating characteristics of the vehicle into account. The authors present methods of obtaining such characteristics along with their assessment and examples. Also there has been described an example of the battery parameters selection based on design assumptions of the vehicle and the expected performance characteristics. Selecting proper battery operating parameters is important due to its impact on the economic result of investments in electric vehicles. For example, for some Li-Ion technologies, the earlier worn out of batteries in a fleet of cruise boats or buses having estimated lifetime of 10 years is not acceptable, because this will cause substantial financial losses for the owner of the rolling stock. The presented method of choosing the right cell technology in the selected application, can be the basis for making the decision on future battery technical parameters.

The use of a diuretic agent as a probe to investigate site and mechanism of ion transport processes.

PubMed

Giebisch, G

1985-01-01

Several features emerge from consideration of a furosemide-sensitive cotransport mechanism in the various tissues surveyed. First discovered in epithelia, above all in the kidney because of its potent diuretic effect, furosemide inhibits a cotransport mechanism that tightly couples the movement of sodium, chloride and potassium. Its mode of operation is electrically neutral and in all tissues so far examined, the cotransport-mediated ion movement is driven by the electrochemical potential of the cotransported ion-species. The energy for this ion movement derives ultimately from the Na-K pump that establishes the Na gradient that drives the coupled ion movement. This type of carrier-mediated and ion-specific solute movement expands the traditional "pump-leak" model of cellular ion transport by providing dissipative "leak" pathways in addition to the well-established ion channels that allow solute movement by electrodiffusion. An important feature of the cotransport mechanism is its important role in both reabsorptive and secretory epithelial transport operations. This variability can be adequately explained by the location of the cotransport mechanism in either the apical or basolateral cell membrane of such epithelia as the renal tubule, the intestinal mucosa, the rectal gland or the trachea. In addition, the furosemide-sensitive transporter has also been shown to play a significant role in cell volume regulation, both in epithelia and in non-epithelia cells, and it appears to participate in the regulation of the cell chloride concentrations in excitable tissues.

Routing and Scheduling Algorithms for WirelessHART Networks: A Survey

PubMed Central

Nobre, Marcelo; Silva, Ivanovitch; Guedes, Luiz Affonso

2015-01-01

Wireless communication is a trend nowadays for the industrial environment. A number of different technologies have emerged as solutions satisfying strict industrial requirements (e.g., WirelessHART, ISA100.11a, WIA-PA). As the industrial environment presents a vast range of applications, adopting an adequate solution for each case is vital to obtain good performance of the system. In this context, the routing and scheduling schemes associated with these technologies have a direct impact on important features, like latency and energy consumption. This situation has led to the development of a vast number of routing and scheduling schemes. In the present paper, we focus on the WirelessHART technology, emphasizing its most important routing and scheduling aspects in order to guide both end users and the developers of new algorithms. Furthermore, we provide a detailed literature review of the newest routing and scheduling techniques for WirelessHART, discussing each of their features. These routing algorithms have been evaluated in terms of their objectives, metrics, the usage of the WirelessHART structures and validation method. In addition, the scheduling algorithms were also evaluated by metrics, validation, objectives and, in addition, by multiple superframe support, as well as by the redundancy method used. Moreover, this paper briefly presents some insights into the main WirelessHART simulation modules available, in order to provide viable test platforms for the routing and scheduling algorithms. Finally, some open issues in WirelessHART routing and scheduling algorithms are discussed. PMID:25919371

Role of Polyamines in Asthma Pathophysiology

PubMed Central

2018-01-01

Asthma is a complex disease of airways, where the interactions of immune and structural cells result in disease outcomes with airway remodeling and airway hyper-responsiveness. Polyamines, which are small-sized, natural super-cations, interact with negatively charged intracellular macromolecules, and altered levels of polyamines and their interactions have been associated with different pathological conditions including asthma. Elevated levels of polyamines have been reported in the circulation of asthmatic patients as well as in the lungs of a murine model of asthma. In various studies, polyamines were found to potentiate the pathogenic potential of inflammatory cells, such as mast cells and granulocytes (eosinophils and neutrophils), by either inducing the release of their pro-inflammatory mediators or prolonging their life span. Additionally, polyamines were crucial in the differentiation and alternative activation of macrophages, which play an important role in asthma pathology. Importantly, polyamines cause airway smooth muscle contraction and thus airway hyper-responsiveness, which is the key feature in asthma pathophysiology. High levels of polyamines in asthma and their active cellular and macromolecular interactions indicate the importance of the polyamine pathway in asthma pathogenesis; therefore, modulation of polyamine levels could be a suitable approach in acute and severe asthma management. This review summarizes the possible roles of polyamines in different pathophysiological features of asthma. PMID:29316647

Immune response and immunopathology during toxoplasmosis1

PubMed Central

Dupont, Christopher D.; Christian, David A.; Hunter, Christopher A.

2012-01-01

Toxoplasma gondii is a protozoan parasite of medical and veterinary significance that is able to infect any warm-blooded vertebrate host. In addition to its importance to public health, several inherent features of the biology of T. gondii have made it an important model organism to study host-pathogen interactions. One factor is the genetic tractability of the parasite, which allows studies on the microbial factors that affect virulence and allows the development of tools that facilitate immune studies. Additionally, mice are natural hosts for T. gondii, and the availability of numerous reagents to study the murine immune system makes this an ideal experimental system to understand the functions of cytokines and effector mechanisms involved in immunity to intracellular microorganisms. In this article, we will review current knowledge of the innate and adaptive immune responses required for resistance to toxoplasmosis, the events that lead to the development of immunopathology, and the natural regulatory mechanisms that limit excessive inflammation during this infection. PMID:22955326

When to suspect a genetic syndrome.

PubMed

Solomon, Benjamin D; Muenke, Maximilian

2012-11-01

Family physicians should be able to recognize findings on physical examination and history that suggest the presence of a genetic syndrome to aid in the diagnosis and treatment of potentially affected patients, as well as subspecialty referral. General themes that can alert family physicians to the presence of genetic conditions include dysmorphic features that are evident on physical examination; multiple anomalies in one patient; unexplained neurocognitive impairment; and a family history that is suggestive of a hereditary disease. The presence of one obvious malformation should not limit the full evaluation, because additional, subtler findings will often be important in the differential diagnosis. Taking an accurate three-generation family history is invaluable when considering a genetic syndrome. Important elements include the age and sex of family members; when family members were affected by disease or when they died; the ethnic background; and if there is consanguinity. Genetic subspecialists can assist family physicians in diagnosis, suggest additional testing and referrals if warranted, help direct medical care, and provide counseling for affected patients and their families.

Diagnostic assessment of intraoperative cytology for papillary thyroid carcinoma: using a decision tree analysis.

PubMed

Pyo, J-S; Sohn, J H; Kang, G

2017-03-01

The aim of this study was to elucidate the cytological characteristics and the diagnostic usefulness of intraoperative cytology (IOC) for papillary thyroid carcinoma (PTC). In addition, using decision tree analysis, effective features for accurate cytological diagnosis were sought. We investigated cellularity, cytological features and diagnosis based on the Bethesda System for Reporting Thyroid Cytopathology in IOC of 240 conventional PTCs. The cytological features were evaluated in terms of nuclear score with nuclear features, and additional figures such as presence of swirling sheets, psammoma bodies, and multinucleated giant cells. The nuclear score (range 0-7) was made via seven nuclear features, including (1) enlarged, (2) oval or irregularly shaped nuclei, (3) longitudinal nuclear grooves, (4) intranuclear cytoplasmic pseudoinclusion, (5) pale nuclei with powdery chromatin, (6) nuclear membrane thickening, and (7) marginally placed micronucleoli. Nuclear scores in PTC, suspicious for malignancy, and atypia of undetermined significance cases were 6.18 ± 0.80, 4.48 ± 0.82, and 3.15 ± 0.67, respectively. Additional figures more frequent in PTC than in other diagnostic categories were identified. Cellularity of IOC significantly correlated with tumor size, nuclear score, and presence of additional figures. Also, IOCs with higher nuclear scores (4-7) significantly correlated with larger tumor size and presence of additional figures. In decision tree analysis, IOCs with nuclear score >5 and swirling sheets could be considered diagnostic for PTCs. Our study suggests that IOCs using nuclear features and additional figures could be useful with decreasing the likelihood of inconclusive results.

MOTIFSIM 2.1: An Enhanced Software Platform for Detecting Similarity in Multiple DNA Motif Data Sets

PubMed Central

Huang, Chun-Hsi

2017-01-01

Abstract Finding binding site motifs plays an important role in bioinformatics as it reveals the transcription factors that control the gene expression. The development for motif finders has flourished in the past years with many tools have been introduced to the research community. Although these tools possess exceptional features for detecting motifs, they report different results for an identical data set. Hence, using multiple tools is recommended because motifs reported by several tools are likely biologically significant. However, the results from multiple tools need to be compared for obtaining common significant motifs. MOTIFSIM web tool and command-line tool were developed for this purpose. In this work, we present several technical improvements as well as additional features to further support the motif analysis in our new release MOTIFSIM 2.1. PMID:28632401

Representing nested semantic information in a linear string of text using XML.

PubMed

Krauthammer, Michael; Johnson, Stephen B; Hripcsak, George; Campbell, David A; Friedman, Carol

2002-01-01

XML has been widely adopted as an important data interchange language. The structure of XML enables sharing of data elements with variable degrees of nesting as long as the elements are grouped in a strict tree-like fashion. This requirement potentially restricts the usefulness of XML for marking up written text, which often includes features that do not properly nest within other features. We encountered this problem while marking up medical text with structured semantic information from a Natural Language Processor. Traditional approaches to this problem separate the structured information from the actual text mark up. This paper introduces an alternative solution, which tightly integrates the semantic structure with the text. The resulting XML markup preserves the linearity of the medical texts and can therefore be easily expanded with additional types of information.

Representing nested semantic information in a linear string of text using XML.

PubMed Central

Krauthammer, Michael; Johnson, Stephen B.; Hripcsak, George; Campbell, David A.; Friedman, Carol

2002-01-01

XML has been widely adopted as an important data interchange language. The structure of XML enables sharing of data elements with variable degrees of nesting as long as the elements are grouped in a strict tree-like fashion. This requirement potentially restricts the usefulness of XML for marking up written text, which often includes features that do not properly nest within other features. We encountered this problem while marking up medical text with structured semantic information from a Natural Language Processor. Traditional approaches to this problem separate the structured information from the actual text mark up. This paper introduces an alternative solution, which tightly integrates the semantic structure with the text. The resulting XML markup preserves the linearity of the medical texts and can therefore be easily expanded with additional types of information. PMID:12463856

Tropical Timber Identification using Backpropagation Neural Network

NASA Astrophysics Data System (ADS)

Siregar, B.; Andayani, U.; Fatihah, N.; Hakim, L.; Fahmi, F.

2017-01-01

Each and every type of wood has different characteristics. Identifying the type of wood properly is important, especially for industries that need to know the type of timber specifically. However, it requires expertise in identifying the type of wood and only limited experts available. In addition, the manual identification even by experts is rather inefficient because it requires a lot of time and possibility of human errors. To overcome these problems, a digital image based method to identify the type of timber automatically is needed. In this study, backpropagation neural network is used as artificial intelligence component. Several stages were developed: a microscope image acquisition, pre-processing, feature extraction using gray level co-occurrence matrix and normalization of data extraction using decimal scaling features. The results showed that the proposed method was able to identify the timber with an accuracy of 94%.

Estimating Shape and Micro-Motion Parameter of Rotationally Symmetric Space Objects from the Infrared Signature

PubMed Central

Wu, Yabei; Lu, Huanzhang; Zhao, Fei; Zhang, Zhiyong

2016-01-01

Shape serves as an important additional feature for space target classification, which is complementary to those made available. Since different shapes lead to different projection functions, the projection property can be regarded as one kind of shape feature. In this work, the problem of estimating the projection function from the infrared signature of the object is addressed. We show that the projection function of any rotationally symmetric object can be approximately represented as a linear combination of some base functions. Based on this fact, the signal model of the emissivity-area product sequence is constructed, which is a particular mathematical function of the linear coefficients and micro-motion parameters. Then, the least square estimator is proposed to estimate the projection function and micro-motion parameters jointly. Experiments validate the effectiveness of the proposed method. PMID:27763500

The mass media destabilizes the cultural homogenous regime in Axelrod's model

NASA Astrophysics Data System (ADS)

Peres, Lucas R.; Fontanari, José F.

2010-02-01

An important feature of Axelrod's model for culture dissemination or social influence is the emergence of many multicultural absorbing states, despite the fact that the local rules that specify the agents interactions are explicitly designed to decrease the cultural differences between agents. Here we re-examine the problem of introducing an external, global interaction—the mass media—in the rules of Axelrod's model: in addition to their nearest neighbors, each agent has a certain probability p to interact with a virtual neighbor whose cultural features are fixed from the outset. Most surprisingly, this apparently homogenizing effect actually increases the cultural diversity of the population. We show that, contrary to previous claims in the literature, even a vanishingly small value of p is sufficient to destabilize the homogeneous regime for very large lattice sizes.

Learning using privileged information: SVM+ and weighted SVM.

PubMed

Lapin, Maksim; Hein, Matthias; Schiele, Bernt

2014-05-01

Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information available only at training time-a framework implemented by SVM+. We relate the privileged information to importance weighting and show that the prior knowledge expressible with privileged features can also be encoded by weights associated with every training example. We show that a weighted SVM can always replicate an SVM+ solution, while the converse is not true and we construct a counterexample highlighting the limitations of SVM+. Finally, we touch on the problem of choosing weights for weighted SVMs when privileged features are not available. Copyright © 2014 Elsevier Ltd. All rights reserved.

Application of Mössbauer Spectroscopy in Earth Sciences

NASA Astrophysics Data System (ADS)

Vandenberghe, Robert E.; De Grave, Eddy

Iron being the fourth most abundant element in the earth crust, 57Fe Mössbauer spectroscopy has become a suitable additional technique for the characterization of all kind of soil materials and minerals. However, for that purpose a good knowledge of the spectral behavior of the various minerals is indispensable. In this chapter a review of the most important soil materials and rock-forming minerals is presented. It starts with a description of the Mössbauer spectroscopic features of the iron oxides and hydroxides, which are essentially present in soils and sediments. Further, the Mössbauer spectra from sulfides, sulfates and carbonates are briefly considered. Finally, the Mössbauer features of the typical and most common silicate and phosphate minerals are reported. The chapter ends with some typical examples, illustrating the use and power of Mössbauer spectroscopy in the characterization of minerals.

Similarity-Based Recommendation of New Concepts to a Terminology

PubMed Central

Chandar, Praveen; Yaman, Anil; Hoxha, Julia; He, Zhe; Weng, Chunhua

2015-01-01

Terminologies can suffer from poor concept coverage due to delays in addition of new concepts. This study tests a similarity-based approach to recommending concepts from a text corpus to a terminology. Our approach involves extraction of candidate concepts from a given text corpus, which are represented using a set of features. The model learns the important features to characterize a concept and recommends new concepts to a terminology. Further, we propose a cost-effective evaluation methodology to estimate the effectiveness of terminology enrichment methods. To test our methodology, we use the clinical trial eligibility criteria free-text as an example text corpus to recommend concepts for SNOMED CT. We computed precision at various rank intervals to measure the performance of the methods. Results indicate that our automated algorithm is an effective method for concept recommendation. PMID:26958170

CINCH (confocal incoherent correlation holography) super resolution fluorescence microscopy based upon FINCH (Fresnel incoherent correlation holography).

PubMed

Siegel, Nisan; Storrie, Brian; Bruce, Marc; Brooker, Gary

2015-02-07

FINCH holographic fluorescence microscopy creates high resolution super-resolved images with enhanced depth of focus. The simple addition of a real-time Nipkow disk confocal image scanner in a conjugate plane of this incoherent holographic system is shown to reduce the depth of focus, and the combination of both techniques provides a simple way to enhance the axial resolution of FINCH in a combined method called "CINCH". An important feature of the combined system allows for the simultaneous real-time image capture of widefield and holographic images or confocal and confocal holographic images for ready comparison of each method on the exact same field of view. Additional GPU based complex deconvolution processing of the images further enhances resolution.

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.

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

An extended supersonic combustion model for the dynamic analysis of hypersonic vehicles

NASA Technical Reports Server (NTRS)

Bossard, J. A.; Peck, R. E.; Schmidt, D. K.

1993-01-01

The development of an advanced dynamic model for aeroelastic hypersonic vehicles powered by air breathing engines requires an adequate engine model. This report provides a discussion of some of the more important features of supersonic combustion and their relevance to the analysis and design of supersonic ramjet engines. Of particular interest are those aspects of combustion that impact the control of the process. Furthermore, the report summarizes efforts to enhance the aeropropulsive/aeroelastic dynamic model developed at the Aerospace Research Center of Arizona State University by focusing on combustion and improved modeling of this flow. The expanded supersonic combustor model described here has the capability to model the effects of friction, area change, and mass addition, in addition to the heat addition process. A comparison is made of the results from four cases: (1) heat addition only; (2) heat addition plus friction; (3) heat addition, friction, and area reduction, and (4) heat addition, friction, area reduction, and mass addition. The relative impact of these effects on the Mach number, static temperature, and static pressure distributions within the combustor are then shown. Finally, the effects of frozen versus equilibrium flow conditions within the exhaust plume is discussed.

Feature and Region Selection for Visual Learning.

PubMed

Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

2016-03-01

Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification

PubMed Central

Satheesha, T. Y.; Prasad, M. N. Giri; Dhruve, Kashyap D.

2017-01-01

Melanoma mortality rates are the highest amongst skin cancer patients. Melanoma is life threating when it grows beyond the dermis of the skin. Hence, depth is an important factor to diagnose melanoma. This paper introduces a non-invasive computerized dermoscopy system that considers the estimated depth of skin lesions for diagnosis. A 3-D skin lesion reconstruction technique using the estimated depth obtained from regular dermoscopic images is presented. On basis of the 3-D reconstruction, depth and 3-D shape features are extracted. In addition to 3-D features, regular color, texture, and 2-D shape features are also extracted. Feature extraction is critical to achieve accurate results. Apart from melanoma, in-situ melanoma the proposed system is designed to diagnose basal cell carcinoma, blue nevus, dermatofibroma, haemangioma, seborrhoeic keratosis, and normal mole lesions. For experimental evaluations, the PH2, ISIC: Melanoma Project, and ATLAS dermoscopy data sets is considered. Different feature set combinations is considered and performance is evaluated. Significant performance improvement is reported the post inclusion of estimated depth and 3-D features. The good classification scores of sensitivity = 96%, specificity = 97% on PH2 data set and sensitivity = 98%, specificity = 99% on the ATLAS data set is achieved. Experiments conducted to estimate tumor depth from 3-D lesion reconstruction is presented. Experimental results achieved prove that the proposed computerized dermoscopy system is efficient and can be used to diagnose varied skin lesion dermoscopy images. PMID:28512610

Parenting, relational aggression, and borderline personality features: associations over time in a Russian longitudinal sample.

PubMed

Nelson, David A; Coyne, Sarah M; Swanson, Savannah M; Hart, Craig H; Olsen, Joseph A

2014-08-01

Crick, Murray-Close, and Woods (2005) encouraged the study of relational aggression as a developmental precursor to borderline personality features in children and adolescents. A longitudinal study is needed to more fully explore this association, to contrast potential associations with physical aggression, and to assess generalizability across various cultural contexts. In addition, parenting is of particular interest in the prediction of aggression or borderline personality disorder. Early aggression and parenting experiences may differ in their long-term prediction of aggression or borderline features, which may have important implications for early intervention. The currrent study incorporated a longitudinal sample of preschool children (84 boys, 84 girls) living in intact, two-parent biological households in Voronezh, Russia. Teachers provided ratings of children's relational and physical aggression in preschool. Mothers and fathers also self-reported their engagement in authoritative, authoritarian, permissive, and psychological controlling forms of parenting with their preschooler. A decade later, 70.8% of the original child participants consented to a follow-up study in which they completed self-reports of relational and physical aggression and borderline personality features. The multivariate results of this study showed that preschool relational aggression in girls predicted adolescent relational aggression. Preschool aversive parenting (i.e., authoritarian, permissive, and psychologically controlling forms) significantly predicted aggression and borderline features in adolescent females. For adolescent males, preschool authoritative parenting served as a protective factor against aggression and borderline features, whereas authoritarian parenting was a risk factor for later aggression.

Qualitative and Quantitative Analysis for Facial Complexion in Traditional Chinese Medicine

PubMed Central

Zhao, Changbo; Li, Guo-zheng; Li, Fufeng; Wang, Zhi; Liu, Chang

2014-01-01

Facial diagnosis is an important and very intuitive diagnostic method in Traditional Chinese Medicine (TCM). However, due to its qualitative and experience-based subjective property, traditional facial diagnosis has a certain limitation in clinical medicine. The computerized inspection method provides classification models to recognize facial complexion (including color and gloss). However, the previous works only study the classification problems of facial complexion, which is considered as qualitative analysis in our perspective. For quantitative analysis expectation, the severity or degree of facial complexion has not been reported yet. This paper aims to make both qualitative and quantitative analysis for facial complexion. We propose a novel feature representation of facial complexion from the whole face of patients. The features are established with four chromaticity bases splitting up by luminance distribution on CIELAB color space. Chromaticity bases are constructed from facial dominant color using two-level clustering; the optimal luminance distribution is simply implemented with experimental comparisons. The features are proved to be more distinctive than the previous facial complexion feature representation. Complexion recognition proceeds by training an SVM classifier with the optimal model parameters. In addition, further improved features are more developed by the weighted fusion of five local regions. Extensive experimental results show that the proposed features achieve highest facial color recognition performance with a total accuracy of 86.89%. And, furthermore, the proposed recognition framework could analyze both color and gloss degrees of facial complexion by learning a ranking function. PMID:24967342

Automated Discovery of Machine-Specific Code Improvements

DTIC Science & Technology

1984-12-01

operation of the source language. Additional analysis may reveal special features of the target architecture that may be exploited to generate efficient...Additional analysis may reveal special features of the target architecture that may be exploited to generate efficient code. Such analysis is optional...incorporate knowledge of the source language, but do not refer to features of the target machine. These early phases are sometimes referred to as the

Relating sub-surface ice features to physiological stress in a climate sensitive mammal, the American pika (Ochotona princeps).

PubMed

Wilkening, Jennifer L; Ray, Chris; Varner, Johanna

2015-01-01

The American pika (Ochotona princeps) is considered a sentinel species for detecting ecological effects of climate change. Pikas are declining within a large portion of their range, and ongoing research suggests loss of sub-surface ice as a mechanism. However, no studies have demonstrated physiological responses of pikas to sub-surface ice features. Here we present the first analysis of physiological stress in pikas living in and adjacent to habitats underlain by ice. Fresh fecal samples were collected non-invasively from two adjacent sites in the Rocky Mountains (one with sub-surface ice and one without) and analyzed for glucocorticoid metabolites (GCM). We also measured sub-surface microclimates in each habitat. Results indicate lower GCM concentration in sites with sub-surface ice, suggesting that pikas are less stressed in favorable microclimates resulting from sub-surface ice features. GCM response was well predicted by habitat characteristics associated with sub-surface ice features, such as lower mean summer temperatures. These results suggest that pikas inhabiting areas without sub-surface ice features are experiencing higher levels of physiological stress and may be more susceptible to changing climates. Although post-deposition environmental effects can confound analyses based on fecal GCM, we found no evidence for such effects in this study. Sub-surface ice features are key to water cycling and storage and will likely represent an increasingly important component of water resources in a warming climate. Fecal samples collected from additional watersheds as part of current pika monitoring programs could be used to further characterize relationships between pika stress and sub-surface ice features.

Categorical and associative relations increase false memory relative to purely associative relations.

PubMed

Coane, Jennifer H; McBride, Dawn M; Termonen, Miia-Liisa; Cutting, J Cooper

2016-01-01

The goal of the present study was to examine the contributions of associative strength and similarity in terms of shared features to the production of false memories in the Deese/Roediger-McDermott list-learning paradigm. Whereas the activation/monitoring account suggests that false memories are driven by automatic associative activation from list items to nonpresented lures, combined with errors in source monitoring, other accounts (e.g., fuzzy trace theory, global-matching models) emphasize the importance of semantic-level similarity, and thus predict that shared features between list and lure items will increase false memory. Participants studied lists of nine items related to a nonpresented lure. Half of the lists consisted of items that were associated but did not share features with the lure, and the other half included items that were equally associated but also shared features with the lure (in many cases, these were taxonomically related items). The two types of lists were carefully matched in terms of a variety of lexical and semantic factors, and the same lures were used across list types. In two experiments, false recognition of the critical lures was greater following the study of lists that shared features with the critical lure, suggesting that similarity at a categorical or taxonomic level contributes to false memory above and beyond associative strength. We refer to this phenomenon as a "feature boost" that reflects additive effects of shared meaning and association strength and is generally consistent with accounts of false memory that have emphasized thematic or feature-level similarity among studied and nonstudied representations.

Incorporating clinical metadata with digital image features for automated identification of cutaneous melanoma.

PubMed

Liu, Z; Sun, J; Smith, M; Smith, L; Warr, R

2013-11-01

Computer-assisted diagnosis (CAD) of malignant melanoma (MM) has been advocated to help clinicians to achieve a more objective and reliable assessment. However, conventional CAD systems examine only the features extracted from digital photographs of lesions. Failure to incorporate patients' personal information constrains the applicability in clinical settings. To develop a new CAD system to improve the performance of automatic diagnosis of melanoma, which, for the first time, incorporates digital features of lesions with important patient metadata into a learning process. Thirty-two features were extracted from digital photographs to characterize skin lesions. Patients' personal information, such as age, gender and, lesion site, and their combinations, was quantified as metadata. The integration of digital features and metadata was realized through an extended Laplacian eigenmap, a dimensionality-reduction method grouping lesions with similar digital features and metadata into the same classes. The diagnosis reached 82.1% sensitivity and 86.1% specificity when only multidimensional digital features were used, but improved to 95.2% sensitivity and 91.0% specificity after metadata were incorporated appropriately. The proposed system achieves a level of sensitivity comparable with experienced dermatologists aided by conventional dermoscopes. This demonstrates the potential of our method for assisting clinicians in diagnosing melanoma, and the benefit it could provide to patients and hospitals by greatly reducing unnecessary excisions of benign naevi. This paper proposes an enhanced CAD system incorporating clinical metadata into the learning process for automatic classification of melanoma. Results demonstrate that the additional metadata and the mechanism to incorporate them are useful for improving CAD of melanoma. © 2013 British Association of Dermatologists.

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

Evaluation of feature selection algorithms for classification in temporal lobe epilepsy based on MR images

NASA Astrophysics Data System (ADS)

Lai, Chunren; Guo, Shengwen; Cheng, Lina; Wang, Wensheng; Wu, Kai

2017-02-01

It's very important to differentiate the temporal lobe epilepsy (TLE) patients from healthy people and localize the abnormal brain regions of the TLE patients. The cortical features and changes can reveal the unique anatomical patterns of brain regions from the structural MR images. In this study, structural MR images from 28 normal controls (NC), 18 left TLE (LTLE), and 21 right TLE (RTLE) were acquired, and four types of cortical feature, namely cortical thickness (CTh), cortical surface area (CSA), gray matter volume (GMV), and mean curvature (MCu), were explored for discriminative analysis. Three feature selection methods, the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE), were investigated to extract dominant regions with significant differences among the compared groups for classification using the SVM classifier. The results showed that the SVM-REF achieved the highest performance (most classifications with more than 92% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and gray volume matter exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical features were combined. Additionally, the dominant regions with higher classification weights were mainly located in temporal and frontal lobe, including the inferior temporal, entorhinal cortex, fusiform, parahippocampal cortex, middle frontal and frontal pole. It was demonstrated that the cortical features provided effective information to determine the abnormal anatomical pattern and the proposed method has the potential to improve the clinical diagnosis of the TLE.

CDKL5-Related Disorders: From Clinical Description to Molecular Genetics.

PubMed

Bahi-Buisson, N; Bienvenu, T

2012-04-01

Mutations in the cyclin-dependent kinase-like 5 gene (CDKL5) have been described in girls with Rett-like features and early-onset epileptic encephalopathy including infantile spasms. To date, with more than 80 reported cases, the phenotype of CDKL5-related encephalopathy is better defined. The main features consist of early-onset seizures starting before 5 months of age, severe mental retardation with absent speech and Rett-like features such as hand stereotypies and deceleration of head growth. On the other hand, neuro-vegetative signs and developmental regression are rare in CDKL5 mutation patients. The CDKL5 gene encodes a serine threonine kinase protein which is characterized by a catalytic domain and a long C-terminal extension involved in the regulation of the catalytic activity of CDKL5 and in the sub-nuclear localization of the protein. To our knowledge, more than 70 different point mutations have been described including missense mutations within the catalytic domain, nonsense mutations causing the premature termination of the protein distributed in the entire open reading frame, splice variants, and frameshift mutations. Additionally, CDKL5 mutations have recently been described in 7 males with a more severe epileptic encephalopathy and a worse outcome compared to female patients. Finally, about 23 male and female patients have been identified with gross rearrangements encompassing all or part of the CDKL5 gene, with a phenotype reminiscent of CDKL5-related encephalopathy combined with dysmorphic features. Even if recent data clearly indicate that CDKL5 plays an important role in brain function, the protein remains largely uncharacterized. Phenotype-genotype correlation is additionally hampered by the relatively small number of patients described.

CDKL5-Related Disorders: From Clinical Description to Molecular Genetics

PubMed Central

Bahi-Buisson, N.; Bienvenu, T.

2012-01-01

Mutations in the cyclin-dependent kinase-like 5 gene (CDKL5) have been described in girls with Rett-like features and early-onset epileptic encephalopathy including infantile spasms. To date, with more than 80 reported cases, the phenotype of CDKL5-related encephalopathy is better defined. The main features consist of early-onset seizures starting before 5 months of age, severe mental retardation with absent speech and Rett-like features such as hand stereotypies and deceleration of head growth. On the other hand, neuro-vegetative signs and developmental regression are rare in CDKL5 mutation patients. The CDKL5 gene encodes a serine threonine kinase protein which is characterized by a catalytic domain and a long C-terminal extension involved in the regulation of the catalytic activity of CDKL5 and in the sub-nuclear localization of the protein. To our knowledge, more than 70 different point mutations have been described including missense mutations within the catalytic domain, nonsense mutations causing the premature termination of the protein distributed in the entire open reading frame, splice variants, and frameshift mutations. Additionally, CDKL5 mutations have recently been described in 7 males with a more severe epileptic encephalopathy and a worse outcome compared to female patients. Finally, about 23 male and female patients have been identified with gross rearrangements encompassing all or part of the CDKL5 gene, with a phenotype reminiscent of CDKL5-related encephalopathy combined with dysmorphic features. Even if recent data clearly indicate that CDKL5 plays an important role in brain function, the protein remains largely uncharacterized. Phenotype-genotype correlation is additionally hampered by the relatively small number of patients described. PMID:22670135

A deep learning framework for modeling structural features of RNA-binding protein targets

PubMed Central

Zhang, Sai; Zhou, Jingtian; Hu, Hailin; Gong, Haipeng; Chen, Ligong; Cheng, Chao; Zeng, Jianyang

2016-01-01

RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https://github.com/thucombio/deepnet-rbp. PMID:26467480

Imaging of Spontaneous and Traumatic Cervical Artery Dissection : Comparison of Typical CT Angiographic Features.

PubMed

Sporns, Peter B; Niederstadt, Thomas; Heindel, Walter; Raschke, Michael J; Hartensuer, René; Dittrich, Ralf; Hanning, Uta

2018-01-26

Cervical artery dissection (CAD) is an important etiology of ischemic stroke and early recognition is vital to protect patients from the major complication of cerebral embolization by administration of anticoagulants. The etiology of arterial dissections differ and can be either spontaneous or traumatic. Even though the historical gold standard is still catheter angiography, recent studies suggest a good performance of computed tomography angiography (CTA) for detection of CAD. We conducted this research to evaluate the variety and frequency of possible imaging signs of spontaneous and traumatic CAD and to guide neuroradiologists' decision making. Retrospective review of the database of our multiple injured patients admitted to the Department of Trauma, Hand, and Reconstructive Surgery of the University Hospital Münster in Germany (a level 1 trauma center) for patients with traumatic CAD (tCAD) and of our stroke database (2008-2015) for patients with spontaneous CAD (sCAD) and CT/CTA on initial clinical work-up. All images were evaluated concerning specific and sensitive radiological features for dissection by two experienced neuroradiologists. Imaging features were compared between the two etiologies. This study included 145 patients (99 male, 46 female; 45 ± 18.8 years of age), consisting of 126 dissected arteries with a traumatic and 43 with spontaneous etiology. Intimal flaps were more frequently observed after traumatic etiology (58.1% tCADs, 6.9% sCADs; p < 0.001); additionally, multivessel dissections were much more frequent in trauma patients (3 sCADs, 21 tCADs) and only less than half (42%) of the patients with traumatic dissections showed cervical spine fractures. Neuroradiologists should be aware that intimal flaps and multivessel dissections are more common after a traumatic etiology. In addition, it seems important to conduct a CTA in a trauma setting, even if no cervical spine fracture is detected.

Dispersion engineering of mode-locked fibre lasers

NASA Astrophysics Data System (ADS)

Woodward, R. I.

2018-03-01

Mode-locked fibre lasers are important sources of ultrashort pulses, where stable pulse generation is achieved through a balance of periodic amplitude and phase evolutions. A range of distinct cavity pulse dynamics have been revealed, arising from the interplay between dispersion and nonlinearity in addition to dissipative processes such as filtering. This has led to the discovery of numerous novel operating regimes, offering significantly improved laser performance. In this Topical Review, we summarise the main steady-state pulse dynamics reported to date through cavity dispersion engineering, including average solitons, dispersion-managed solitons, dissipative solitons, giant-chirped pulses and similaritons. Characteristic features and the stabilisation mechanism of each regime are described, supported by numerical modelling, in addition to the typical performance and limitations. Opportunities for further pulse energy scaling are discussed, in addition to considering other recent advances including automated self-tuning cavities and fluoride-fibre-based mid-infrared mode-locked lasers.

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images.

PubMed

Guo, Shengwen; Lai, Chunren; Wu, Congling; Cen, Guiyin

2017-01-01

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI-cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI-NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI-NC comparison. The best performances obtained by the SVM classifier using the essential features were 5-40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.

Dry etching of chrome for photomasks for 100-nm technology using chemically amplified resist

NASA Astrophysics Data System (ADS)

Mueller, Mark; Komarov, Serguie; Baik, Ki-Ho

2002-07-01

Photo mask etching for the 100nm technology node places new requirements on dry etching processes. As the minimum-size features on the mask, such as assist bars and optical proximity correction (OPC) patterns, shrink down to 100nm, it is necessary to produce etch CD biases of below 20nm in order to reproduce minimum resist features into chrome with good pattern fidelity. In addition, vertical profiles are necessary. In previous generations of photomask technology, footing and sidewall profile slope were tolerated, since this dry etch profile was an improvement from wet etching. However, as feature sizes shrink, it is extremely important to select etch processes which do not generate a foot, because this will affect etch linearity and also limit the smallest etched feature size. Chemically amplified resist (CAR) from TOK is patterned with a 50keV MEBES eXara e-beam writer, allowing for patterning of small features with vertical resist profiles. This resist is developed for raster scan 50 kV e-beam systems. It has high contrast, good coating characteristics, good dry etch selectivity, and high environmental stability. Chrome etch process development has been performed using Design of Experiments to optimize parameters such as sidewall profile, etch CD bias, etch CD linearity for varying sizes of line/space patterns, etch CD linearity for varying sizes of isolated lines and spaces, loading effects, and application to contact etching.

MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features.

PubMed

Xu, Juan; Li, Chuan-Xing; Li, Yong-Sheng; Lv, Jun-Ying; Ma, Ye; Shao, Ting-Ting; Xu, Liang-De; Wang, Ying-Ying; Du, Lei; Zhang, Yun-Peng; Jiang, Wei; Li, Chun-Quan; Xiao, Yun; Li, Xia

2011-02-01

Synergistic regulations among multiple microRNAs (miRNAs) are important to understand the mechanisms of complex post-transcriptional regulations in humans. Complex diseases are affected by several miRNAs rather than a single miRNA. So, it is a challenge to identify miRNA synergism and thereby further determine miRNA functions at a system-wide level and investigate disease miRNA features in the miRNA-miRNA synergistic network from a new view. Here, we constructed a miRNA-miRNA functional synergistic network (MFSN) via co-regulating functional modules that have three features: common targets of corresponding miRNA pairs, enriched in the same gene ontology category and close proximity in the protein interaction network. Predicted miRNA synergism is validated by significantly high co-expression of functional modules and significantly negative regulation to functional modules. We found that the MFSN exhibits a scale free, small world and modular architecture. Furthermore, the topological features of disease miRNAs in the MFSN are distinct from non-disease miRNAs. They have more synergism, indicating their higher complexity of functions and are the global central cores of the MFSN. In addition, miRNAs associated with the same disease are close to each other. The structure of the MFSN and the features of disease miRNAs are validated to be robust using different miRNA target data sets.

True-Triaxial Experimental Study of the Evolutionary Features of the Acoustic Emissions and Sounds of Rockburst Processes

NASA Astrophysics Data System (ADS)

Su, Guoshao; Shi, Yanjiong; Feng, Xiating; Jiang, Jianqing; Zhang, Jie; Jiang, Quan

2018-02-01

Rockbursts are markedly characterized by the ejection of rock fragments from host rocks at certain speeds. The rockburst process is always accompanied by acoustic signals that include acoustic emissions (AE) and sounds. A deep insight into the evolutionary features of AE and sound signals is important to improve the accuracy of rockburst prediction. To investigate the evolutionary features of AE and sound signals, rockburst tests on granite rock specimens under true-triaxial loading conditions were performed using an improved rockburst testing system, and the AE and sounds during rockburst development were recorded and analyzed. The results show that the evolutionary features of the AE and sound signals were obvious and similar. On the eve of a rockburst, a `quiescent period' could be observed in both the evolutionary process of the AE hits and the sound waveform. Furthermore, the time-dependent fractal dimensions of the AE hits and sound amplitude both showed a tendency to continuously decrease on the eve of the rockbursts. In addition, on the eve of the rockbursts, the main frequency of the AE and sound signals both showed decreasing trends, and the frequency spectrum distributions were both characterized by low amplitudes, wide frequency bands and multiple peak shapes. Thus, the evolutionary features of sound signals on the eve of rockbursts, as well as that of AE signals, can be used as beneficial information for rockburst prediction.

What automated age estimation of hand and wrist MRI data tells us about skeletal maturation in male adolescents.

PubMed

Urschler, Martin; Grassegger, Sabine; Štern, Darko

2015-01-01

Age estimation of individuals is important in human biology and has various medical and forensic applications. Recent interest in MR-based methods aims to investigate alternatives for established methods involving ionising radiation. Automatic, software-based methods additionally promise improved estimation objectivity. To investigate how informative automatically selected image features are regarding their ability to discriminate age, by exploring a recently proposed software-based age estimation method for MR images of the left hand and wrist. One hundred and two MR datasets of left hand images are used to evaluate age estimation performance, consisting of bone and epiphyseal gap volume localisation, computation of one age regression model per bone mapping image features to age and fusion of individual bone age predictions to a final age estimate. Quantitative results of the software-based method show an age estimation performance with a mean absolute difference of 0.85 years (SD = 0.58 years) to chronological age, as determined by a cross-validation experiment. Qualitatively, it is demonstrated how feature selection works and which image features of skeletal maturation are automatically chosen to model the non-linear regression function. Feasibility of automatic age estimation based on MRI data is shown and selected image features are found to be informative for describing anatomical changes during physical maturation in male adolescents.

Attention-Based Recurrent Temporal Restricted Boltzmann Machine for Radar High Resolution Range Profile Sequence Recognition.

PubMed

Zhang, Yifan; Gao, Xunzhang; Peng, Xuan; Ye, Jiaqi; Li, Xiang

2018-05-16

The High Resolution Range Profile (HRRP) recognition has attracted great concern in the field of Radar Automatic Target Recognition (RATR). However, traditional HRRP recognition methods failed to model high dimensional sequential data efficiently and have a poor anti-noise ability. To deal with these problems, a novel stochastic neural network model named Attention-based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM) is proposed in this paper. RTRBM is utilized to extract discriminative features and the attention mechanism is adopted to select major features. RTRBM is efficient to model high dimensional HRRP sequences because it can extract the information of temporal and spatial correlation between adjacent HRRPs. The attention mechanism is used in sequential data recognition tasks including machine translation and relation classification, which makes the model pay more attention to the major features of recognition. Therefore, the combination of RTRBM and the attention mechanism makes our model effective for extracting more internal related features and choose the important parts of the extracted features. Additionally, the model performs well with the noise corrupted HRRP data. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our proposed model outperforms other traditional methods, which indicates that ARTRBM extracts, selects, and utilizes the correlation information between adjacent HRRPs effectively and is suitable for high dimensional data or noise corrupted data.

Optimized Kernel Entropy Components.

PubMed

Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau

2017-06-01

This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

Effect of wine addition on microbiological characteristics, volatile molecule profiles and biogenic amine contents in fermented sausages.

PubMed

Coloretti, Fabio; Tabanelli, Giulia; Chiavari, Cristiana; Lanciotti, Rosalba; Grazia, Luigi; Gardini, Fausto; Montanari, Chiara

2014-03-01

The aim was to evaluate the effect of wine addition during manufacturing of dry fermented sausages, in terms of safety aspects (biogenic amine accumulation), aroma profile and sensory characteristics. Three batches of salami were produced: without wine addition and with 7.5% or 15% (v/w) of white wine. The fermented sausages showed characteristics that can increase product diversification. Some of the sensory features (i.e. increased salty perception) can represent an important strategy because of the trend to reduce salt intake for health reasons. The presence of wine immediately reduced the pH and is a source of ethanol, which can have an inhibitory effect against undesirable microflora. The microbiological results observed regarding Enterobacteriaceae and enterococci were encouraging. The addition of wine did not negatively affect the ripening time or increase the presence of biogenic amines. The samples containing wine showed reduced concentrations of putrescine. Copyright © 2013 Elsevier Ltd. All rights reserved.

A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN.

PubMed

Wen, Tingxi; Zhang, Zhongnan; Qiu, Ming; Zeng, Ming; Luo, Weizhen

2017-01-01

The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features.

Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta

NASA Astrophysics Data System (ADS)

Zhang, Hongsheng; Xu, Ru

2018-02-01

Integrating synthetic aperture radar (SAR) and optical data to improve urban land cover classification has been identified as a promising approach. However, which integration level is the most suitable remains unclear but important to many researchers and engineers. This study aimed to compare different integration levels for providing a scientific reference for a wide range of studies using optical and SAR data. SAR data from TerraSAR-X and ENVISAT ASAR in both WSM and IMP modes were used to be combined with optical data at pixel level, feature level and decision levels using four typical machine learning methods. The experimental results indicated that: 1) feature level that used both the original images and extracted features achieved a significant improvement of up to 10% compared to that using optical data alone; 2) different levels of fusion required different suitable methods depending on the data distribution and data resolution. For instance, support vector machine was the most stable at both the feature and decision levels, while random forest was suitable at the pixel level but not suitable at the decision level. 3) By examining the distribution of SAR features, some features (e.g., homogeneity) exhibited a close-to-normal distribution, explaining the improvement from the maximum likelihood method at the feature and decision levels. This indicated the benefits of using texture features from SAR data when being combined with optical data for land cover classification. Additionally, the research also shown that combining optical and SAR data does not guarantee improvement compared with using single data source for urban land cover classification, depending on the selection of appropriate fusion levels and fusion methods.

The Detection of 6.9 μm Emission Features in the Infrared Spectra of IRAS 04296+3429, IRAS 05341+0852, and IRAS 22272+5435: Evidence for the Presence of Hn-PAHs in Post-AGB Stars

NASA Astrophysics Data System (ADS)

Materese, Christopher K.; Bregman, Jesse D.; Sandford, Scott A.

2017-12-01

Polycyclic aromatic hydrocarbons (PAHs) are generally believed to be ubiquitous in space and responsible for numerous telltale interstellar infrared emission bands. In Sandford et al., we suggested that PAHs with excess hydrogenation at their periphery ({{{H}}}{{n}}-PAHs) may be an important subclass of these molecules in some astrophysical environments. These molecules are candidates to explain objects with anomalously large 3.4 μm features, which are presumed to be associated with the aliphatic C-H stretching vibrations of the excess hydrogen. In that work, we suggest that for Hn-PAHs to be a viable candidate as the source for this 3.4 μm feature, we must also expect to observe methylene scissoring modes at 6.9 μm. In this work, we continue to develop the {{{H}}}{{n}} - {PAH} hypothesis with a focus on the 6.9 μm feature. We also present some new observations of three post-asymptotic giant branch (post-AGB) objects with abnormally large 3.4 μm features, IRAS 04296+3429, IRAS 05341+0852, and IRAS 22272+5435, in addition to one post-AGB object with normal PAH emissions, IRAS 20000+3239. These observations were made using the FORCAST instrument in grism mode on the Stratospheric Observatory for Infrared Astronomy aircraft and demonstrate the presence of a 6.9 μm feature for the three objects with abnormally large 3.4 μm features and no detectable 6.9 μm feature for the normal PAH emitter. These results are consistent with the hypothesis that Hn-PAHs are a possible source of these infrared emission bands.

[Advances in metabolic engineering of Escherichia coli for isoprene biosynthesis].

PubMed

Guo, Jing; Cao, Yujin; Xian, Mo; Liu, Huizhou

2016-08-25

As an important industrial chemical, isoprene is mainly used as a precursor for synthetic rubbers. In addition, it also has wide applications in the field of pharmaceutical and chemical intermediates, food, adhesives and aviation fuel. Compared with conventional petrochemical routes, production of isoprene in microbial systems has been the research focus considering environment friendly and sustainable development features. This article summarizes the metabolic pathways and key enzymes of isoprene biosynthesis, reviews current methods and strategies in improving isoprene production of Escherichia coli, and also gives some basic ideas and expectation.

Nonstate Security Threats in Africa: Challenges for U.S. Engagement

DTIC Science & Technology

2010-12-01

kidnapping instead le SAge PRISM 2, no. 1 FeatuReS | 67 East Asia has led to a spike in elephant poach - ing. In June 2002, Singapore seized 6.2 metric...thousands of people in a country with no functioning cen- tral government since the 1980s.23 In addition to the impact on the lives and families of...percent of U.S. for- eign oil imports by 2015,25 and militant attacks impacting West African oil already affect the price of oil on global stock markets

The new Mobile Command Center at KSC is important addition to emergency preparedness

NASA Technical Reports Server (NTRS)

2000-01-01

This new specially equipped vehicle serves as a mobile command center for emergency preparedness staff and other support personnel when needed at KSC or Cape Canaveral Air Force Station. It features a conference room, computer work stations, mobile telephones and a fax machine. It also can generate power with its onboard generator. Besides being ready to respond in case of emergencies during launches, the vehicle must be ready to help address fires, security threats, chemical spills, terrorist attaches, weather damage or other critical situations that might face KSC or CCAFS.

Spiral Galaxy Lensing: A Model with Twist

NASA Astrophysics Data System (ADS)

Bell, Steven R.; Ernst, Brett; Fancher, Sean; Keeton, Charles R.; Komanduru, Abi; Lundberg, Erik

2014-12-01

We propose a single galaxy gravitational lensing model with a mass density that has a spiral structure. Namely, we extend the arcsine gravitational lens (a truncated singular isothermal elliptical model), adding an additional parameter that controls the amount of spiraling in the structure of the mass density. An important feature of our model is that, even though the mass density is sophisticated, we succeed in integrating the deflection term in closed form using a Gauss hypergeometric function. When the spiraling parameter is set to zero, this reduces to the arcsine lens.

Imaging experiment: The Viking Lander

USGS Publications Warehouse

Mutch, T.A.; Binder, A.B.; Huck, F.O.; Levinthal, E.C.; Morris, E.C.; Sagan, C.; Young, A.T.

1972-01-01

The Viking Lander Imaging System will consist of two identical facsimile cameras. Each camera has a high-resolution mode with an instantaneous field of view of 0.04??, and survey and color modes with instantaneous fields of view of 0.12??. Cameras are positioned one meter apart to provide stereoscopic coverage of the near-field. The Imaging Experiment will provide important information about the morphology, composition, and origin of the Martian surface and atmospheric features. In addition, lander pictures will provide supporting information for other experiments in biology, organic chemistry, meteorology, and physical properties. ?? 1972.

Issues or Identity? Cognitive Foundations of Voter Choice

PubMed Central

Jenke, Libby; Huettel, Scott A.

2016-01-01

Voter choice is one of the most important problems in political science. The most common models assume that voting is a rational choice based on policy positions (e.g., key issues) and non-policy information (e.g., social identity, personality). Though such models explain macroscopic features of elections, they also reveal important anomalies that have been resistant to explanation. We argue for a new approach that builds upon recent research in cognitive science and neuroscience; specifically, we contend that policy positions and social identities do not combine in merely an additive manner, but compete to determine voter preferences. This model not only explains several key anomalies in voter choice, but also suggests new directions for research in both political science and cognitive science. PMID:27769726

Nickel-Catalyzed Suzuki-Miyaura Cross-Coupling in a Green Alcohol Solvent for an Undergraduate Organic Chemistry Laboratory.

PubMed

Hie, Liana; Chang, Jonah J; Garg, Neil K

2015-03-10

A modern undergraduate organic chemistry laboratory experiment involving the Suzuki-Miyaura coupling is reported. Although Suzuki-Miyaura couplings typically employ palladium catalysts in environmentally harmful solvents, this experiment features the use of inexpensive nickel catalysis, in addition to a "green" alcohol solvent. The experiment employs heterocyclic substrates, which are important pharmaceutical building blocks. Thus, this laboratory procedure exposes students to a variety of contemporary topics in organic chemistry, including transition metal-catalyzed cross-couplings, green chemistry, and the importance of heterocycles in drug discovery, none of which are well represented in typical undergraduate organic chemistry curricula. The experimental protocol uses commercially available reagents and is useful in both organic and inorganic instructional laboratories.

Dissociable roles of internal feelings and face recognition ability in facial expression decoding.

PubMed

Zhang, Lin; Song, Yiying; Liu, Ling; Liu, Jia

2016-05-15

The problem of emotion recognition has been tackled by researchers in both affective computing and cognitive neuroscience. While affective computing relies on analyzing visual features from facial expressions, it has been proposed that humans recognize emotions by internally simulating the emotional states conveyed by others' expressions, in addition to perceptual analysis of facial features. Here we investigated whether and how our internal feelings contributed to the ability to decode facial expressions. In two independent large samples of participants, we observed that individuals who generally experienced richer internal feelings exhibited a higher ability to decode facial expressions, and the contribution of internal feelings was independent of face recognition ability. Further, using voxel-based morphometry, we found that the gray matter volume (GMV) of bilateral superior temporal sulcus (STS) and the right inferior parietal lobule was associated with facial expression decoding through the mediating effect of internal feelings, while the GMV of bilateral STS, precuneus, and the right central opercular cortex contributed to facial expression decoding through the mediating effect of face recognition ability. In addition, the clusters in bilateral STS involved in the two components were neighboring yet separate. Our results may provide clues about the mechanism by which internal feelings, in addition to face recognition ability, serve as an important instrument for humans in facial expression decoding. Copyright © 2016 Elsevier Inc. All rights reserved.

Effects of moiré lattice structure on electronic properties of graphene

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

Huang, Lunan; Wu, Yun; Hershberger, M. T.

Here, we study structural and electronic properties of graphene grown on silicone carbide (SiC) substrate using a scanning tunneling microscope, spot-profile-analysis low-energy electron diffraction, and angle-resolved photoemission spectroscopy. We find several new replicas of Dirac cones in the Brillouin zone. Their locations can be understood in terms of a combination of basis vectors linked to SiC 6 × 6 and graphene 6√3×6√3 reconstruction. Therefore, these new features originate from the moiré caused by the lattice mismatch between SiC and graphene. More specifically, Dirac cone replicas are caused by underlying weak modulation of the ionic potential by the substrate that ismore » then experienced by the electrons in the graphene. We also demonstrate that this effect is equally strong in single- and trilayer graphene; therefore, the additional Dirac cones are intrinsic features rather than the result of photoelectron diffraction. These new features in the electronic structure are very important for the interpretation of recent transport measurements and can assist in tuning the properties of graphene for practical applications.« less

Automatic detection and classification of artifacts in single-channel EEG.

PubMed

Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W; Sorensen, Helge B D

2014-01-01

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.

QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.

Prediction of distal residue participation in enzyme catalysis

PubMed Central

Brodkin, Heather R; DeLateur, Nicholas A; Somarowthu, Srinivas; Mills, Caitlyn L; Novak, Walter R; Beuning, Penny J; Ringe, Dagmar; Ondrechen, Mary Jo

2015-01-01

A scoring method for the prediction of catalytically important residues in enzyme structures is presented and used to examine the participation of distal residues in enzyme catalysis. Scores are based on the Partial Order Optimum Likelihood (POOL) machine learning method, using computed electrostatic properties, surface geometric features, and information obtained from the phylogenetic tree as input features. Predictions of distal residue participation in catalysis are compared with experimental kinetics data from the literature on variants of the featured enzymes; some additional kinetics measurements are reported for variants of Pseudomonas putida nitrile hydratase (ppNH) and for Escherichia coli alkaline phosphatase (AP). The multilayer active sites of P. putida nitrile hydratase and of human phosphoglucose isomerase are predicted by the POOL log ZP scores, as is the single-layer active site of P. putida ketosteroid isomerase. The log ZP score cutoff utilized here results in over-prediction of distal residue involvement in E. coli alkaline phosphatase. While fewer experimental data points are available for P. putida mandelate racemase and for human carbonic anhydrase II, the POOL log ZP scores properly predict the previously reported participation of distal residues. PMID:25627867

Application of ERTS-1 imagery to detecting and mapping modern erosion features and to monitoring erosional changes, in southern Arizona

NASA Technical Reports Server (NTRS)

Morrison, R. B. (Principal Investigator); Cooley, M. E.

1973-01-01

The author has identified the following significant results. The chief results during the reporting period were three 1:1,000,000 scale maps made from one ERTS-1 frame (1085-17330, 16 October 1972) showing: (1) the three most important types of materials in terms of the modern erosion problem: the readily erodible soils, gravel piedmonts and basin-fill areas, and consolidated rocks; (2) alluvial fans (dissected and relatively undissected); and (3) (as an additional bonus) linear structural features. Eight key areas (small parts of the whole study area) were selected for detailed study, and mapping was started in two of them, by interpretation of ultrahigh (U-2 and RB-57) airphotos, supplemented by field studies. In these areas detailed mapping was done not only on the modern erosion phenomena (arroyos, gullies, modern flood plains and terraces, and areas of sheet erosion and deposition), but also other features pertinent to the erosion problem, such as slope-local relief, landforms rock units, soil particle size and erodibility, and classes of vegetative cover.

Dissociative features in posttraumatic stress disorder: A latent profile analysis.

PubMed

Műllerová, Jana; Hansen, Maj; Contractor, Ateka A; Elhai, Jon D; Armour, Cherie

2016-09-01

The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) characterizes the dissociative subtype of posttraumatic stress disorder (PTSD) in terms of the individual meeting the criteria for PTSD and additionally reporting symptoms of depersonalization and/or derealization. The current study aimed to examine whether a dissociative PTSD profile may include alternative features of dissociation and whether it could be differentiated from a nondissociative PTSD profile on certain psychopathologies and demographics. Data from 309 trauma-exposed participants, collected through Amazon Mechanical Turk, were subjected to latent profile analysis. Regression analyses were used to examine the predictors of latent classes. Three discrete profiles named Baseline, PTSD, and Dissociative profile were uncovered. All examined features of dissociation were significantly elevated in the Dissociative profile. Anxiety, male sex, being employed, and having a minority racial background significantly predicted the Dissociative profile relative to the PTSD profile. The study points to the importance of alternative symptoms of dissociation in the dissociative PTSD subtype beyond the symptoms of depersonalization and derealization. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Effects of moiré lattice structure on electronic properties of graphene

NASA Astrophysics Data System (ADS)

Huang, Lunan; Wu, Yun; Hershberger, M. T.; Mou, Daixiang; Schrunk, Benjamin; Tringides, Michael C.; Hupalo, Myron; Kaminski, Adam

2017-07-01

We study structural and electronic properties of graphene grown on silicone carbide (SiC) substrate using a scanning tunneling microscope, spot-profile-analysis low-energy electron diffraction, and angle-resolved photoemission spectroscopy. We find several new replicas of Dirac cones in the Brillouin zone. Their locations can be understood in terms of a combination of basis vectors linked to SiC 6 × 6 and graphene 6 √{3 }×6 √{3 } reconstruction. Therefore, these new features originate from the moiré caused by the lattice mismatch between SiC and graphene. More specifically, Dirac cone replicas are caused by underlying weak modulation of the ionic potential by the substrate that is then experienced by the electrons in the graphene. We also demonstrate that this effect is equally strong in single- and trilayer graphene; therefore, the additional Dirac cones are intrinsic features rather than the result of photoelectron diffraction. These new features in the electronic structure are very important for the interpretation of recent transport measurements and can assist in tuning the properties of graphene for practical applications.

Wireless medical sensor networks: design requirements and enabling technologies.

PubMed

Vallejos de Schatz, Cecilia H; Medeiros, Henry Ponti; Schneider, Fabio K; Abatti, Paulo J

2012-06-01

This article analyzes wireless communication protocols that could be used in healthcare environments (e.g., hospitals and small clinics) to transfer real-time medical information obtained from noninvasive sensors. For this purpose the features of the three currently most widely used protocols-namely, Bluetooth(®) (IEEE 802.15.1), ZigBee (IEEE 802.15.4), and Wi-Fi (IEEE 802.11)-are evaluated and compared. The important features under consideration include data bandwidth, frequency band, maximum transmission distance, encryption and authentication methods, power consumption, and current applications. In addition, an overview of network requirements with respect to medical sensor features, patient safety and patient data privacy, quality of service, and interoperability between other sensors is briefly presented. Sensor power consumption is also discussed because it is considered one of the main obstacles for wider adoption of wireless networks in medical applications. The outcome of this assessment will be a useful tool in the hands of biomedical engineering researchers. It will provide parameters to select the most effective combination of protocols to implement a specific wireless network of noninvasive medical sensors to monitor patients remotely in the hospital or at home.

Multiple Representations-Based Face Sketch-Photo Synthesis.

PubMed

Peng, Chunlei; Gao, Xinbo; Wang, Nannan; Tao, Dacheng; Li, Xuelong; Li, Jie

2016-11-01

Face sketch-photo synthesis plays an important role in law enforcement and digital entertainment. Most of the existing methods only use pixel intensities as the feature. Since face images can be described using features from multiple aspects, this paper presents a novel multiple representations-based face sketch-photo-synthesis method that adaptively combines multiple representations to represent an image patch. In particular, it combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches. The proposed framework could be solved using an alternating optimization strategy and it normally converges in only five outer iterations in the experiments. Our experimental results on the Chinese University of Hong Kong (CUHK) face sketch database, celebrity photos, CUHK Face Sketch FERET Database, IIIT-D Viewed Sketch Database, and forensic sketches demonstrate the effectiveness of our method for face sketch-photo synthesis. In addition, cross-database and database-dependent style-synthesis evaluations demonstrate the generalizability of this novel method and suggest promising solutions for face identification in forensic science.

Effects of moiré lattice structure on electronic properties of graphene

DOE PAGES

Huang, Lunan; Wu, Yun; Hershberger, M. T.; ...

2017-07-10

Here, we study structural and electronic properties of graphene grown on silicone carbide (SiC) substrate using a scanning tunneling microscope, spot-profile-analysis low-energy electron diffraction, and angle-resolved photoemission spectroscopy. We find several new replicas of Dirac cones in the Brillouin zone. Their locations can be understood in terms of a combination of basis vectors linked to SiC 6 × 6 and graphene 6√3×6√3 reconstruction. Therefore, these new features originate from the moiré caused by the lattice mismatch between SiC and graphene. More specifically, Dirac cone replicas are caused by underlying weak modulation of the ionic potential by the substrate that ismore » then experienced by the electrons in the graphene. We also demonstrate that this effect is equally strong in single- and trilayer graphene; therefore, the additional Dirac cones are intrinsic features rather than the result of photoelectron diffraction. These new features in the electronic structure are very important for the interpretation of recent transport measurements and can assist in tuning the properties of graphene for practical applications.« less

Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning

PubMed Central

2017-01-01

This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively. PMID:28464010

Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning.

PubMed

Arribas-Bel, Daniel; Patino, Jorge E; Duque, Juan C

2017-01-01

This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively.

Change blindness in pigeons (Columba livia): the effects of change salience and timing

PubMed Central

Herbranson, Walter T.

2015-01-01

Change blindness is a well-established phenomenon in humans, in which plainly visible changes in the environment go unnoticed. Recently a parallel change blindness phenomenon has been demonstrated in pigeons. The reported experiment follows up on this finding by investigating whether change salience affects change blindness in pigeons the same way it affects change blindness in humans. Birds viewed alternating displays of randomly generated lines back-projected onto three response keys, with one or more line features on a single key differing between consecutive displays. Change salience was manipulated by varying the number of line features that changed on the critical response key. Results indicated that change blindness is reduced if a change is made more salient, and this matches previous human results. Furthermore, accuracy patterns indicate that pigeons’ effective search area expanded over the course of a trial to encompass a larger portion of the stimulus environment. Thus, the data indicate two important aspects of temporal cognition. First, the timing of a change has a profound influence on whether or not that change will be perceived. Second, pigeons appear to engage in a serial search for changes, in which additional time is required to search additional locations. PMID:26284021

User-Independent Motion State Recognition Using Smartphone Sensors

PubMed Central

Gu, Fuqiang; Kealy, Allison; Khoshelham, Kourosh; Shang, Jianga

2015-01-01

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy. PMID:26690163

User-Independent Motion State Recognition Using Smartphone Sensors.

PubMed

Gu, Fuqiang; Kealy, Allison; Khoshelham, Kourosh; Shang, Jianga

2015-12-04

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users' data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people's motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human's motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

Comparing object recognition from binary and bipolar edge images for visual prostheses.

PubMed

Jung, Jae-Hyun; Pu, Tian; Peli, Eli

2016-11-01

Visual prostheses require an effective representation method due to the limited display condition which has only 2 or 3 levels of grayscale in low resolution. Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary (black and white) edge images have been used to represent features to convey essential information. However, in scenes with a complex cluttered background, the recognition rate of the binary edge images by human observers is limited and additional information is required. The polarity of edges and cusps (black or white features on a gray background) carries important additional information; the polarity may provide shape from shading information missing in the binary edge image. This depth information may be restored by using bipolar edges. We compared object recognition rates from 16 binary edge images and bipolar edge images by 26 subjects to determine the possible impact of bipolar filtering in visual prostheses with 3 or more levels of grayscale. Recognition rates were higher with bipolar edge images and the improvement was significant in scenes with complex backgrounds. The results also suggest that erroneous shape from shading interpretation of bipolar edges resulting from pigment rather than boundaries of shape may confound the recognition.

What to Build for Middle-Agers to Come? Attractive and Necessary Functions of Exercise-Promotion Mobile Phone Apps: A Cross-Sectional Study.

PubMed

Liao, Gen-Yih; Chien, Yu-Tai; Chen, Yu-Jen; Hsiung, Hsiao-Fang; Chen, Hsiao-Jung; Hsieh, Meng-Hua; Wu, Wen-Jie

2017-05-25

Physical activity is important for middle-agers to maintain health both in middle age and in old age. Although thousands of exercise-promotion mobile phone apps are available for download, current literature offers little understanding regarding which design features can enhance middle-aged adults' quality perception toward exercise-promotion apps and which factor may influence such perception. The aims of this study were to understand (1) which design features of exercise-promotion apps can enhance quality perception of middle-agers, (2) whether their needs are matched by current functions offered in app stores, and (3) whether physical activity (PA) and mobile phone self-efficacy (MPSE) influence quality perception. A total of 105 middle-agers participated and filled out three scales: the International Physical Activity Questionnaire (IPAQ), the MPSE scale, and the need for design features questionnaire. The design features were developed based on the Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy. Following the Kano quality model, the need for design features questionnaire asked participants to classify design features into five categories: attractive, one-dimensional, must-be, indifferent, and reverse. The quality categorization was conducted based on a voting approach and the categorization results were compared with the findings of a prevalence study to realize whether needs match current availability. In total, 52 multinomial logistic regression models were analyzed to evaluate the effects of PA level and MPSE on quality perception of design features. The Kano analysis on the total sample revealed that visual demonstration of exercise instructions is the only attractive design feature, whereas the other 51 design features were perceived with indifference. Although examining quality perception by PA level, 21 features are recommended to low level, 6 features to medium level, but none to high-level PA. In contrast, high-level MPSE is recommended with 14 design features, medium level with 6 features, whereas low-level participants are recommended with 1 feature. The analysis suggests that the implementation of demanded features could be low, as the average prevalence of demanded design features is 20% (4.3/21). Surprisingly, social comparison and social support, most implemented features in current apps, were categorized into the indifferent category. The magnitude of effect is larger for MPSE because it effects quality perception of more design features than PA. Delving into the 52 regression models revealed that high MPSE more likely induces attractive or one- dimensional categorization, suggesting the importance of technological self-efficacy on eHealth care promotion. This study is the first to propose middle-agers' needs in relation to mobile phone exercise-promotion. In addition to the tailor-made recommendations, suggestions are offered to app designers to enhance the performance of persuasive features. An interesting finding on change of quality perception attributed to MPSE is proposed as future research. ©Gen-Yih Liao, Yu-Tai Chien, Yu-Jen Chen, Hsiao-Fang Hsiung, Hsiao-Jung Chen, Meng-Hua Hsieh, Wen-Jie Wu. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 25.05.2017.

Resliced image space construction for coronary artery collagen fibers.

PubMed

Luo, Tong; Chen, Huan; Kassab, Ghassan S

2017-01-01

Collagen fibers play an important role in the biomechanics of the blood vessel wall. The objective of this study was to determine the 3D microstructure of collagen fibers in the media and adventitia of coronary arteries. We present a novel optimal angle consistence algorithm to reform image slices in the visualization and analysis of 3D collagen images. 3D geometry was reconstructed from resliced image space where the 3D skeleton was extracted as the primary feature for accurate reconstruction of geometrical parameters. Collagen fibers (range 80-200) were reconstructed from the porcine coronary artery wall for the measurement of various morphological parameters. Collagen waviness and diameters were 1.37 ± 0.19 and 2.61 ± 0.89 μm, respectively. The biaxial distributions of orientation had two different peaks at 110.7 ± 25.2° and 18.4 ± 19.3°. Results for width, waviness, and orientation were found to be in good agreement with manual measurements. In addition to accurately measuring 2D features more efficiently than the manual approach, the present method produced 3D features that could not be measured in the 2D manual approach. These additional parameters included the tilt angle (5.10 ± 2.95°) and cross-sectional area (CSA; 5.98 ± 3.79 μm2) of collagen fibers. These 3D collagen reconstructions provide accurate and reliable microstructure for biomechanical modeling of vessel wall mechanics.

Functional constraints on tooth morphology in carnivorous mammals

PubMed Central

2012-01-01

Background The range of potential morphologies resulting from evolution is limited by complex interacting processes, ranging from development to function. Quantifying these interactions is important for understanding adaptation and convergent evolution. Using three-dimensional reconstructions of carnivoran and dasyuromorph tooth rows, we compared statistical models of the relationship between tooth row shape and the opposing tooth row, a static feature, as well as measures of mandibular motion during chewing (occlusion), which are kinetic features. This is a new approach to quantifying functional integration because we use measures of movement and displacement, such as the amount the mandible translates laterally during occlusion, as opposed to conventional morphological measures, such as mandible length and geometric landmarks. By sampling two distantly related groups of ecologically similar mammals, we study carnivorous mammals in general rather than a specific group of mammals. Results Statistical model comparisons demonstrate that the best performing models always include some measure of mandibular motion, indicating that functional and statistical models of tooth shape as purely a function of the opposing tooth row are too simple and that increased model complexity provides a better understanding of tooth form. The predictors of the best performing models always included the opposing tooth row shape and a relative linear measure of mandibular motion. Conclusions Our results provide quantitative support of long-standing hypotheses of tooth row shape as being influenced by mandibular motion in addition to the opposing tooth row. Additionally, this study illustrates the utility and necessity of including kinetic features in analyses of morphological integration. PMID:22899809

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.

Characteristics of residential areas and transportational walking among frail and non-frail Dutch elderly: does the size of the area matter?

PubMed

Etman, Astrid; Kamphuis, Carlijn B M; Prins, Richard G; Burdorf, Alex; Pierik, Frank H; van Lenthe, Frank J

2014-03-04

A residential area supportive for walking may facilitate elderly to live longer independently. However, current evidence on area characteristics potentially important for walking among older persons is mixed. This study hypothesized that the importance of area characteristics for transportational walking depends on the size of the area characteristics measured, and older person's frailty level. The study population consisted of 408 Dutch community-dwelling persons aged 65 years and older participating in the Elderly And their Neighborhood (ELANE) study in 2011-2012. Characteristics (aesthetics, functional features, safety, and destinations) of areas surrounding participants' residences ranging from a buffer of 400 meters up to 1600 meters (based on walking path networks) were linked with self-reported transportational walking using linear regression analyses. In addition, interaction effects between frailty level and area characteristics were tested. An increase in functional features (e.g. presence of sidewalks and benches) within a 400 meter buffer, in aesthetics (e.g. absence of litter and graffiti) within 800 and 1200 meter buffers, and an increase of one destination per buffer of 400 and 800 meters were associated with more transportational walking, up to 2.89 minutes per two weeks (CI 1.07-7.32; p < 0.05). No differences were found between frail and non-frail elderly. Better functional and aesthetic features, and more destinations in the residential area of community-dwelling older persons were associated with more transportational walking. The importance of area characteristics for transportational walking differs by area size, but not by frailty level. Neighbourhood improvements may increase transportational walking among older persons, thereby contributing to living longer independently.

Multidetector CT features of pulmonary focal ground-glass opacity: differences between benign and malignant

PubMed Central

Fan, L; Liu, S-Y; Li, Q-C; Yu, H; Xiao, X-S

2012-01-01

Objective To evaluate different features between benign and malignant pulmonary focal ground-glass opacity (fGGO) on multidetector CT (MDCT). Methods 82 pathologically or clinically confirmed fGGOs were retrospectively analysed with regard to demographic data, lesion size and location, attenuation value and MDCT features including shape, margin, interface, internal characteristics and adjacent structure. Differences between benign and malignant fGGOs were analysed using a χ2 test, Fisher's exact test or Mann–Whitney U-test. Morphological characteristics were analysed by binary logistic regression analysis to estimate the likelihood of malignancy. Results There were 21 benign and 61 malignant lesions. No statistical differences were found between benign and malignant fGGOs in terms of demographic data, size, location and attenuation value. The frequency of lobulation (p=0.000), spiculation (p=0.008), spine-like process (p=0.004), well-defined but coarse interface (p=0.000), bronchus cut-off (p=0.003), other air-containing space (p=0.000), pleural indentation (p=0.000) and vascular convergence (p=0.006) was significantly higher in malignant fGGOs than that in benign fGGOs. Binary logistic regression analysis showed that lobulation, interface and pleural indentation were important indicators for malignant diagnosis of fGGO, with the corresponding odds ratios of 8.122, 3.139 and 9.076, respectively. In addition, a well-defined but coarse interface was the most important indicator of malignancy among all interface types. With all three important indicators considered, the diagnostic sensitivity, specificity and accuracy were 93.4%, 66.7% and 86.6%, respectively. Conclusion An fGGO with lobulation, a well-defined but coarse interface and pleural indentation gives a greater than average likelihood of being malignant. PMID:22128130

Atlas and feature based 3D pathway visualization enhancement for skull base pre-operative fast planning from head CT

NASA Astrophysics Data System (ADS)

Aghdasi, Nava; Li, Yangming; Berens, Angelique; Moe, Kris S.; Bly, Randall A.; Hannaford, Blake

2015-03-01

Minimally invasive neuroendoscopic surgery provides an alternative to open craniotomy for many skull base lesions. These techniques provides a great benefit to the patient through shorter ICU stays, decreased post-operative pain and quicker return to baseline function. However, density of critical neurovascular structures at the skull base makes planning for these procedures highly complex. Furthermore, additional surgical portals are often used to improve visualization and instrument access, which adds to the complexity of pre-operative planning. Surgical approach planning is currently limited and typically involves review of 2D axial, coronal, and sagittal CT and MRI images. In addition, skull base surgeons manually change the visualization effect to review all possible approaches to the target lesion and achieve an optimal surgical plan. This cumbersome process relies heavily on surgeon experience and it does not allow for 3D visualization. In this paper, we describe a rapid pre-operative planning system for skull base surgery using the following two novel concepts: importance-based highlight and mobile portal. With this innovation, critical areas in the 3D CT model are highlighted based on segmentation results. Mobile portals allow surgeons to review multiple potential entry portals in real-time with improved visualization of critical structures located inside the pathway. To achieve this we used the following methods: (1) novel bone-only atlases were manually generated, (2) orbits and the center of the skull serve as features to quickly pre-align the patient's scan with the atlas, (3) deformable registration technique was used for fine alignment, (4) surgical importance was assigned to each voxel according to a surgical dictionary, and (5) pre-defined transfer function was applied to the processed data to highlight important structures. The proposed idea was fully implemented as independent planning software and additional data are used for verification and validation. The experimental results show: (1) the proposed methods provided greatly improved planning efficiency while optimal surgical plans were successfully achieved, (2) the proposed methods successfully highlighted important structures and facilitated planning, (3) the proposed methods require shorter processing time than classical segmentation algorithms, and (4) these methods can be used to improve surgical safety for surgical robots.

Fully-automated identification of fish species based on otolith contour: using short-time Fourier transform and discriminant analysis (STFT-DA).

PubMed

Salimi, Nima; Loh, Kar Hoe; Kaur Dhillon, Sarinder; Chong, Ving Ching

2016-01-01

Background. Fish species may be identified based on their unique otolith shape or contour. Several pattern recognition methods have been proposed to classify fish species through morphological features of the otolith contours. However, there has been no fully-automated species identification model with the accuracy higher than 80%. The purpose of the current study is to develop a fully-automated model, based on the otolith contours, to identify the fish species with the high classification accuracy. Methods. Images of the right sagittal otoliths of 14 fish species from three families namely Sciaenidae, Ariidae, and Engraulidae were used to develop the proposed identification model. Short-time Fourier transform (STFT) was used, for the first time in the area of otolith shape analysis, to extract important features of the otolith contours. Discriminant Analysis (DA), as a classification technique, was used to train and test the model based on the extracted features. Results. Performance of the model was demonstrated using species from three families separately, as well as all species combined. Overall classification accuracy of the model was greater than 90% for all cases. In addition, effects of STFT variables on the performance of the identification model were explored in this study. Conclusions. Short-time Fourier transform could determine important features of the otolith outlines. The fully-automated model proposed in this study (STFT-DA) could predict species of an unknown specimen with acceptable identification accuracy. The model codes can be accessed at http://mybiodiversityontologies.um.edu.my/Otolith/ and https://peerj.com/preprints/1517/. The current model has flexibility to be used for more species and families in future studies.

The fundamental role of subcellular topography in peripheral nerve repair therapies.

PubMed

Spivey, Eric C; Khaing, Zin Z; Shear, Jason B; Schmidt, Christine E

2012-06-01

Clinical evidence suggests that nano- and microtopography incorporated into scaffolds does not merely improve peripheral nerve regeneration, but is in fact a prerequisite for meaningful restoration of nerve function. Although the biological mechanisms involved are not fully understood, grafts incorporating physical guides that mimic microscopic nerve tissue features (e.g., basal laminae) appear to provide a significant advantage over grafts that rely on purely chemical or macroscopic similarities to nerve tissue. Investigators consistently demonstrate the fundamental importance of nano- and micro-scale physical features for appropriate cell response in a wide range of biological scenarios. Additionally, recent in vivo research demonstrates that nerve regeneration scaffolds with cell-scale physical features are more effective than those that rely only on chemical or macro-scale features. Physical guidance at the cell-scale is especially important for long (>20 mm) nerve defects, for which the only reliable treatment is the autologous nerve graft. The lack of other available options exposes a clear need for the application of nano- and microfabrication techniques that will allow the next generation of engineered nerve guides to more closely mimic native tissue at those scales. This review examines current research to determine what elements of cell-scale topography in experimental scaffolds are most effective at stimulating functional recovery, and then presents an overview of fabrication techniques that could potentially improve future treatment paradigms. Relative advantages and disadvantages of these techniques are discussed, with respect to both clinical adaptation and likely effectiveness. Our intent is to more clearly delineate the remaining obstacles in the development of a next generation nerve guide, particularly for long defects, and offer new perspectives on steering current technologies towards clinically viable solutions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Machine learning techniques in searches for$$t\\bar{t}$$h in the h → $$b\\bar{b}$$ decay channel

DOE PAGES

Santos, Robert; Nguyen, M.; Webster, Jordan; ...

2017-04-10

Study of the production of pairs of top quarks in association with a Higgs boson is one of the primary goals of the Large Hadron Collider over the next decade, as measurements of this process may help us to understand whether the uniquely large mass of the top quark plays a special role in electroweak symmetry breaking. Higgs bosons decay predominantly to bmore » $$\\bar{_b}$$, yielding signatures for the signal that are similar to t$$\\bar{_t}$$ + jets with heavy flavor. Though particularly challenging to study due to the similar kinematics between signal and background events, such final states (t$$\\bar{_t}$$b$$\\bar{b}$$) are an important channel for studying the top quark Yukawa coupling. This paper presents a systematic study of machine learning (ML) methods for detecting t$$\\bar{_t}$$h in the h → b$$\\bar{b}$$ decay channel. Among the seven ML methods tested, we show that neural network models outperform alternative methods. In addition, two neural models used in this paper outperform NeuroBayes, one of the standard algorithms used in current particle physics experiments. We further study the effectiveness of ML algorithms by investigating the impact of feature set and data size, as well as the depth of the networks for neural models. We demonstrate that an extended feature set leads to improvement of performance over basic features. Furthermore, the availability of large samples for training is found to be important for improving the performance of the techniques. For the features and the data set studied here, neural networks of more layers deliver comparable performance to their simpler counterparts.« less

Machine learning techniques in searches for$$t\\bar{t}$$h in the h → $$b\\bar{b}$$ decay channel

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

Santos, Robert; Nguyen, M.; Webster, Jordan

Study of the production of pairs of top quarks in association with a Higgs boson is one of the primary goals of the Large Hadron Collider over the next decade, as measurements of this process may help us to understand whether the uniquely large mass of the top quark plays a special role in electroweak symmetry breaking. Higgs bosons decay predominantly to bmore » $$\\bar{_b}$$, yielding signatures for the signal that are similar to t$$\\bar{_t}$$ + jets with heavy flavor. Though particularly challenging to study due to the similar kinematics between signal and background events, such final states (t$$\\bar{_t}$$b$$\\bar{b}$$) are an important channel for studying the top quark Yukawa coupling. This paper presents a systematic study of machine learning (ML) methods for detecting t$$\\bar{_t}$$h in the h → b$$\\bar{b}$$ decay channel. Among the seven ML methods tested, we show that neural network models outperform alternative methods. In addition, two neural models used in this paper outperform NeuroBayes, one of the standard algorithms used in current particle physics experiments. We further study the effectiveness of ML algorithms by investigating the impact of feature set and data size, as well as the depth of the networks for neural models. We demonstrate that an extended feature set leads to improvement of performance over basic features. Furthermore, the availability of large samples for training is found to be important for improving the performance of the techniques. For the features and the data set studied here, neural networks of more layers deliver comparable performance to their simpler counterparts.« less

PredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.

PubMed

Wang, Huilin; Wang, Mingjun; Tan, Hao; Li, Yuan; Zhang, Ziding; Song, Jiangning

2014-01-01

X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed 'PredPPCrys' using the support vector machine (SVM). Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I). Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II), which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization targets of currently non-crystallizable proteins were provided as compendium data, which are anticipated to facilitate target selection and design for the worldwide structural genomics consortium. PredPPCrys is freely available at http://www.structbioinfor.org/PredPPCrys.

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

End-to-End Multimodal Emotion Recognition Using Deep Neural Networks

NASA Astrophysics Data System (ADS)

Tzirakis, Panagiotis; Trigeorgis, George; Nicolaou, Mihalis A.; Schuller, Bjorn W.; Zafeiriou, Stefanos

2017-12-01

Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using auditory and visual modalities. To capture the emotional content for various styles of speaking, robust features need to be extracted. To this purpose, we utilize a Convolutional Neural Network (CNN) to extract features from the speech, while for the visual modality a deep residual network (ResNet) of 50 layers. In addition to the importance of feature extraction, a machine learning algorithm needs also to be insensitive to outliers while being able to model the context. To tackle this problem, Long Short-Term Memory (LSTM) networks are utilized. The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.

Statistical molecular design of balanced compound libraries for QSAR modeling.

PubMed

Linusson, A; Elofsson, M; Andersson, I E; Dahlgren, M K

2010-01-01

A fundamental step in preclinical drug development is the computation of quantitative structure-activity relationship (QSAR) models, i.e. models that link chemical features of compounds with activities towards a target macromolecule associated with the initiation or progression of a disease. QSAR models are computed by combining information on the physicochemical and structural features of a library of congeneric compounds, typically assembled from two or more building blocks, and biological data from one or more in vitro assays. Since the models provide information on features affecting the compounds' biological activity they can be used as guides for further optimization. However, in order for a QSAR model to be relevant to the targeted disease, and drug development in general, the compound library used must contain molecules with balanced variation of the features spanning the chemical space believed to be important for interaction with the biological target. In addition, the assays used must be robust and deliver high quality data that are directly related to the function of the biological target and the associated disease state. In this review, we discuss and exemplify the concept of statistical molecular design (SMD) in the selection of building blocks and final synthetic targets (i.e. compounds to synthesize) to generate information-rich, balanced libraries for biological testing and computation of QSAR models.

A neural joint model for entity and relation extraction from biomedical text.

PubMed

Li, Fei; Zhang, Meishan; Fu, Guohong; Ji, Donghong

2017-03-31

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

Classification of Error Related Brain Activity in an Auditory Identification Task with Conditions of Varying Complexity

NASA Astrophysics Data System (ADS)

Kakkos, I.; Gkiatis, K.; Bromis, K.; Asvestas, P. A.; Karanasiou, I. S.; Ventouras, E. M.; Matsopoulos, G. K.

2017-11-01

The detection of an error is the cognitive evaluation of an action outcome that is considered undesired or mismatches an expected response. Brain activity during monitoring of correct and incorrect responses elicits Event Related Potentials (ERPs) revealing complex cerebral responses to deviant sensory stimuli. Development of accurate error detection systems is of great importance both concerning practical applications and in investigating the complex neural mechanisms of decision making. In this study, data are used from an audio identification experiment that was implemented with two levels of complexity in order to investigate neurophysiological error processing mechanisms in actors and observers. To examine and analyse the variations of the processing of erroneous sensory information for each level of complexity we employ Support Vector Machines (SVM) classifiers with various learning methods and kernels using characteristic ERP time-windowed features. For dimensionality reduction and to remove redundant features we implement a feature selection framework based on Sequential Forward Selection (SFS). The proposed method provided high accuracy in identifying correct and incorrect responses both for actors and for observers with mean accuracy of 93% and 91% respectively. Additionally, computational time was reduced and the effects of the nesting problem usually occurring in SFS of large feature sets were alleviated.

Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging.

PubMed

Wang, Rui; Li, Rui; Lei, Yanyan; Zhu, Quing

2015-01-01

Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset- the Wisconsin Diagnostic Breast Cancer (WDBC), revealed the different accuracy of a SVM classification in terms of the number of features used and the parameters selected. A pattern recognition system is proposed by means of SVM-Recursive Feature Elimination (RFE) with the Radial Basis Function (RBF) kernel. To improve the effectiveness and robustness of the system, an optimized tuning ensemble algorithm called as SVM-RFE(C) with correlation filter was implemented to quantify feature and parameter information based on cross validation. The proposed algorithm is first demonstrated outperforming SVM-RFE on WDBC. Then the best accuracy of 94.643% and sensitivity of 94.595% were achieved when using SVM-RFE(C) to test 57 new PAT data from 19 patients. The experiment results show that the classifier constructed with SVM-RFE(C) algorithm is able to learn additional information from new data and has significant potential in ovarian cancer diagnosis.

Disease named entity recognition from biomedical literature using a novel convolutional neural network.

PubMed

Zhao, Zhehuan; Yang, Zhihao; Luo, Ling; Wang, Lei; Zhang, Yin; Lin, Hongfei; Wang, Jian

2017-12-28

Automatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intensive and time-consuming. Though most deep learning methods can solve NER problems with little feature engineering, they employ additional CRF layer to capture the correlation information between labels in neighborhoods which makes them much complicated. In this paper, we propose a novel multiple label convolutional neural network (MCNN) based disease NER approach. In this approach, instead of the CRF layer, a multiple label strategy (MLS) first introduced by us, is employed. First, the character-level embedding, word-level embedding and lexicon feature embedding are concatenated. Then several convolutional layers are stacked over the concatenated embedding. Finally, MLS strategy is applied to the output layer to capture the correlation information between neighboring labels. As shown by the experimental results, MCNN can achieve the state-of-the-art performance on both NCBI and CDR corpora. The proposed MCNN based disease NER method achieves the state-of-the-art performance with little feature engineering. And the experimental results show the MLS strategy's effectiveness of capturing the correlation information between labels in the neighborhood.

Ligand-based and structure-based approaches in identifying ideal pharmacophore against c-Jun N-terminal kinase-3.

PubMed

Kumar, B V S Suneel; Kotla, Rohith; Buddiga, Revanth; Roy, Jyoti; Singh, Sardar Shamshair; Gundla, Rambabu; Ravikumar, Muttineni; Sarma, Jagarlapudi A R P

2011-01-01

Structure and ligand based pharmacophore modeling and docking studies carried out using diversified set of c-Jun N-terminal kinase-3 (JNK3) inhibitors are presented in this paper. Ligand based pharmacophore model (LBPM) was developed for 106 inhibitors of JNK3 using a training set of 21 compounds to reveal structural and chemical features necessary for these molecules to inhibit JNK3. Hypo1 consisted of two hydrogen bond acceptors (HBA), one hydrogen bond donor (HBD), and a hydrophobic (HY) feature with a correlation coefficient (r²) of 0.950. This pharmacophore model was validated using test set containing 85 inhibitors and had a good r² of 0.846. All the molecules were docked using Glide software and interestingly, all the docked conformations showed hydrogen bond interactions with important hinge region amino acids (Gln155 and Met149)and these interactions were compared with Hypo1 features. The results of ligand based pharmacophore model (LBPM)and docking studies are validated each other. The structure based pharmacophore model (SBPM) studies have identified additional features, two hydrogen bond donors and one hydrogen bond acceptor. The combination of these methodologies is useful in designing ideal pharmacophore which provides a powerful tool for the discovery of novel and selective JNK3 inhibitors.

The role of MicroRNA molecules and MicroRNA-regulating machinery in the pathogenesis and progression of epithelial ovarian cancer.

PubMed

Wang, Xiyin; Ivan, Mircea; Hawkins, Shannon M

2017-11-01

MicroRNA molecules are small, single-stranded RNA molecules that function to regulate networks of genes. They play important roles in normal female reproductive tract biology, as well as in the pathogenesis and progression of epithelial ovarian cancer. DROSHA, DICER, and Argonaute proteins are components of the microRNA-regulatory machinery and mediate microRNA production and function. This review discusses aberrant expression of microRNA molecules and microRNA-regulating machinery associated with clinical features of epithelial ovarian cancer. Understanding the regulation of microRNA molecule production and function may facilitate the development of novel diagnostic and therapeutic strategies to improve the prognosis of women with epithelial ovarian cancer. Additionally, understanding microRNA molecules and microRNA-regulatory machinery associations with clinical features may influence prevention and early detection efforts. Copyright © 2017 Elsevier Inc. All rights reserved.

Desktop Nanofabrication with Massively Multiplexed Beam Pen Lithography

PubMed Central

Liao, Xing; Brown, Keith A.; Schmucker, Abrin L.; Liu, Guoliang; He, Shu; Shim, Wooyoung; Mirkin, Chad A.

2013-01-01

The development of a lithographic method that can rapidly define nanoscale features across centimeter-scale surfaces has been a long standing goal of the nanotechnology community. If such a ‘desktop nanofab’ could be implemented in a low-cost format, it would bring the possibility of point-of-use nanofabrication for rapidly prototyping diverse functional structures. Here we report the development of a new tool that is capable of writing arbitrary patterns composed of diffraction-unlimited features over square centimeter areas that are in registry with existing patterns and nanostructures. Importantly, this instrument is based on components that are inexpensive compared to the combination of state-of-the-art nanofabrication tools that approach its capabilities. This tool can be used to prototype functional electronic devices in a mask-free fashion in addition to providing a unique platform for performing high throughput nano- to macroscale photochemistry with relevance to biology and medicine. PMID:23868336

Deep learning with convolutional neural network in radiology.

PubMed

Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

2018-04-01

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Flying insect detection and classification with inexpensive sensors.

PubMed

Chen, Yanping; Why, Adena; Batista, Gustavo; Mafra-Neto, Agenor; Keogh, Eamonn

2014-10-15

An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect's flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.

Advanced manufacturing—A transformative enabling capability for fusion

DOE PAGES

Nygren, Richard E.; Dehoff, Ryan R.; Youchison, Dennis L.; ...

2018-05-24

Additive Manufacturing (AM) can create novel and complex engineered material structures. Features such as controlled porosity, micro-fibers and/or nano-particles, transitions in materials and integral robust coatings can be important in developing solutions for fusion subcomponents. A realistic understanding of this capability would be particularly valuable in identifying development paths. Major concerns for using AM processes with lasers or electron beams that melt powder to make refractory parts are the power required and residual stresses arising in fabrication. A related issue is the required combination of lasers or e-beams to continue heating of deposited material (to reduce stresses) and to depositmore » new material at a reasonable built rate while providing adequate surface finish and resolution for meso-scale features. In conclusion, Some Direct Write processes that can make suitable preforms and be cured to an acceptable density may offer another approach for PFCs.« less

Ecosystem features determine seagrass community response to sea otter foraging

USGS Publications Warehouse

Hessing-Lewis, Margot; Rechsteiner, Erin U.; Hughes, Brent B.; Tinker, M. Tim; Monteith, Zachary L.; Olson, Angeleen M.; Henderson, Matthew Morgan; Watson, Jane C.

2017-01-01

Comparing sea otter recovery in California (CA) and British Columbia (BC) reveals key ecosystem properties that shape top-down effects in seagrass communities. We review potential ecosystem drivers of sea otter foraging in CA and BC seagrass beds, including the role of coastline complexity and environmental stress on sea otter effects. In BC, we find greater species richness across seagrass trophic assemblages. Furthermore, Cancer spp. crabs, an important link in the seagrass trophic cascade observed in CA, are less common. Additionally, the more recent reintroduction of sea otters, more complex coastline, and reduced environmental stress in BC seagrass habitats supports the hypotheses that sea otter foraging pressure is currently reduced there. In order to manage the ecosystem features that lead to regional differences in top predator effects in seagrass communities, we review our findings, their spatial and temporal constraints, and present a social-ecological framework for future research.

Hereditary spastic paraplegia.

PubMed

Blackstone, Craig

2018-01-01

The hereditary spastic paraplegias (HSPs) are a heterogeneous group of neurologic disorders with the common feature of prominent lower-extremity spasticity, resulting from a length-dependent axonopathy of corticospinal upper motor neurons. The HSPs exist not only in "pure" forms but also in "complex" forms that are associated with additional neurologic and extraneurologic features. The HSPs are among the most genetically diverse neurologic disorders, with well over 70 distinct genetic loci, for which about 60 mutated genes have already been identified. Numerous studies elucidating the molecular pathogenesis underlying HSPs have highlighted the importance of basic cellular functions - especially membrane trafficking, mitochondrial function, organelle shaping and biogenesis, axon transport, and lipid/cholesterol metabolism - in axon development and maintenance. An encouragingly small number of converging cellular pathogenic themes have been identified for the most common HSPs, and some of these pathways present compelling targets for future therapies. Copyright © 2018 Elsevier B.V. All rights reserved.

13C NMR spectroscopy of the insoluble carbon of carbonaceous chondrites

NASA Technical Reports Server (NTRS)

Cronin, J. R.; Pizzarello, S.; Frye, J. S.

1987-01-01

13C NMR spectra have been obtained of the insoluble carbon residues resulting from HF-digestion of three carbonaceous chondrites, Orgueil (C1), Murchison (CM2), and Allende (CV3). Spectra obtained using the cross polarization magic-angle spinning technique show two major features attributable respectively to carbon in aliphatic/olefinic structures. The spectrum obtained from the Allende sample was weak, presumably as a consequence of its low hydrogen content. Single pulse excitation spectra, which do not depend on 1H-13C polarization transfer for signal enhancement were also obtained. These spectra, which may be more representative of the total carbon in the meteorite samples, indicate a greater content of carbon in aromatic/olefinic structures. These results suggest that extensive polycyclic aromatic sheets are important structural features of the insoluble carbon of all three meteorites. The Orgueil and Murchison materials contain additional hydrogenated aromatic/olefinic and aliphatic groups.

Bat-to-human: spike features determining 'host jump' of coronaviruses SARS-CoV, MERS-CoV, and beyond.

PubMed

Lu, Guangwen; Wang, Qihui; Gao, George F

2015-08-01

Both severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) are zoonotic pathogens that crossed the species barriers to infect humans. The mechanism of viral interspecies transmission is an important scientific question to be addressed. These coronaviruses contain a surface-located spike (S) protein that initiates infection by mediating receptor-recognition and membrane fusion and is therefore a key factor in host specificity. In addition, the S protein needs to be cleaved by host proteases before executing fusion, making these proteases a second determinant of coronavirus interspecies infection. Here, we summarize the progress made in the past decade in understanding the cross-species transmission of SARS-CoV and MERS-CoV by focusing on the features of the S protein, its receptor-binding characteristics, and the cleavage process involved in priming. Copyright © 2015 Elsevier Ltd. All rights reserved.

Construction of Lower Limbs Rehabilitation System Based on Bodily Features and EMG

NASA Astrophysics Data System (ADS)

Kushida, Daisuke; Kanazawa, Tomohiro; Kitamura, Akira

In rehabilitation, there are two roles of reinforcement of the expansion of the motion range and muscular power. The diseased part is requested to be operated by the external force in the former, and the load corresponding to patient's muscular power is requested to be given in the latter. Recently, there are a lot of researches that try such rehabilitation by the machine. The principal object is put on the motion control of the machine in those researches. The most important thing is a mechanism that patient's state is quantitatively evaluated. This paper proposes the mechanism that presumes the patient recovery by relating bodily features to EMG of the diseased part in rehabilitation. In addition, a new rehabilitation system, that contained the self adjustment of the load using those mechanisms and the consideration of fatigue, is proposed. Effectiveness of the proposed rehabilitation system is verified by the simulation work.

Advanced manufacturing—A transformative enabling capability for fusion

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

Nygren, Richard E.; Dehoff, Ryan R.; Youchison, Dennis L.

Additive Manufacturing (AM) can create novel and complex engineered material structures. Features such as controlled porosity, micro-fibers and/or nano-particles, transitions in materials and integral robust coatings can be important in developing solutions for fusion subcomponents. A realistic understanding of this capability would be particularly valuable in identifying development paths. Major concerns for using AM processes with lasers or electron beams that melt powder to make refractory parts are the power required and residual stresses arising in fabrication. A related issue is the required combination of lasers or e-beams to continue heating of deposited material (to reduce stresses) and to depositmore » new material at a reasonable built rate while providing adequate surface finish and resolution for meso-scale features. In conclusion, Some Direct Write processes that can make suitable preforms and be cured to an acceptable density may offer another approach for PFCs.« less

Combining convolutional neural networks and Hough Transform for classification of images containing lines

NASA Astrophysics Data System (ADS)

Sheshkus, Alexander; Limonova, Elena; Nikolaev, Dmitry; Krivtsov, Valeriy

2017-03-01

In this paper, we propose an expansion of convolutional neural network (CNN) input features based on Hough Transform. We perform morphological contrasting of source image followed by Hough Transform, and then use it as input for some convolutional filters. Thus, CNNs computational complexity and the number of units are not affected. Morphological contrasting and Hough Transform are the only additional computational expenses of introduced CNN input features expansion. Proposed approach was demonstrated on the example of CNN with very simple structure. We considered two image recognition problems, that were object classification on CIFAR-10 and printed character recognition on private dataset with symbols taken from Russian passports. Our approach allowed to reach noticeable accuracy improvement without taking much computational effort, which can be extremely important in industrial recognition systems or difficult problems utilising CNNs, like pressure ridge analysis and classification.

Face-space architectures: evidence for the use of independent color-based features.

PubMed

Nestor, Adrian; Plaut, David C; Behrmann, Marlene

2013-07-01

The concept of psychological face space lies at the core of many theories of face recognition and representation. To date, much of the understanding of face space has been based on principal component analysis (PCA); the structure of the psychological space is thought to reflect some important aspects of a physical face space characterized by PCA applications to face images. In the present experiments, we investigated alternative accounts of face space and found that independent component analysis provided the best fit to human judgments of face similarity and identification. Thus, our results challenge an influential approach to the study of human face space and provide evidence for the role of statistically independent features in face encoding. In addition, our findings support the use of color information in the representation of facial identity, and we thus argue for the inclusion of such information in theoretical and computational constructs of face space.

Plasmonic spectral tunability of conductive ternary nitrides

NASA Astrophysics Data System (ADS)

Kassavetis, S.; Bellas, D. V.; Abadias, G.; Lidorikis, E.; Patsalas, P.

2016-06-01

Conductive binary transition metal nitrides, such as TiN and ZrN, have emerged as a category of promising alternative plasmonic materials. In this work, we show that ternary transition metal nitrides such as TixTa1-xN, TixZr1-xN, TixAl1-xN, and ZrxTa1-xN share the important plasmonic features with their binary counterparts, while having the additional asset of the exceptional spectral tunability in the entire visible (400-700 nm) and UVA (315-400 nm) spectral ranges depending on their net valence electrons. In particular, we demonstrate that such ternary nitrides can exhibit maximum field enhancement factors comparable with gold in the aforementioned broadband range. We also critically evaluate the structural features that affect the quality factor of the plasmon resonance and we provide rules of thumb for the selection and growth of materials for nitride plasmonics.

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

Desktop nanofabrication with massively multiplexed beam pen lithography.

PubMed

Liao, Xing; Brown, Keith A; Schmucker, Abrin L; Liu, Guoliang; He, Shu; Shim, Wooyoung; Mirkin, Chad A

2013-01-01

The development of a lithographic method that can rapidly define nanoscale features across centimetre-scale surfaces has been a long-standing goal for the nanotechnology community. If such a 'desktop nanofab' could be implemented in a low-cost format, it would bring the possibility of point-of-use nanofabrication for rapidly prototyping diverse functional structures. Here we report the development of a new tool that is capable of writing arbitrary patterns composed of diffraction-unlimited features over square centimetre areas that are in registry with existing patterns and nanostructures. Importantly, this instrument is based on components that are inexpensive compared with the combination of state-of-the-art nanofabrication tools that approach its capabilities. This tool can be used to prototype functional electronic devices in a mask-free fashion in addition to providing a unique platform for performing high-throughput nano- to macroscale photochemistry with relevance to biology and medicine.

Helioviewer.org: Enhanced Solar & Heliospheric Data Visualization

NASA Astrophysics Data System (ADS)

Stys, J. E.; Ireland, J.; Hughitt, V. K.; Mueller, D.

2013-12-01

Helioviewer.org enables the simultaneous exploration of multiple heterogeneous solar data sets. In the latest iteration of this open-source web application, Hinode XRT and Yohkoh SXT join SDO, SOHO, STEREO, and PROBA2 as supported data sources. A newly enhanced user-interface expands the utility of Helioviewer.org by adding annotations backed by data from the Heliospheric Events Knowledgebase (HEK). Helioviewer.org can now overlay solar feature and event data via interactive marker pins, extended regions, data labels, and information panels. An interactive time-line provides enhanced browsing and visualization to image data set coverage and solar events. The addition of a size-of-the-Earth indicator provides a sense of the scale to solar and heliospheric features for education and public outreach purposes. Tight integration with the Virtual Solar Observatory and SDO AIA cutout service enable solar physicists to seamlessly import science data into their SSW/IDL or SunPy/Python data analysis environments.

Past, Present, and Future Capabilities of the Transonic Dynamics Tunnel from an Aeroelasticity Perspective

NASA Technical Reports Server (NTRS)

Cole, Stanley R.; Garcia, Jerry L.

2000-01-01

The NASA Langley Transonic Dynamics Tunnel (TDT) has provided a unique capability for aeroelastic testing for forty years. The facility has a rich history of significant contributions to the design of many United States commercial transports, military aircraft, launch vehicles, and spacecraft. The facility has many features that contribute to its uniqueness for aeroelasticity testing, perhaps the most important feature being the use of a heavy gas test medium to achieve higher test densities. Higher test medium densities substantially improve model-building requirements and therefore simplify the fabrication process for building aeroelastically scaled wind tunnel models. Aeroelastic scaling for the heavy gas results in lower model structural frequencies. Lower model frequencies tend to a make aeroelastic testing safer. This paper will describe major developments in the testing capabilities at the TDT throughout its history, the current status of the facility, and planned additions and improvements to its capabilities in the near future.

Barrier scattering with complex-valued quantum trajectories: Taxonomy and analysis of isochrones

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

David, Julianne K.; Wyatt, Robert E.

2008-03-07

To facilitate the search for isochrones when using complex-valued trajectory methods for quantum barrier scattering calculations, the structure and shape of isochrones in the complex plane were studied. Isochrone segments were categorized based on their distinguishing features, which are shared by each situation studied: High and low energy wave packets, scattering from both thick and thin Gaussian and Eckart barriers of varying height. The characteristic shape of the isochrone is a trifurcated system: Trajectories that transmit the barrier are launched from the lower branch (T), while the middle and upper branches form the segments for reflected trajectories (F and B).more » In addition, a model is presented for the curved section of the lower branch (from which transmitted trajectories are launched), and important features of the complex extension of the initial wave packet are identified.« less

Defect generation in electronic devices under plasma exposure: Plasma-induced damage

NASA Astrophysics Data System (ADS)

Eriguchi, Koji

2017-06-01

The increasing demand for higher performance of ULSI circuits requires aggressive shrinkage of device feature sizes in accordance with Moore’s law. Plasma processing plays an important role in achieving fine patterns with anisotropic features in metal-oxide-semiconductor field-effect transistors (MOSFETs). This article comprehensively addresses the negative aspect of plasma processing — plasma-induced damage (PID). PID naturally not only modifies the surface morphology of materials but also degrades the performance and reliability of MOSFETs as a result of defect generation in the materials. Three key mechanisms of PID, i.e., physical, electrical, and photon-irradiation interactions, are overviewed in terms of modeling, characterization techniques, and experimental evidence reported so far. In addition, some of the emerging topics — control of parameter variability in ULSI circuits caused by PID and recovery of PID — are discussed as future perspectives.

Flying Insect Detection and Classification with Inexpensive Sensors

PubMed Central

Chen, Yanping; Why, Adena; Batista, Gustavo; Mafra-Neto, Agenor; Keogh, Eamonn

2014-01-01

An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect’s flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered. PMID:25350921

The widetilde{A}←widetilde{X} ABSORPTION SPECTRUM OF 2-NITROOXYBUTYL PEROXY RADICAL

NASA Astrophysics Data System (ADS)

Eddingsaas, Nathan; Takematsu, Kana; Okumura, Mitchio

2009-06-01

The nitrate radical is an important atmospheric oxidant in the nighttime sky. Nitrate radicals react by addition to alkenes, and in the presence of oxygen form nitrooxyalkyl peroxy radicals. The peroxy radical formed from the reaction of 2-butene, nitrate radical, and oxygen was detected by cavity ringdown spectroscopy (CRDS) via its widetilde{A}←widetilde{X} electronic absorption spectrum. The widetilde{A}←widetilde{X} electronic transition is a bound-bound transition with enough structure to distinguish between different peroxy radicals as well as different conformers of the same peroxy radical. Two conformers of the nitrooxybutyl peroxy radical have been observed; the absorption features are red shifted from the same absorption features of sec-butyl peroxy radical. Calculations on the structure of nitrooxyalkyl peroxy radicals and general trends of the position of the widetilde{A}←widetilde{X} absorption transitions have also been performed and compared to those of unsubstituted peroxy radicals.

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

Chinthavali, Madhu Sudhan; Campbell, Steven L; Tolbert, Leon M

So far, vehicular power electronics integration is limited to the integration of on-board battery chargers (OBC) into the traction drive system and sometimes to the accessory dc/dc converters in plug-in electric vehicles (PEV). These integration approaches do not provide isolation from the grid although it is an important feature that is required for user interface systems that have grid connections. This is therefore a major limitation that needs to be addressed along with the integrated functionality. Furthermore, there is no previous study that proposes the integration of wireless charger with the other on-board components. This study features a unique waymore » of combining the wired and wireless charging functionalities with vehicle side boost converter integration and maintaining the isolation to provide the best solution to the plug-in electric vehicle users. The new topology is additionally compared with commercially available OBC systems from manufacturers.« less

Controlling the intermediate structure of an ionic liquid for f-block element separations

DOE PAGES

Abney, Carter W.; Do, Changwoo; Luo, Huimin; ...

2017-04-19

Recent research has revealed molecular structure beyond the inner coordination sphere is essential in defining the performance of separations processes, but nevertheless remains largely unexplored. Here we apply small angle neutron scattering (SANS) and x-ray absorption fine structure (XAFS) spectroscopy to investigate the structure of an ionic liquid system studied for f-block element separations. SANS data reveal dramatic changes in the ionic liquid microstructure (~150 Å) which we demonstrate can be controlled by judicious selection of counter ion. Mesoscale structural features (> 500 Å) are also observed as a function of metal concentration. XAFS analysis supports formation of extended aggregatemore » structures, similar to those observed in traditional solvent extraction processes, and suggest additional parallels may be drawn from further study. As a result, achieving precise tunability over the intermediate features is an important development in controlling mesoscale structure and realizing advanced new forms of soft matter.« less

Designing action games for appealing to buyers.

PubMed

Hsu, Shang Hwa; Lee, Feng-Liang; Wu, Muh-Cherng

2005-12-01

This study aims to identify design features for action games that would appeal to game-buyers, rather than game-players. Sixteen frequent-buyers of computer games identified 39 design features that appeal to buyers by contrasting different versions of Pacman games. Twenty-eight versions of Pacman were then evaluated in terms of the identified design features by 45 participants (27 male and 18 female college students). Qnet2000 neural network software was used to determine the relative importance of these design features. The results indicated that the top 10 most important design features could account for more than 50% of "perceived fun" among these 39 design features. The feature of avatar is important to game-buyers, yet not revealed in previous player-oriented studies. Moreover, six design factors underlying the 39 features were identified through factor analysis. These factors included "novelty and powerfulness," "appealing presentation," "interactivity," "challenging," "sense of control," and "rewarding," and could account for 54% of total variance. Among these six factors, appealing presentation has not been emphasized by player-oriented research. Implications of the findings were discussed.

Commemorative naming in the United States

USGS Publications Warehouse

,

1999-01-01

Naming is a basic human tendency; it allows us to perceive the distinct identities of people and places and conveys those characteristics that make them unique. The name of a geographic feature can describe spectacular physical attributes (such as the Grand Canyon or Half Dome in Yosemite National Park), indicate cultural or historical significance (such as Washington Crossing on the Delaware River), or commemorate a worthy individual (such as the Hudson River, named for Henry Hudson, the explorer). Names have many different origins, and regardless of the type of name, they give us a greater familiarity with our surroundings and a sense of belonging to our environment. Naming rivers, mountains, and valleys after individuals was one way settlers marked the land; it signified their lives on these lands were important and, in addition to being a point of reference, usually satisfied the need for stability and enhanced the general concept of sense of place. Even today, naming geographic features after individuals helps us to recognize their special achievements and contributions to the physical or cultural landscape. However, what may be most significant about the present commemorative naming decisions is their permanence. It is important for us to realize that the commemorative names assigned today may last for centuries.

Commemorative Naming in the United States

USGS Publications Warehouse

,

1998-01-01

Naming is a basic human tendency; it allows us to perceive the distinct identities of people and places and conveys those characteristics that make them unique. The name of a geographic feature can describe spectacular physical attributes (such as the Grand Canyon or Half Dome in Yosemite National Park), indicate cultural or historical significance (such as Washington Crossing on the Delaware River), or commemorate a worthy individual (such as the Hudson River, named for Henry Hudson, the explorer). Names have many different origins, and regardless of the type of name, they give us a greater familiarity with our surroundings and a sense of belonging to our environment. Naming rivers, mountains, and valleys after individuals was one way settlers marked the land; it signified their lives on these lands were important and, in addition to being a point of reference, usually satisfied the need for stability and enhanced the general concept of sense of place. Even today, naming geographic features after individuals helps us to recognize their special achievements and contributions to the physical or cultural landscape. However, what may be most significant about the present commemorative naming decisions is their permanence. It is important for us to realize that the commemorative names assigned today may last for centuries.

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

Analysis of Mold Friction in a Continuous Casting Using Wavelet Transform

NASA Astrophysics Data System (ADS)

Ma, Yong; Fang, Bohan; Ding, Qiqi; Wang, Fangyin

2018-04-01

Mold friction (MDF) is an important parameter reflecting the lubrication condition between the initial shell and the mold during continuous casting. In this article, based on practical MDF from the slab continuous casting driven by a mechanical vibration device, the characteristics of friction were analyzed by continuous wavelet transform (CWT) and discrete wavelet transform (DWT) in different casting conditions, such as normal casting, level fluctuation, and alarming of the temperature measurement system. The results show that the CWT of friction accurately captures the subtle changes in friction force, such as the periodic characteristic of MDF during normal casting and the disordered feature of MDF during level fluctuation. Most important, the results capture the occurrence of abnormal casting and display the friction frequency characteristics at this abnormal time. In addition, in this article, there are some abnormal casting conditions, and the friction signal is stable until there is a sudden large change when abnormal casting, such as split breakout and submerged entry nozzle breakage, occurs. The DWT has a good ability to capture the friction characteristics for such abnormal situations. In particular, the potential abnormal features of MDF were presented in advance, which provides strong support for identifying abnormal casting and even preventing abnormal casting.

Scratching behaviour and its features: a questionnaire-based study in an Italian sample of domestic cats.

PubMed

Mengoli, Manuel; Mariti, Chiara; Cozzi, Alessandro; Cestarollo, Elisa; Lafont-Lecuelle, Céline; Pageat, Patrick; Gazzano, Angelo

2013-10-01

Scratching behaviour in cats is described as a normal expression of the feline ethogram, having different possible purposes related to visual and chemical communication. During behavioural consultations owners often mention scratching as an additional problem. This preliminary study aimed to understand the characteristics of this complex behaviour by examining the variables displayed by a sample of the Italian feline population using multiple correspondence analysis. One hundred and twenty-eight cats were screened by means of a questionnaire to identify features of their scratching behaviour. Our data showed the importance of both the presence/absence of a scratching post in the cat's living area and its relationship to marking. When a scratching post is present in a cat's living area, the cat appears to use it. Some aspects related to sex, neutering, age and environmental characteristics may modify the expression of scratching as a marking behaviour. Research has led to increased knowledge of this behaviour and may help veterinarians in describing to owners why it is important for cats to express scratching behaviour in their environment. Such information could help veterinarians and owners to recognise normal and problematic scratching behaviours.

CINCH (confocal incoherent correlation holography) super resolution fluorescence microscopy based upon FINCH (Fresnel incoherent correlation holography)

PubMed Central

Siegel, Nisan; Storrie, Brian; Bruce, Marc

2016-01-01

FINCH holographic fluorescence microscopy creates high resolution super-resolved images with enhanced depth of focus. The simple addition of a real-time Nipkow disk confocal image scanner in a conjugate plane of this incoherent holographic system is shown to reduce the depth of focus, and the combination of both techniques provides a simple way to enhance the axial resolution of FINCH in a combined method called “CINCH”. An important feature of the combined system allows for the simultaneous real-time image capture of widefield and holographic images or confocal and confocal holographic images for ready comparison of each method on the exact same field of view. Additional GPU based complex deconvolution processing of the images further enhances resolution. PMID:26839443

Consolidation & Factors Influencing Sintering Process in Polymer Powder Based Additive Manufacturing

NASA Astrophysics Data System (ADS)

Sagar, M. B.; Elangovan, K.

2017-08-01

Additive Manufacturing (AM) is two decade old technology; where parts are build layer manufacturing method directly from a CAD template. Over the years, AM techniques changes the future way of part fabrication with enhanced intricacy and custom-made features are aimed. Commercially polymers, metals, ceramic and metal-polymer composites are in practice where polymers enhanced the expectations in AM and are considered as a kind of next industrial revolution. Growing trend in polymer application motivated to study their feasibility and properties. Laser sintering, Heat sintering and Inhibition sintering are the most successful AM techniques for polymers but having least application. The presentation gives up selective sintering of powder polymers and listed commercially available polymer materials. Important significant factors for effective processing and analytical approaches to access them are discussed.

A Systematic Investigation of Computation Models for Predicting Adverse Drug Reactions (ADRs)

PubMed Central

Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

2014-01-01

Background Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. Principal Findings In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Conclusion Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms. PMID:25180585

Assessing prescription drug abuse using functional principal component analysis (FPCA) of wastewater data.

PubMed

Salvatore, Stefania; Røislien, Jo; Baz-Lomba, Jose A; Bramness, Jørgen G

2017-03-01

Wastewater-based epidemiology is an alternative method for estimating the collective drug use in a community. We applied functional data analysis, a statistical framework developed for analysing curve data, to investigate weekly temporal patterns in wastewater measurements of three prescription drugs with known abuse potential: methadone, oxazepam and methylphenidate, comparing them to positive and negative control drugs. Sewage samples were collected in February 2014 from a wastewater treatment plant in Oslo, Norway. The weekly pattern of each drug was extracted by fitting of generalized additive models, using trigonometric functions to model the cyclic behaviour. From the weekly component, the main temporal features were then extracted using functional principal component analysis. Results are presented through the functional principal components (FPCs) and corresponding FPC scores. Clinically, the most important weekly feature of the wastewater-based epidemiology data was the second FPC, representing the difference between average midweek level and a peak during the weekend, representing possible recreational use of a drug in the weekend. Estimated scores on this FPC indicated recreational use of methylphenidate, with a high weekend peak, but not for methadone and oxazepam. The functional principal component analysis uncovered clinically important temporal features of the weekly patterns of the use of prescription drugs detected from wastewater analysis. This may be used as a post-marketing surveillance method to monitor prescription drugs with abuse potential. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Geophysical surveys and archaeological insights at Fort Pierre Chouteau, a frontier trading post on the Middle Missouri

NASA Astrophysics Data System (ADS)

Patton, Margaret Maurine

Fort Pierre Chouteau in present day South Dakota was the most important fur trading post of the American Fur Company in the 1830s, serving as a regional hub for the fur trade. The Fort was sold to the U.S. Military in 1855 for use as a base in the Sioux Wars but was abandoned in 1856. Geophysical surveys and previous excavations indicate evidence of both occupations. Geophysics is an important tool for determining the extent of archaeological sites, yet the relationships between geophysical anomalies and excavation features may not be readily evident. Initial geophysical surveys (Kvamme 2007) were completed to determine the extent of the fur trading Fort, and additional surveys in August 2012 used magnetometry and electrical resistance to determine if evidence of military structures exists outside of the Fort. This study examines connections between excavation features and geophysical anomalies in order to better interpret anomalies inside the Fort palisade. The palisade builder's trench, adobe pavement, post holes, and unknown structures are characterized through the analysis of the excavations and anomalies. The location of one of the military structures outside of the palisade is also identified. As many sites have histories of excavations prior to any geophysical surveys, combining the two sets of information is important in order to more fully understand site layout and the archaeological causes of geophysical anomalies.

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.

Assessing future vent opening locations at the Somma-Vesuvio volcanic complex: 1. A new information geodatabase with uncertainty characterizations

NASA Astrophysics Data System (ADS)

Tadini, A.; Bisson, M.; Neri, A.; Cioni, R.; Bevilacqua, A.; Aspinall, W. P.

2017-06-01

This study presents new and revised data sets about the spatial distribution of past volcanic vents, eruptive fissures, and regional/local structures of the Somma-Vesuvio volcanic system (Italy). The innovative features of the study are the identification and quantification of important sources of uncertainty affecting interpretations of the data sets. In this regard, the spatial uncertainty of each feature is modeled by an uncertainty area, i.e., a geometric element typically represented by a polygon drawn around points or lines. The new data sets have been assembled as an updatable geodatabase that integrates and complements existing databases for Somma-Vesuvio. The data are organized into 4 data sets and stored as 11 feature classes (points and lines for feature locations and polygons for the associated uncertainty areas), totaling more than 1700 elements. More specifically, volcanic vent and eruptive fissure elements are subdivided into feature classes according to their associated eruptive styles: (i) Plinian and sub-Plinian eruptions (i.e., large- or medium-scale explosive activity); (ii) violent Strombolian and continuous ash emission eruptions (i.e., small-scale explosive activity); and (iii) effusive eruptions (including eruptions from both parasitic vents and eruptive fissures). Regional and local structures (i.e., deep faults) are represented as linear feature classes. To support interpretation of the eruption data, additional data sets are provided for Somma-Vesuvio geological units and caldera morphological features. In the companion paper, the data presented here, and the associated uncertainties, are used to develop a first vent opening probability map for the Somma-Vesuvio caldera, with specific attention focused on large or medium explosive events.

Pollenkitt wetting mechanism enables species-specific tunable pollen adhesion.

PubMed

Lin, Haisheng; Gomez, Ismael; Meredith, J Carson

2013-03-05

Plant pollens are microscopic particles exhibiting a remarkable breadth of complex solid surface features. In addition, many pollen grains are coated with a viscous liquid, "pollenkitt", thought to play important roles in pollen dispersion and adhesion. However, there exist no quantitative studies of the effects of solid surface features or pollenkitt on adhesion of pollen grains, and it remains unclear what role these features play in pollen adhesion and transport. We report AFM adhesion measurements of five pollen species with a series of test surfaces in which each pollen has a unique solid surface morphology and pollenkitt volume. The results indicate that the combination of surface morphology (size and shape of echinate or reticulate features) with the pollenkitt volume provides pollens with a remarkably tunable adhesion to surfaces. With pollenkitt removed, pollen grains had relatively low adhesion strengths that were independent of surface chemistry and scalable with the tip radius of the pollen's ornamentation features, according to the Hamaker model. With the pollenkitt intact, adhesion was up to 3-6 times higher than the dry grains and exhibited strong substrate dependence. The adhesion enhancing effect of pollenkitt was driven by the formation of pollenkitt capillary bridges and was surprisingly species-dependent, with echinate insect-pollinated species (dandelion and sunflower) showing significantly stronger adhesion and higher substrate dependence than wind-pollinated species (ragweed, poplar, and olive). The combination of high pollenkitt volume and large convex, spiny surface features in echinate entomophilous varieties appears to enhance the spreading area of the liquid pollenkitt relative to varieties of pollen with less pollenkitt volume and less pronounced surface features. Measurements of pollenkitt surface energy indicate that the adhesive strength of capillary bridges is primarily dependent on nonpolar van der Waals interactions, with some contribution from the Lewis basic component of surface energy.

Relating Sub-Surface Ice Features to Physiological Stress in a Climate Sensitive Mammal, the American Pika (Ochotona princeps)

PubMed Central

Wilkening, Jennifer L.; Ray, Chris; Varner, Johanna

2015-01-01

The American pika (Ochotona princeps) is considered a sentinel species for detecting ecological effects of climate change. Pikas are declining within a large portion of their range, and ongoing research suggests loss of sub-surface ice as a mechanism. However, no studies have demonstrated physiological responses of pikas to sub-surface ice features. Here we present the first analysis of physiological stress in pikas living in and adjacent to habitats underlain by ice. Fresh fecal samples were collected non-invasively from two adjacent sites in the Rocky Mountains (one with sub-surface ice and one without) and analyzed for glucocorticoid metabolites (GCM). We also measured sub-surface microclimates in each habitat. Results indicate lower GCM concentration in sites with sub-surface ice, suggesting that pikas are less stressed in favorable microclimates resulting from sub-surface ice features. GCM response was well predicted by habitat characteristics associated with sub-surface ice features, such as lower mean summer temperatures. These results suggest that pikas inhabiting areas without sub-surface ice features are experiencing higher levels of physiological stress and may be more susceptible to changing climates. Although post-deposition environmental effects can confound analyses based on fecal GCM, we found no evidence for such effects in this study. Sub-surface ice features are key to water cycling and storage and will likely represent an increasingly important component of water resources in a warming climate. Fecal samples collected from additional watersheds as part of current pika monitoring programs could be used to further characterize relationships between pika stress and sub-surface ice features. PMID:25803587

ON THE MISALIGNMENT BETWEEN CHROMOSPHERIC FEATURES AND THE MAGNETIC FIELD ON THE SUN

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

Martínez-Sykora, Juan; Pontieu, Bart De; Hansteen, Viggo

2016-11-01

Observations of the upper chromosphere show an enormous amount of intricate fine structure. Much of this comes in the form of linear features, which are most often assumed to be well aligned with the direction of the magnetic field in the low plasma β regime that is thought to dominate the upper chromosphere. We use advanced radiative magnetohydrodynamic simulations, including the effects of ion-neutral interactions (using the generalized Ohm’s law) in the partially ionized chromosphere, to show that the magnetic field is often not well aligned with chromospheric features. This occurs where the ambipolar diffusion is large, i.e., ions andmore » neutral populations decouple as the ion-neutral collision frequency drops, allowing the field to slip through the neutral population; where currents perpendicular to the field are strong; and where thermodynamic timescales are longer than or similar to those of ambipolar diffusion. We find this often happens in dynamic spicule or fibril-like features at the top of the chromosphere. This has important consequences for field extrapolation methods, which increasingly use such upper chromospheric features to help constrain the chromospheric magnetic field: our results invalidate the underlying assumption that these features are aligned with the field. In addition, our results cast doubt on results from 1D hydrodynamic models, which assume that plasma remains on the same field lines. Finally, our simulations show that ambipolar diffusion significantly alters the amount of free energy available in the coronal part of our simulated volume, which is likely to have consequences for studies of flare initiation.« less

Novel Features for Brain-Computer Interfaces

PubMed Central

Woon, W. L.; Cichocki, A.

2007-01-01

While conventional approaches of BCI feature extraction are based on the power spectrum, we have tried using nonlinear features for classifying BCI data. In this paper, we report our test results and findings, which indicate that the proposed method is a potentially useful addition to current feature extraction techniques. PMID:18364991

Consumers' Preferences for Electronic Nicotine Delivery System Product Features: A Structured Content Analysis.

PubMed

Kistler, Christine E; Crutchfield, Trisha M; Sutfin, Erin L; Ranney, Leah M; Berman, Micah L; Zarkin, Gary A; Goldstein, Adam O

2017-06-07

To inform potential governmental regulations, we aimed to develop a list of electronic nicotine delivery system (ENDS) product features important to U.S. consumers by age and gender. We employed qualitative data methods. Participants were eligible if they had used an ENDS at least once. Groups were selected by age and gender (young adult group aged 18-25, n = 11; middle-age group aged 26-64, n = 9; and women's group aged 26-64, n = 9). We conducted five individual older adult interviews (aged 68-80). Participants discussed important ENDS features. We conducted a structured content analysis of the group and interview responses. Of 34 participants, 68% were white and 56% were female. Participants mentioned 12 important ENDS features, including: (1) user experience; (2) social acceptability; (3) cost; (4) health risks/benefits; (5) ease of use; (6) flavors; (7) smoking cessation aid; (8) nicotine content; (9) modifiability; (10) ENDS regulation; (11) bridge between tobacco cigarettes; (12) collectability. The most frequently mentioned ENDS feature was modifiability for young adults, user experience for middle-age and older adults, and flavor for the women's group. This study identified multiple features important to ENDS consumers. Groups differed in how they viewed various features by age and gender. These results can inform ongoing regulatory efforts.

Consumers’ Preferences for Electronic Nicotine Delivery System Product Features: A Structured Content Analysis

PubMed Central

Kistler, Christine E.; Crutchfield, Trisha M.; Sutfin, Erin L.; Ranney, Leah M.; Berman, Micah L.; Zarkin, Gary A.; Goldstein, Adam O.

2017-01-01

To inform potential governmental regulations, we aimed to develop a list of electronic nicotine delivery system (ENDS) product features important to U.S. consumers by age and gender. We employed qualitative data methods. Participants were eligible if they had used an ENDS at least once. Groups were selected by age and gender (young adult group aged 18–25, n = 11; middle-age group aged 26–64, n = 9; and women’s group aged 26–64, n = 9). We conducted five individual older adult interviews (aged 68–80). Participants discussed important ENDS features. We conducted a structured content analysis of the group and interview responses. Of 34 participants, 68% were white and 56% were female. Participants mentioned 12 important ENDS features, including: (1) user experience; (2) social acceptability; (3) cost; (4) health risks/benefits; (5) ease of use; (6) flavors; (7) smoking cessation aid; (8) nicotine content; (9) modifiability; (10) ENDS regulation; (11) bridge between tobacco cigarettes; (12) collectability. The most frequently mentioned ENDS feature was modifiability for young adults, user experience for middle-age and older adults, and flavor for the women’s group. This study identified multiple features important to ENDS consumers. Groups differed in how they viewed various features by age and gender. These results can inform ongoing regulatory efforts. PMID:28590444

Speech recognition features for EEG signal description in detection of neonatal seizures.

PubMed

Temko, A; Boylan, G; Marnane, W; Lightbody, G

2010-01-01

In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.

The development of a decision support system with an interactive clinical user interface for estimating treatment parameters in radiation therapy in order to reduce radiation dose in head and neck patients

NASA Astrophysics Data System (ADS)

Verma, Sneha; Liu, Joseph; Deshpande, Ruchi; DeMarco, John; Liu, Brent J.

2017-03-01

The primary goal in radiation therapy is to target the tumor with the maximum possible radiation dose while limiting the radiation exposure of the surrounding healthy tissues. However, in order to achieve an optimized treatment plan, many constraints, such as gender, age, tumor type, location, etc. need to be considered. The location of the malignant tumor with respect to the vital organs is another possible important factor for treatment planning process which can be quantified as a feature making it easier to analyze its effects. Incorporation of such features into the patient's medical history could provide additional knowledge that could lead to better treatment outcomes. To show the value of features such as relative locations of tumors and surrounding organs, the data is first processed in order to calculate the features and formulate a feature matrix. Then these feature are matched with retrospective cases with similar features to provide the clinician with insight on delivered dose in similar cases from past. This process provides a range of doses that can be delivered to the patient while limiting the radiation exposure of surrounding organs based on similar retrospective cases. As the number of patients increase, there will be an increase in computations needed for feature extraction as well as an increase in the workload for the physician to find the perfect dose amount. In order to show how such algorithms can be integrated we designed and developed a system with a streamlined workflow and interface as prototype for the clinician to test and explore. Integration of the tumor location feature with the clinician's experience and training could play a vital role in designing new treatment algorithms and better outcomes. Last year, we presented how multi-institutional data into a decision support system is incorporated. This year the presentation is focused on the interface and demonstration of the working prototype of informatics system.

Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

NASA Astrophysics Data System (ADS)

Kokaly, Raymond F.; Skidmore, Andrew K.

2015-12-01

Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic Csbnd H bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin).

Dynamic compressive properties of bovine knee layered tissue

NASA Astrophysics Data System (ADS)

Nishida, Masahiro; Hino, Yuki; Todo, Mitsugu

2015-09-01

In Japan, the most common articular disease is knee osteoarthritis. Among many treatment methodologies, tissue engineering and regenerative medicine have recently received a lot of attention. In this field, cells and scaffolds are important, both ex vivo and in vivo. From the viewpoint of effective treatment, in addition to histological features, the compatibility of mechanical properties is also important. In this study, the dynamic and static compressive properties of bovine articular cartilage-cancellous bone layered tissue were measured using a universal testing machine and a split Hopkinson pressure bar method. The compressive behaviors of bovine articular cartilage-cancellous bone layered tissue were examined. The effects of strain rate on the maximum stress and the slope of stress-strain curves of the bovine articular cartilage-cancellous bone layered tissue were discussed.

Issues or Identity? Cognitive Foundations of Voter Choice.

PubMed

Jenke, Libby; Huettel, Scott A

2016-11-01

Voter choice is one of the most important problems in political science. The most common models assume that voting is a rational choice based on policy positions (e.g., key issues) and nonpolicy information (e.g., social identity, personality). Though such models explain macroscopic features of elections, they also reveal important anomalies that have been resistant to explanation. We argue for a new approach that builds upon recent research in cognitive science and neuroscience; specifically, we contend that policy positions and social identities do not combine in merely an additive manner, but compete to determine voter preferences. This model not only explains several key anomalies in voter choice, but also suggests new directions for research in both political science and cognitive science. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Physics and Mathematics of MRI

NASA Astrophysics Data System (ADS)

Ansorge, Richard; Graves, Martin

2016-10-01

Magnetic Resonance Imaging is a very important clinical imaging tool. It combines different fields of physics and engineering in a uniquely complex way. MRI is also surprisingly versatile, `pulse sequences' can be designed to yield many different types of contrast. This versatility is unique to MRI. This short book gives both an in depth account of the methods used for the operation and construction of modern MRI systems and also the principles of sequence design and many examples of applications. An important additional feature of this book is the detailed discussion of the mathematical principles used in building optimal MRI systems and for sequence design. The mathematical discussion is very suitable for undergraduates attending medical physics courses. It is also more complete than usually found in alternative books for physical scientists or more clinically orientated works.

Degeneration and domestication of a selfish gene in yeast: molecular evolution versus site-directed mutagenesis.

PubMed

Koufopanou, Vassiliki; Burt, Austin

2005-07-01

VDE is a homing endonuclease gene in yeasts with an unusual evolutionary history including horizontal transmission, degeneration, and domestication into the mating-type switching locus HO. We investigate here the effects of these features on its molecular evolution. In addition, we correlate rates of evolution with results from site-directed mutagenesis studies. Functional elements have lower rates of evolution than degenerate ones and higher conservation at functionally important sites. However, functionally important and unimportant sites are equally likely to have been involved in the evolution of new function during the domestication of VDE into HO. The domestication event also indicates that VDE has been lost in some species and that VDE has been present in yeasts for more than 50 Myr.

THE PATH AND PROMISE OF FATHERHOOD FOR GANG MEMBERS

PubMed Central

Moloney, Molly; MacKenzie, Kathleen; Hunt, Geoffrey; Joe-Laidler, Karen

2009-01-01

While an increase in research on criminal desistance has occurred in recent years, little research has been applied to the gang field. Using qualitative interview data, this article examines fatherhood as a potential turning point in the lives of 91 gang members in the San Francisco Bay Area. Fatherhood initiated important subjective and affective transformations that led to changes in outlook, priorities and future orientation. However, these subjective changes were not sufficient unless accompanied by two additional features: first, changes in the amount of time spent on the streets and, second, an ability to support oneself or one’s family with legal income. Though fatherhood is no panacea, becoming a father did act as an important turning point toward desistance and motivator for change for some. PMID:20046970

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.

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

Weighted Feature Significance: A Simple, Interpretable Model of Compound Toxicity Based on the Statistical Enrichment of Structural Features

PubMed Central

Huang, Ruili; Southall, Noel; Xia, Menghang; Cho, Ming-Hsuang; Jadhav, Ajit; Nguyen, Dac-Trung; Inglese, James; Tice, Raymond R.; Austin, Christopher P.

2009-01-01

In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation. PMID:19805409

Can theories of animal discrimination explain perceptual learning in humans?

PubMed

Mitchell, Chris; Hall, Geoffrey

2014-01-01

We present a review of recent studies of perceptual learning conducted with nonhuman animals. The focus of this research has been to elucidate the mechanisms by which mere exposure to a pair of similar stimuli can increase the ease with which those stimuli are discriminated. These studies establish an important role for 2 mechanisms, one involving inhibitory associations between the unique features of the stimuli, the other involving a long-term habituation process that enhances the relative salience of these features. We then examine recent work investigating equivalent perceptual learning procedures with human participants. Our aim is to determine the extent to which the phenomena exhibited by people are susceptible to explanation in terms of the mechanisms revealed by the animal studies. Although we find no evidence that associative inhibition contributes to the perceptual learning effect in humans, initial detection of unique features (those that allow discrimination between 2 similar stimuli) appears to depend on an habituation process. Once the unique features have been detected, a tendency to attend to those features and to learn about their properties enhances subsequent discrimination. We conclude that the effects obtained with humans engage mechanisms additional to those seen in animals but argue that, for the most part, these have their basis in learning processes that are common to animals and people. In a final section, we discuss some implications of this analysis of perceptual learning for other aspects of experimental psychology and consider some potential applications. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Feature extraction via KPCA for classification of gait patterns.

PubMed

Wu, Jianning; Wang, Jue; Liu, Li

2007-06-01

Automated recognition of gait pattern change is important in medical diagnostics as well as in the early identification of at-risk gait in the elderly. We evaluated the use of Kernel-based Principal Component Analysis (KPCA) to extract more gait features (i.e., to obtain more significant amounts of information about human movement) and thus to improve the classification of gait patterns. 3D gait data of 24 young and 24 elderly participants were acquired using an OPTOTRAK 3020 motion analysis system during normal walking, and a total of 36 gait spatio-temporal and kinematic variables were extracted from the recorded data. KPCA was used first for nonlinear feature extraction to then evaluate its effect on a subsequent classification in combination with learning algorithms such as support vector machines (SVMs). Cross-validation test results indicated that the proposed technique could allow spreading the information about the gait's kinematic structure into more nonlinear principal components, thus providing additional discriminatory information for the improvement of gait classification performance. The feature extraction ability of KPCA was affected slightly with different kernel functions as polynomial and radial basis function. The combination of KPCA and SVM could identify young-elderly gait patterns with 91% accuracy, resulting in a markedly improved performance compared to the combination of PCA and SVM. These results suggest that nonlinear feature extraction by KPCA improves the classification of young-elderly gait patterns, and holds considerable potential for future applications in direct dimensionality reduction and interpretation of multiple gait signals.

eCOMPAGT – efficient Combination and Management of Phenotypes and Genotypes for Genetic Epidemiology

PubMed Central

Schönherr, Sebastian; Weißensteiner, Hansi; Coassin, Stefan; Specht, Günther; Kronenberg, Florian; Brandstätter, Anita

2009-01-01

Background High-throughput genotyping and phenotyping projects of large epidemiological study populations require sophisticated laboratory information management systems. Most epidemiological studies include subject-related personal information, which needs to be handled with care by following data privacy protection guidelines. In addition, genotyping core facilities handling cooperative projects require a straightforward solution to monitor the status and financial resources of the different projects. Description We developed a database system for an efficient combination and management of phenotypes and genotypes (eCOMPAGT) deriving from genetic epidemiological studies. eCOMPAGT securely stores and manages genotype and phenotype data and enables different user modes with different rights. Special attention was drawn on the import of data deriving from TaqMan and SNPlex genotyping assays. However, the database solution is adjustable to other genotyping systems by programming additional interfaces. Further important features are the scalability of the database and an export interface to statistical software. Conclusion eCOMPAGT can store, administer and connect phenotype data with all kinds of genotype data and is available as a downloadable version at . PMID:19432954

Exploring Martian Impact Craters: Why They are Important for the Search for Life

NASA Technical Reports Server (NTRS)

Schwenzer, S. P.; Abramov, O.; Allen, C. C.; Clifford, S.; Filiberto, J.; Kring, D. A.; Lasue, J.; McGovern, P. J.; Newsom, H. E.; Treiman, A. H.;

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.

Human immunophenotyping via low-variance, low-bias, interpretive regression modeling of small, wide data sets: Application to aging and immune response to influenza vaccination.

PubMed

Holmes, Tyson H; He, Xiao-Song

2016-10-01

Small, wide data sets are commonplace in human immunophenotyping research. As defined here, a small, wide data set is constructed by sampling a small to modest quantity n,1

Human Immunophenotyping via Low-Variance, Low-Bias, Interpretive Regression Modeling of Small, Wide Data Sets: Application to Aging and Immune Response to Influenza Vaccination

PubMed Central

Holmes, Tyson H.; He, Xiao-Song

2016-01-01

Small, wide data sets are commonplace in human immunophenotyping research. As defined here, a small, wide data set is constructed by sampling a small to modest quantity n, 1 < n < 50, of human participants for the purpose of estimating many parameters p, such that n < p < 1,000. We offer a set of prescriptions that are designed to facilitate low-variance (i.e. stable), low-bias, interpretive regression modeling of small, wide data sets. These prescriptions are distinctive in their especially heavy emphasis on minimizing use of out-of-sample information for conducting statistical inference. That allows the working immunologist to proceed without being encumbered by imposed and often untestable statistical assumptions. Problems of unmeasured confounders, confidence-interval coverage, feature selection, and shrinkage/denoising are defined clearly and treated in detail. We propose an extension of an existing nonparametric technique for improved small-sample confidence-interval tail coverage from the univariate case (single immune feature) to the multivariate (many, possibly correlated immune features). An important role for derived features in the immunological interpretation of regression analyses is stressed. Areas of further research are discussed. Presented principles and methods are illustrated through application to a small, wide data set of adults spanning a wide range in ages and multiple immunophenotypes that were assayed before and after immunization with inactivated influenza vaccine (IIV). Our regression modeling prescriptions identify some potentially important topics for future immunological research. 1) Immunologists may wish to distinguish age-related differences in immune features from changes in immune features caused by aging. 2) A form of the bootstrap that employs linear extrapolation may prove to be an invaluable analytic tool because it allows the working immunologist to obtain accurate estimates of the stability of immune parameter estimates with a bare minimum of imposed assumptions. 3) Liberal inclusion of immune features in phenotyping panels can facilitate accurate separation of biological signal of interest from noise. In addition, through a combination of denoising and potentially improved confidence interval coverage, we identify some candidate immune correlates (frequency of cell subset and concentration of cytokine) with B cell response as measured by quantity of IIV-specific IgA antibody-secreting cells and quantity of IIV-specific IgG antibody-secreting cells. PMID:27196789

Quantifying site-specific physical heterogeneity within an estuarine seascape

USGS Publications Warehouse

Kennedy, Cristina G.; Mather, Martha E.; Smith, Joseph M.

2017-01-01

Quantifying physical heterogeneity is essential for meaningful ecological research and effective resource management. Spatial patterns of multiple, co-occurring physical features are rarely quantified across a seascape because of methodological challenges. Here, we identified approaches that measured total site-specific heterogeneity, an often overlooked aspect of estuarine ecosystems. Specifically, we examined 23 metrics that quantified four types of common physical features: (1) river and creek confluences, (2) bathymetric variation including underwater drop-offs, (3) land features such as islands/sandbars, and (4) major underwater channel networks. Our research at 40 sites throughout Plum Island Estuary (PIE) provided solutions to two problems. The first problem was that individual metrics that measured heterogeneity of a single physical feature showed different regional patterns. We solved this first problem by combining multiple metrics for a single feature using a within-physical feature cluster analysis. With this approach, we identified sites with four different types of confluences and three different types of underwater drop-offs. The second problem was that when multiple physical features co-occurred, new patterns of total site-specific heterogeneity were created across the seascape. This pattern of total heterogeneity has potential ecological relevance to structure-oriented predators. To address this second problem, we identified sites with similar types of total physical heterogeneity using an across-physical feature cluster analysis. Then, we calculated an additive heterogeneity index, which integrated all physical features at a site. Finally, we tested if site-specific additive heterogeneity index values differed for across-physical feature clusters. In PIE, the sites with the highest additive heterogeneity index values were clustered together and corresponded to sites where a fish predator, adult striped bass (Morone saxatilis), aggregated in a related acoustic tracking study. In summary, we have shown general approaches to quantifying site-specific heterogeneity.

The new Mobile Command Center at KSC is important addition to emergency preparedness

NASA Technical Reports Server (NTRS)

2000-01-01

Charles Street, Roger Scheidt and Robert ZiBerna, the Emergency Preparedness team at KSC, sit in the conference room inside the Mobile Command Center, a specially equipped vehicle. Nicknamed '''The Brute,''' it also features computer work stations, mobile telephones and a fax machine. It also can generate power with its onboard generator. Besides being ready to respond in case of emergencies during launches, the vehicle must be ready to help address fires, security threats, chemical spills, terrorist attaches, weather damage or other critical situations that might face KSC or Cape Canaveral Air Force Station.

The new Mobile Command Center at KSC is important addition to emergency preparedness

NASA Technical Reports Server (NTRS)

2000-01-01

Robert ZiBerna, Roger Scheidt and Charles Street, the Emergency Preparedness team at KSC, practice for an emergency scenario inside the Mobile Command Center, a specially equipped vehicle. It features a conference room, computer work stations, mobile telephones and a fax machine. It also can generate power with its onboard generator. Besides being ready to respond in case of emergencies during launches, the vehicle must be ready to help address fires, security threats, chemical spills, terrorist attaches, weather damage or other critical situations that might face KSC or Cape Canaveral Air Force Station.

Bone tumor mimickers: A pictorial essay

PubMed Central

Mhuircheartaigh, Jennifer Ni; Lin, Yu-Ching; Wu, Jim S

2014-01-01

Focal lesions in bone are very common and many of these lesions are not bone tumors. These bone tumor mimickers can include numerous normal anatomic variants and non-neoplastic processes. Many of these tumor mimickers can be left alone, while others can be due to a significant disease process. It is important for the radiologist and clinician to be aware of these bone tumor mimickers and understand the characteristic features which allow discrimination between them and true neoplasms in order to avoid unnecessary additional workup. Knowing which lesions to leave alone or which ones require workup can prevent misdiagnosis and reduce patient anxiety. PMID:25114385

Target attribute-based false alarm rejection in small infrared target detection

NASA Astrophysics Data System (ADS)

Kim, Sungho

2012-11-01

Infrared search and track is an important research area in military applications. Although there are a lot of works on small infrared target detection methods, we cannot apply them in real field due to high false alarm rate caused by clutters. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. Eight kinds of target features are extracted and analyzed statistically. Learning-based classifiers such as SVM and Adaboost are developed and compared with conventional classifiers for real infrared images. In addition, the generalization capability is also inspected for various infrared clutters.

Incremental benefit of three-dimensional transesophageal echocardiography in the assessment of a primary pericardial hemangioma.

PubMed

Arisha, Mohammed J; Hsiung, Ming C; Nanda, Navin C; ElKaryoni, Ahmed; Mohamed, Ahmed H; Wei, Jeng

2017-08-01

Hemangiomas are rarely found in the heart and pericardial involvement is even more rare. We report a case of primary pericardial hemangioma, in which three-dimensional transesophageal echocardiography (3DTEE) provided incremental benefit over standard two-dimensional images. Our case also highlights the importance of systematic cropping of the 3D datasets in making a diagnosis of pericardial hemangioma with a greater degree of certainty. In addition, we also provide a literature review of the features of cardiac/pericardial hemangiomas in a tabular form. © 2017, Wiley Periodicals, Inc.

Cardiometabolic Risks in Polycystic Ovary Syndrome: Non-Traditional Risk Factors and the Impact of Obesity.

PubMed

Chiu, Wei-Ling; Boyle, Jacqueline; Vincent, Amanda; Teede, Helena; Moran, Lisa J

2017-01-01

Polycystic ovary syndrome (PCOS) is a common and complex endocrinopathy with reproductive, metabolic, and psychological features and significantly increased cardiometabolic risks. PCOS is underpinned by inherent insulin resistance and hyperandrogenism. Obesity, more common in PCOS, plays an important role in the pathophysiology, exacerbating hyperinsulinaemia and hyperandrogenism, leading to recommended first-line lifestyle intervention. Significant traditional and non-traditional risk factors are implicated in PCOS in addition to obesity-exacerbated cardiometabolic risks and are explored in this review to promote the understanding of this common metabolic and reproductive condition. © 2016 S. Karger AG, Basel.

PLUTONIUM ELECTROREFINING CELLS

DOEpatents

Mullins, L.J. Jr.; Leary, J.A.; Bjorklund, C.W.; Maraman, W.J.

1963-07-16

Electrorefining cells for obtaining 99.98% plutonium are described. The cells consist of an impure liquid plutonium anode, a molten PuCl/sub 3/-- alkali or alkaline earth metal chloanode, a molten PuCl/sub 3/-alkali or alkaline earth metal chloride electrolyte, and a nonreactive cathode, all being contained in nonreactive ceramic containers which separate anode from cathode by a short distance and define a gap for the collection of the purified liquid plutonium deposited on the cathode. Important features of these cells are the addition of stirrer blades on the anode lead and a large cathode surface to insure a low current density. (AEC)

A room-temperature protocol to access isoquinolines through Ag(I) catalysed annulation of o-(1-alkynyl)arylaldehydes and ketones with NH4OAc: elaboration to berberine and palmatine.

PubMed

Reddy, Virsinha; Jadhav, Abhijeet S; Vijaya Anand, Ramasamy

2015-03-28

An efficient and mild protocol for the direct construction of aryl- and alkyl-substituted isoquinolines has been realized through silver nitrate catalyzed aromatic annulation of o-(1-alkynyl)arylaldehydes and ketones with ammonium acetate. The salient feature of this methodology is that this annulation could be effected at room temperature leading to a wide range of isoquinoline derivatives in good to excellent yields. Additionally, this approach has been employed to the synthesis of biologically important isoquinoline alkaloids such as berberine and palmatine.

[Brachycephaly in dog and cat: a "human induced" obstruction of the upper airways].

PubMed

Oechtering, G U; Schlüter, C; Lippert, J P

2010-07-01

Selective breeding for exaggerated features caused in many brachycephalic dog and cat breeds virtually a loss of the nose, with serious anatomical and functional consequences. In addition to respiratory and olfactory tasks, in dogs the nose is of vital importance for thermoregulation. As obligatory nose breathers, dogs suffer far more than humans when their nasal ventilation is restricted. An open discussion in the broad public has to motivate authorities and kennel clubs to recognize extreme brachycephalic breeding as seriously affecting animal health and welfare. Copyright Georg Thieme Verlag KG Stuttgart . New York.

Current viewpoints on oxide adherence mechanisms

NASA Technical Reports Server (NTRS)

Smialek, J. L.; Browning, R.

1985-01-01

Additional hot stage Auger experiments have provided surface segregation data for NiCrAl + or - Y or Zr alloys in agreement with other investigations. This data, combined with experimental and theoretical evidence of the Al2O3-metal bond strength, is presented in support of a chemical mechanism of Al2O3 scale adhesion. Both the detrimental effects of sulfur segregation and the beneficial effects of dopant segregation may be important. Chemical features of the dopants are compared in light of these proposed mechanisms, namely delta H sub f (sulfide), delta H sub f (oxide), electron orbital configuration, and insolubility in Ni.

A field study of air flow and turbulent features of advection fog

NASA Technical Reports Server (NTRS)

Connell, J. D.

1979-01-01

The setup and initial operation of a set of specialized meteorological data collection hardware are described. To study the life cycle of advection fogs at a lake test site, turbulence levels in the fog are identified, and correlated with the temperature gradients and mean wind profiles. A meteorological tower was instrumented to allow multiple-level measurements of wind and temperature on a continuous basis. Additional instrumentation was: (1)hydrothermograph, (2)microbarograph, (3)transmissometers, and (4)a boundary layer profiler. Two types of fogs were identified, and important differences in the turbulence scales were noted.

A real-time, dual processor simulation of the rotor system research aircraft

NASA Technical Reports Server (NTRS)

Mackie, D. B.; Alderete, T. S.

1977-01-01

A real-time, man-in-the loop, simulation of the rotor system research aircraft (RSRA) was conducted. The unique feature of this simulation was that two digital computers were used in parallel to solve the equations of the RSRA mathematical model. The design, development, and implementation of the simulation are documented. Program validation was discussed, and examples of data recordings are given. This simulation provided an important research tool for the RSRA project in terms of safe and cost-effective design analysis. In addition, valuable knowledge concerning parallel processing and a powerful simulation hardware and software system was gained.

Non-encapsidation Activities of the Capsid Proteins of Positive-strand RNA Viruses

PubMed Central

Ni, Peng; Kao, C. Cheng

2013-01-01

Viral capsid proteins (CPs) are characterized by their role in forming protective shells around viral genomes. However, CPs have additional and important roles in the virus infection cycles and in the cellular response to infection. These activities involve CP binding to RNAs in both sequence-specific and nonspecific manners as well as association with other proteins. This review focuses on CPs of both plant and animal-infecting viruses with positive-strand RNA genomes. We summarize the structural features of CPs and describe their modulatory roles in viral translation, RNA-dependent RNA synthesis, and host defense responses. PMID:24074574

Mondini dysplasia with recurrent bacterial meningitis caused by three different pathogens.

PubMed

Shikano, Hiroaki; Ohnishi, Hidenori; Fukutomi, Hisashi; Ito, Kimiko; Morimoto, Masahiro; Teramoto, Takahide; Aoki, Mitsuhiro; Nishihori, Takezumi; Akeda, Yukihiro; Oishi, Kazunori; Fukao, Toshiyuki

2015-12-01

Mondini dysplasia is rare, but has an important association with recurrent bacterial meningitis. We herein describe the case of a 3-year-old girl with unilateral sensorineural hearing loss who presented with three independent episodes of bacterial meningitis within 8 months. Temporal bone computed tomography indicated the characteristic features of Mondini dysplasia in the right inner ear. This was treated by surgical closure of the inner ear defect via oval window and additional vaccination was administered. Appropriate vaccination might prevent the recurrent bacterial meningitis associated with Mondini dysplasia. © 2015 Japan Pediatric Society.

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.

Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

NASA Astrophysics Data System (ADS)

Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

2014-03-01

Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 High Power Fields (HPF, x400 magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Apart from this being the second best performance ever recorded for this MITOS dataset, our approach is faster and requires fewer computing resources compared to extant methods, making this feasible for clinical use.

Simulation of Attacks for Security in Wireless Sensor Network.

PubMed

Diaz, Alvaro; Sanchez, Pablo

2016-11-18

The increasing complexity and low-power constraints of current Wireless Sensor Networks (WSN) require efficient methodologies for network simulation and embedded software performance analysis of nodes. In addition, security is also a very important feature that has to be addressed in most WSNs, since they may work with sensitive data and operate in hostile unattended environments. In this paper, a methodology for security analysis of Wireless Sensor Networks is presented. The methodology allows designing attack-aware embedded software/firmware or attack countermeasures to provide security in WSNs. The proposed methodology includes attacker modeling and attack simulation with performance analysis (node's software execution time and power consumption estimation). After an analysis of different WSN attack types, an attacker model is proposed. This model defines three different types of attackers that can emulate most WSN attacks. In addition, this paper presents a virtual platform that is able to model the node hardware, embedded software and basic wireless channel features. This virtual simulation analyzes the embedded software behavior and node power consumption while it takes into account the network deployment and topology. Additionally, this simulator integrates the previously mentioned attacker model. Thus, the impact of attacks on power consumption and software behavior/execution-time can be analyzed. This provides developers with essential information about the effects that one or multiple attacks could have on the network, helping them to develop more secure WSN systems. This WSN attack simulator is an essential element of the attack-aware embedded software development methodology that is also introduced in this work.

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.

The One Micron Fe II Lines in Active Galaxies and Emission Line Stars

NASA Astrophysics Data System (ADS)

Rudy, R. J.; Mazuk, S.; Puetter, R. C.; Hamann, F. W.

1999-05-01

The infrared multiplet of Fe II lines at 0.9997, 1.0501, 1.0863, and 1.1126 microns are particularly strong relative to other red and infrared Fe II features. They reach their greatest strength, relative to the hydrogen lines, in the Seyfert 1 galaxy I Zw 1, and are a common, although not ubiquitous feature, in the broad line regions of active galaxies. In addition, they are seen in a diverse assortment of Galactic sources including young stars, Herbig Ae and Be stars, luminous blue variables, proto-planetary nebulae, and symbiotic novae. They are probably excited by Lyman alpha florescence but the exact path of the cascade to their upper levels is uncertain. They arise in dense, sheltered regions of low ionization and are frequently observed together with the infrared Ca II triplet and the Lyman beta excited O I lines 8446 and 11287. The strengths of the four Fe II features, relative to each other, are nearly constant from object to object suggesting a statistical population of their common upper multiplet. Their intensities, in comparison to the Paschen lines, indicate that they can be important coolants for regions with high optical depths in the hydrogen lines. In addition to I Zw 1 and other active galaxies, we present spectra for the Galactic sources MWC 17, MWC 84, MWC 340, MWC 922, PU Vul, and M 1-92. We review the status of the Fe II observations and discuss the excitation process and possible implications. This work was supported by the IR&D program of the Aerospace Corporation. RCP and FWH acknowledge support from NASA.

Modeling the dynamic crush of impact mitigating materials

NASA Astrophysics Data System (ADS)

Logan, R. W.; McMichael, L. D.

1995-05-01

Crushable materials are commonly utilized in the design of structural components to absorb energy and mitigate shock during the dynamic impact of a complex structure, such as an automobile chassis or drum-type shipping container. The development and application of several finite-element material models which have been developed at various times at LLNL for DYNA3D are discussed. Between the models, they are able to account for several of the predominant mechanisms which typically influence the dynamic mechanical behavior of crushable materials. One issue we addressed was that no single existing model would account for the entire gambit of constitutive features which are important for crushable materials. Thus, we describe the implementation and use of an additional material model which attempts to provide a more comprehensive model of the mechanics of crushable material behavior. This model combines features of the pre-existing DYNA models and incorporates some new features as well in an invariant large-strain formulation. In addition to examining the behavior of a unit cell in uniaxial compression, two cases were chosen to evaluate the capabilities and accuracy of the various material models in DYNA. In the first case, a model for foam filled box beams was developed and compared to test data from a four-point bend test. The model was subsequently used to study its effectiveness in energy absorption in an aluminum extrusion, spaceframe, vehicle chassis. The second case examined the response of the AT-400A shipping container and the performance of the overpack material during accident environments selected from 10CFR71 and IAEA regulations.

Structural features facilitating tumor cell targeting and internalization by bleomycin and its disaccharide.

PubMed

Yu, Zhiqiang; Paul, Rakesh; Bhattacharya, Chandrabali; Bozeman, Trevor C; Rishel, Michael J; Hecht, Sidney M

2015-05-19

We have shown previously that the bleomycin (BLM) carbohydrate moiety can recapitulate the tumor cell targeting effects of the entire BLM molecule, that BLM itself is modular in nature consisting of a DNA-cleaving aglycone which is delivered selectively to the interior of tumor cells by its carbohydrate moiety, and that there are disaccharides structurally related to the BLM disaccharide which are more efficient than the natural disaccharide at tumor cell targeting/uptake. Because BLM sugars can deliver molecular cargoes selectively to tumor cells, and thus potentially form the basis for a novel antitumor strategy, it seemed important to consider additional structural features capable of affecting the efficiency of tumor cell recognition and delivery. These included the effects of sugar polyvalency and net charge (at physiological pH) on tumor cell recognition, internalization, and trafficking. Since these parameters have been shown to affect cell surface recognition, internalization, and distribution in other contexts, this study has sought to define the effects of these structural features on tumor cell recognition by bleomycin and its disaccharide. We demonstrate that both can have a significant effect on tumor cell binding/internalization, and present data which suggests that the metal ions normally bound by bleomycin following clinical administration may significantly contribute to the efficiency of tumor cell uptake, in addition to their characterized function in DNA cleavage. A BLM disaccharide-Cy5** conjugate incorporating the positively charged dipeptide d-Lys-d-Lys was found to associate with both the mitochondria and the nuclear envelope of DU145 cells, suggesting possible cellular targets for BLM disaccharide-cytotoxin conjugates.

Structural Features Facilitating Tumor Cell Targeting and Internalization by Bleomycin and Its Disaccharide

PubMed Central

2016-01-01

We have shown previously that the bleomycin (BLM) carbohydrate moiety can recapitulate the tumor cell targeting effects of the entire BLM molecule, that BLM itself is modular in nature consisting of a DNA-cleaving aglycone which is delivered selectively to the interior of tumor cells by its carbohydrate moiety, and that there are disaccharides structurally related to the BLM disaccharide which are more efficient than the natural disaccharide at tumor cell targeting/uptake. Because BLM sugars can deliver molecular cargoes selectively to tumor cells, and thus potentially form the basis for a novel antitumor strategy, it seemed important to consider additional structural features capable of affecting the efficiency of tumor cell recognition and delivery. These included the effects of sugar polyvalency and net charge (at physiological pH) on tumor cell recognition, internalization, and trafficking. Since these parameters have been shown to affect cell surface recognition, internalization, and distribution in other contexts, this study has sought to define the effects of these structural features on tumor cell recognition by bleomycin and its disaccharide. We demonstrate that both can have a significant effect on tumor cell binding/internalization, and present data which suggests that the metal ions normally bound by bleomycin following clinical administration may significantly contribute to the efficiency of tumor cell uptake, in addition to their characterized function in DNA cleavage. A BLM disaccharide-Cy5** conjugate incorporating the positively charged dipeptide d-Lys-d-Lys was found to associate with both the mitochondria and the nuclear envelope of DU145 cells, suggesting possible cellular targets for BLM disaccharide–cytotoxin conjugates. PMID:25905565

Discontinuities concentrate mobile predators: Quantifying organism-environment interactions at a seascape scale

USGS Publications Warehouse

Kennedy, Christina G.; Mather, Martha E.; Smith, Joseph M.; Finn, John T.; Deegan, Linda A.

2016-01-01

Understanding environmental drivers of spatial patterns is an enduring ecological problem that is critical for effective biological conservation. Discontinuities (ecologically meaningful habitat breaks), both naturally occurring (e.g., river confluence, forest edge, drop-off) and anthropogenic (e.g., dams, roads), can influence the distribution of highly mobile organisms that have land- or seascape scale ranges. A geomorphic discontinuity framework, expanded to include ecological patterns, provides a way to incorporate important but irregularly distributed physical features into organism–environment relationships. Here, we test if migratory striped bass (Morone saxatilis) are consistently concentrated by spatial discontinuities and why. We quantified the distribution of 50 acoustically tagged striped bass at 40 sites within Plum Island Estuary, Massachusetts during four-monthly surveys relative to four physical discontinuities (sandbar, confluence, channel network, drop-off), one continuous physical feature (depth variation), and a geographic location variable (region). Despite moving throughout the estuary, striped bass were consistently clustered in the middle geographic region at sites with high sandbar area, close to channel networks, adjacent to complex confluences, with intermediate levels of bottom unevenness, and medium sized drop-offs. In addition, the highest striped bass concentrations occurred at sites with the greatest additive physical heterogeneity (i.e., where multiple discontinuities co-occurred). The need to incorporate irregularly distributed features in organism–environment relationships will increase as high-quality telemetry and GIS data accumulate for mobile organisms. The spatially explicit approach we used to address this challenge can aid both researchers who seek to understand the impact of predators on ecosystems and resource managers who require new approaches for biological conservation.

The Orphan Disease Networks

PubMed Central

Zhang, Minlu; Zhu, Cheng; Jacomy, Alexis; Lu, Long J.; Jegga, Anil G.

2011-01-01

The low prevalence rate of orphan diseases (OD) requires special combined efforts to improve diagnosis, prevention, and discovery of novel therapeutic strategies. To identify and investigate relationships based on shared genes or shared functional features, we have conducted a bioinformatic-based global analysis of all orphan diseases with known disease-causing mutant genes. Starting with a bipartite network of known OD and OD-causing mutant genes and using the human protein interactome, we first construct and topologically analyze three networks: the orphan disease network, the orphan disease-causing mutant gene network, and the orphan disease-causing mutant gene interactome. Our results demonstrate that in contrast to the common disease-causing mutant genes that are predominantly nonessential, a majority of orphan disease-causing mutant genes are essential. In confirmation of this finding, we found that OD-causing mutant genes are topologically important in the protein interactome and are ubiquitously expressed. Additionally, functional enrichment analysis of those genes in which mutations cause ODs shows that a majority result in premature death or are lethal in the orthologous mouse gene knockout models. To address the limitations of traditional gene-based disease networks, we also construct and analyze OD networks on the basis of shared enriched features (biological processes, cellular components, pathways, phenotypes, and literature citations). Analyzing these functionally-linked OD networks, we identified several additional OD-OD relations that are both phenotypically similar and phenotypically diverse. Surprisingly, we observed that the wiring of the gene-based and other feature-based OD networks are largely different; this suggests that the relationship between ODs cannot be fully captured by the gene-based network alone. PMID:21664998

Comparison of three methods for long-term monitoring of boreal lake area using Landsat TM and ETM+ imagery

USGS Publications Warehouse

Roach, Jennifer K.; Griffith, Brad; Verbyla, David

2012-01-01

Programs to monitor lake area change are becoming increasingly important in high latitude regions, and their development often requires evaluating tradeoffs among different approaches in terms of accuracy of measurement, consistency across multiple users over long time periods, and efficiency. We compared three supervised methods for lake classification from Landsat imagery (density slicing, classification trees, and feature extraction). The accuracy of lake area and number estimates was evaluated relative to high-resolution aerial photography acquired within two days of satellite overpasses. The shortwave infrared band 5 was better at separating surface water from nonwater when used alone than when combined with other spectral bands. The simplest of the three methods, density slicing, performed best overall. The classification tree method resulted in the most omission errors (approx. 2x), feature extraction resulted in the most commission errors (approx. 4x), and density slicing had the least directional bias (approx. half of the lakes with overestimated area and half of the lakes with underestimated area). Feature extraction was the least consistent across training sets (i.e., large standard error among different training sets). Density slicing was the best of the three at classifying small lakes as evidenced by its lower optimal minimum lake size criterion of 5850 m2 compared with the other methods (8550 m2). Contrary to conventional wisdom, the use of additional spectral bands and a more sophisticated method not only required additional processing effort but also had a cost in terms of the accuracy and consistency of lake classifications.

Morphologic features of basal cell carcinoma using the en-face mode in frequency domain optical coherence tomography.

PubMed

von Braunmühl, T; Hartmann, D; Tietze, J K; Cekovic, D; Kunte, C; Ruzicka, T; Berking, C; Sattler, E C

2016-11-01

Optical coherence tomography (OCT) has become a valuable non-invasive tool in the in vivo diagnosis of non-melanoma skin cancer, especially of basal cell carcinoma (BCC). Due to an updated software-supported algorithm, a new en-face mode - similar to the horizontal en-face mode in high-definition OCT and reflectance confocal microscopy - surface-parallel imaging is possible which, in combination with the established slice mode of frequency domain (FD-)OCT, may offer additional information in the diagnosis of BCC. To define characteristic morphologic features of BCC using the new en-face mode in addition to the conventional cross-sectional imaging mode for three-dimensional imaging of BCC in FD-OCT. A total of 33 BCC were examined preoperatively by imaging in en-face mode as well as cross-sectional mode in FD-OCT. Characteristic features were evaluated and correlated with histopathology findings. Features established in the cross-sectional imaging mode as well as additional features were present in the en-face mode of FD-OCT: lobulated structures (100%), dark peritumoral rim (75%), bright peritumoral stroma (96%), branching vessels (90%), compressed fibrous bundles between lobulated nests ('star shaped') (78%), and intranodular small bright dots (51%). These features were also evaluated according to the histopathological subtype. In the en-face mode, the lobulated structures with compressed fibrous bundles of the BCC were more distinct than in the slice mode. FD-OCT with a new depiction for horizontal and vertical imaging modes offers additional information in the diagnosis of BCC, especially in nodular BCC, and enhances the possibility of the evaluation of morphologic tumour features. © 2016 European Academy of Dermatology and Venereology.

Additional Improvements to the NASA Lewis Ice Accretion Code LEWICE

NASA Technical Reports Server (NTRS)

Wright, William B.; Bidwell, Colin S.

1995-01-01

Due to the feedback of the user community, three major features have been added to the NASA Lewis ice accretion code LEWICE. These features include: first, further improvements to the numerics of the code so that more time steps can be run and so that the code is more stable; second, inclusion and refinement of the roughness prediction model described in an earlier paper; third, inclusion of multi-element trajectory and ice accretion capabilities to LEWICE. This paper will describe each of these advancements in full and make comparisons with the experimental data available. Further refinement of these features and inclusion of additional features will be performed as more feedback is received.

Rigorous assessment and integration of the sequence and structure based features to predict hot spots

PubMed Central

2011-01-01

Background Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need. Results In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes. Conclusion Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots. PMID:21798070

Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination

NASA Astrophysics Data System (ADS)

Anderson, Dylan; Bapst, Aleksander; Coon, Joshua; Pung, Aaron; Kudenov, Michael

2017-05-01

Hyperspectral imaging provides a highly discriminative and powerful signature for target detection and discrimination. Recent literature has shown that considering additional target characteristics, such as spatial or temporal profiles, simultaneously with spectral content can greatly increase classifier performance. Considering these additional characteristics in a traditional discriminative algorithm requires a feature extraction step be performed first. An example of such a pipeline is computing a filter bank response to extract spatial features followed by a support vector machine (SVM) to discriminate between targets. This decoupling between feature extraction and target discrimination yields features that are suboptimal for discrimination, reducing performance. This performance reduction is especially pronounced when the number of features or available data is limited. In this paper, we propose the use of Supervised Nonnegative Tensor Factorization (SNTF) to jointly perform feature extraction and target discrimination over hyperspectral data products. SNTF learns a tensor factorization and a classification boundary from labeled training data simultaneously. This ensures that the features learned via tensor factorization are optimal for both summarizing the input data and separating the targets of interest. Practical considerations for applying SNTF to hyperspectral data are presented, and results from this framework are compared to decoupled feature extraction/target discrimination pipelines.

Poliosis overlying a nevus with blue nevus features.

PubMed

Young, Lorraine C; Van Dyke, Gregory S; Lipton, Shira; Binder, Scott W

2008-02-28

Poliosis is a localized patch of gray or white hair. Because it can be seen with a variety of disorders and drugs, a full history and exam is indicated. Additionally, it can be associated with underlying benign and malignant tumors, necessitating histological identification. We review the lesions that are reported with poliosis. In addition, we will report a case of poliosis overlying an intradermal nevus with congenital as well as blue nevus features. To the best of our knowledge, blue nevus features associated with poliosis have not been previously described.

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.

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.

Understanding Clicker Discussions: Student Reasoning and the Impact of Instructional Cues

PubMed Central

Knight, Jennifer K.; Wise, Sarah B.; Southard, Katelyn M.

2013-01-01

Previous research has shown that undergraduate science students learn from peer discussions of in-class clicker questions. However, the features that characterize such discussions are largely unknown, as are the instructional factors that may lead students into productive discussions. To explore these questions, we recorded and transcribed 83 discussions among groups of students discussing 34 different clicker questions in an upper-level developmental biology class. Discussion transcripts were analyzed for features such as making claims, questioning, and explaining reasoning. In addition, transcripts were categorized by the quality of reasoning students used and for performance features, such as percent correct on initial vote, percent correct on revote, and normalized learning change. We found that the majority of student discussions included exchanges of reasoning that used evidence and that many such exchanges resulted in students achieving the correct answer. Students also had discussions in which ideas were exchanged, but the correct answer not achieved. Importantly, instructor prompts that asked students to use reasoning resulted in significantly more discussions containing reasoning connected to evidence than without such prompts. Overall, these results suggest that these upper-level biology students readily employ reasoning in their discussions and are positively influenced by instructor cues. PMID:24297291

The New Face of Genetics: Creating A Multimedia Educational Tool for the Twenty-First Century

NASA Astrophysics Data System (ADS)

Fan, Audrey

In the study of certain genetic conditions, it is important to understand the specific "dysmorphology" associated with them. This describes the unique anatomical manifestations of the genetic condition. Traditionally, students learn about dysmorphology by reading text descriptions or looking at photographs of affected individuals. The New Face of Genetics is a film project that aims to teach students dysmorphology by featuring people who have specific genetic conditions. The goal is to enhance students' understanding of these conditions as well as to impart the humanity and beauty of the people who appear in the film. Students will have the opportunity to see dysmorphic features on the animated human form as well as meet individuals who are living with genetic difference. The target audience includes genetic counseling students and other medical professionals. Three short films were made in this format to demonstrate how this type of educational tool can be made. The featured conditions were Marfan syndrome, Sturge-Weber syndrome and Joubert syndrome. Future work will be carried out by other genetic counseling students who will make additional films based on our templates. A compendium of approximately 20 films will be eventually completed and released to genetic counseling programs and medical schools.

Prediction of distal residue participation in enzyme catalysis.

PubMed

Brodkin, Heather R; DeLateur, Nicholas A; Somarowthu, Srinivas; Mills, Caitlyn L; Novak, Walter R; Beuning, Penny J; Ringe, Dagmar; Ondrechen, Mary Jo

2015-05-01

A scoring method for the prediction of catalytically important residues in enzyme structures is presented and used to examine the participation of distal residues in enzyme catalysis. Scores are based on the Partial Order Optimum Likelihood (POOL) machine learning method, using computed electrostatic properties, surface geometric features, and information obtained from the phylogenetic tree as input features. Predictions of distal residue participation in catalysis are compared with experimental kinetics data from the literature on variants of the featured enzymes; some additional kinetics measurements are reported for variants of Pseudomonas putida nitrile hydratase (ppNH) and for Escherichia coli alkaline phosphatase (AP). The multilayer active sites of P. putida nitrile hydratase and of human phosphoglucose isomerase are predicted by the POOL log ZP scores, as is the single-layer active site of P. putida ketosteroid isomerase. The log ZP score cutoff utilized here results in over-prediction of distal residue involvement in E. coli alkaline phosphatase. While fewer experimental data points are available for P. putida mandelate racemase and for human carbonic anhydrase II, the POOL log ZP scores properly predict the previously reported participation of distal residues. 2015 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

PubMed

Mudali, D; Teune, L K; Renken, R J; Leenders, K L; Roerdink, J B T M

2015-01-01

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

Outstanding diversity of heritage features in large geological bodies: The Gachsaran Formation in southwest Iran

NASA Astrophysics Data System (ADS)

Habibi, Tahereh; Ruban, Dmitry A.

2017-09-01

The ideas of geological heritage and geological diversity have become very popular in the modern science. These are usually applied to geological domains or countries, provinces, districts, etc. Additionally, it appears to be sensible to assess heritage value of geological bodies. The review of the available knowledge and the field investigation of the Gachsaran Formation (lower Miocene) in southwest Iran permit to assign its features and the relevant phenomena to as much as 10 geological heritage types, namely stratigraphical, sedimentary, palaeontological, palaeogeographical, geomorphological, hydrogeological, engineering, structural, economical, and geohistorical types. The outstanding diversity of the features of this formation determines its high heritage value and the national rank. The geological heritage of the Gachsaran Formation is important to scientists, educators, and tourists. The Papoon and Abolhaiat sections of this formation are potential geological heritage sites, although these do not represent all above-mentioned types. The large territory, where the Gachsaran Formation outcrop, has a significant geoconservation and geotourism potential, and further inventory of geosites on this territory is necessary. Similar studies of geological bodies in North Africa and the Middle East can facilitate better understanding of the geological heritage of this vast territory.

Difference in EUV photoresist design towards reduction of LWR and LCDU

NASA Astrophysics Data System (ADS)

Jiang, Jing; De Simone, Danilo; Vandenberghe, Geert

2017-03-01

Pattern fidelity of EUV lithography is crucial for high resolution features, since small variation can affect device performance and even cause short or open circuit. For 1D features, dense lines and contact holes are the most common features for active, metal and contact layer, therefore line width roughness (LWR) and local critical dimension uniformity (LCDU) are important indexes to monitor. Both LWR and LCDU are greatly influenced by photon and acid shot noise. In addition, LWR is also affected by resist mechanical properties, like pattern collapse. In this study, we studied the influence of different chemically amplified resist components, such as polymer, PAG and quencher for both types and concentrations in order to understand the relative extent of influences of deprotection, acid diffusion, and base neutralization on pattern fidelity. However, conventional methods to approach higher resolution or low LWR/LCDU by sacrificing the dose are not sustainable. In order to continue to improve resist performance, a new component, metal salt sensitizer, is introduced into the resist system. This metal salt is able to achieve 30% dose reduction by increasing EUV absorption, maintaining LWR. We believe metal sensitizer might give us a new way to challenge the RLS trade-off.

Automatic lip reading by using multimodal visual features

NASA Astrophysics Data System (ADS)

Takahashi, Shohei; Ohya, Jun

2013-12-01

Since long time ago, speech recognition has been researched, though it does not work well in noisy places such as in the car or in the train. In addition, people with hearing-impaired or difficulties in hearing cannot receive benefits from speech recognition. To recognize the speech automatically, visual information is also important. People understand speeches from not only audio information, but also visual information such as temporal changes in the lip shape. A vision based speech recognition method could work well in noisy places, and could be useful also for people with hearing disabilities. In this paper, we propose an automatic lip-reading method for recognizing the speech by using multimodal visual information without using any audio information such as speech recognition. First, the ASM (Active Shape Model) is used to track and detect the face and lip in a video sequence. Second, the shape, optical flow and spatial frequencies of the lip features are extracted from the lip detected by ASM. Next, the extracted multimodal features are ordered chronologically so that Support Vector Machine is performed in order to learn and classify the spoken words. Experiments for classifying several words show promising results of this proposed method.

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.

Recurrent Guillain-Barré syndrome presenting stereotypic manifestations, positive antiganglioside antibodies, and rapid recovery.

PubMed

Pyun, So Young; Jeong, Jin-Ho; Bae, Jong Seok

2015-12-01

Recurrent Guillain-Barré syndrome (rGBS) has been described as a rare entity with distinct characteristics. However, little is known about rGBS in Asian group. The aim of this study was to identify the incidence and clinical course of rGBS, and to determine its clinical/pathophysiological implications. The consecutive data of 117 GBS patients were retrieved from a single university-based hospital in Korea and analyzed in terms of clinical, serological, electrophysiological aspects. A thorough review revealed that three (2.6%) of the enrolled patients had experienced more than two definite recurrent attacks of GBS. Interestingly, all three cases exhibited clinically stereotypical features, serum antiganglioside antibodies, and rapid recovery after intravenous immunoglobulin treatment. Clinical, serological, and electrophysiological features of rGBS cases were described in detail. The stereotypic presentation of each attack in this variant suggests the importance of both host and genetic factors for the clinical manifestations. In addition, the simultaneous presence of serum antiganglioside antibodies and rapid recovery implicate reversible nerve conduction failure as the mechanism of rGBS. These features are different from typical monophasic GBS and acute onset of chronic inflammatory demyelinating polyneuropathy. Copyright © 2015 Elsevier B.V. All rights reserved.

"It's a hurricane! it's a hurricane!": can music facilitate social constructive and sociodramatic play in a preschool classroom?

PubMed

Love, Angela; Burns, M Susan

2006-12-01

Sustaining attention and successfully engaging with others in collaborative play are important accomplishments focused on in preschool classrooms and childcare centers. In addition, music is frequently used in early childhood classrooms, and even recommended as an environmental feature to motivate and regulate children's behavior. Although pretend play provides appealing opportunities for developing these social abilities, no studies to date have explored the use of music as a tool to motivate and sustain constructive and social pretend play. Results from the current study indicate that within 1 preschool classroom, more sustained play (with fewer interruptions) occurred when music played as compared to when no music played in the background. In addition, significantly more dyadic play occurred when slower music played in the background, than when no music played.

Additional Security Considerations for Grid Management

NASA Technical Reports Server (NTRS)

Eidson, Thomas M.

2003-01-01

The use of Grid computing environments is growing in popularity. A Grid computing environment is primarily a wide area network that encompasses multiple local area networks, where some of the local area networks are managed by different organizations. A Grid computing environment also includes common interfaces for distributed computing software so that the heterogeneous set of machines that make up the Grid can be used more easily. The other key feature of a Grid is that the distributed computing software includes appropriate security technology. The focus of most Grid software is on the security involved with application execution, file transfers, and other remote computing procedures. However, there are other important security issues related to the management of a Grid and the users who use that Grid. This note discusses these additional security issues and makes several suggestions as how they can be managed.

Modulus and yield stress of drawn LDPE

NASA Astrophysics Data System (ADS)

Thavarungkul, Nandh

Modulus and yield stress were investigated in drawn low density polyethylene (LDPE) film. Uniaxially drawn polymeric films usually show high values of modulus and yield stress, however, studies have normally only been conducted to identify the structural features that determine modulus. In this study small-angle x-ray scattering (SAXS), thermal shrinkage, birefringence, differential scanning calorimetry (DSC), and dynamic mechanical thermal analysis (DMTA) were used to examine, directly and indirectly, the structural features that determine both modulus and yield stress, which are often closely related in undrawn materials. Shish-kebab structures are proposed to account for the mechanical properties in drawn LDPE. The validity of this molecular/morphological model was tested using relationships between static mechanical data and structural and physical parameters. In addition, dynamic mechanical results are also in line with static data in supporting the model. In the machine direction (MD), "shish" and taut tie molecules (TTM) anchored in the crystalline phase account for E; whereas crystal lamellae with contributions from "shish" and TTM determine yield stress. In the transverse direction (TD), the crystalline phase plays an important roll in both modulus and yield stress. Modulus is determined by crystal lamellae functioning as platelet reinforcing elements in the amorphous matrix with an additional contributions from TTM and yield stress is determined by the crystal lamellae's resistance to deformation.

Comparing object recognition from binary and bipolar edge images for visual prostheses

PubMed Central

Jung, Jae-Hyun; Pu, Tian; Peli, Eli

2017-01-01

Visual prostheses require an effective representation method due to the limited display condition which has only 2 or 3 levels of grayscale in low resolution. Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary (black and white) edge images have been used to represent features to convey essential information. However, in scenes with a complex cluttered background, the recognition rate of the binary edge images by human observers is limited and additional information is required. The polarity of edges and cusps (black or white features on a gray background) carries important additional information; the polarity may provide shape from shading information missing in the binary edge image. This depth information may be restored by using bipolar edges. We compared object recognition rates from 16 binary edge images and bipolar edge images by 26 subjects to determine the possible impact of bipolar filtering in visual prostheses with 3 or more levels of grayscale. Recognition rates were higher with bipolar edge images and the improvement was significant in scenes with complex backgrounds. The results also suggest that erroneous shape from shading interpretation of bipolar edges resulting from pigment rather than boundaries of shape may confound the recognition. PMID:28458481

Robustly detecting differential expression in RNA sequencing data using observation weights

PubMed Central

Zhou, Xiaobei; Lindsay, Helen; Robinson, Mark D.

2014-01-01

A popular approach for comparing gene expression levels between (replicated) conditions of RNA sequencing data relies on counting reads that map to features of interest. Within such count-based methods, many flexible and advanced statistical approaches now exist and offer the ability to adjust for covariates (e.g. batch effects). Often, these methods include some sort of ‘sharing of information’ across features to improve inferences in small samples. It is important to achieve an appropriate tradeoff between statistical power and protection against outliers. Here, we study the robustness of existing approaches for count-based differential expression analysis and propose a new strategy based on observation weights that can be used within existing frameworks. The results suggest that outliers can have a global effect on differential analyses. We demonstrate the effectiveness of our new approach with real data and simulated data that reflects properties of real datasets (e.g. dispersion-mean trend) and develop an extensible framework for comprehensive testing of current and future methods. In addition, we explore the origin of such outliers, in some cases highlighting additional biological or technical factors within the experiment. Further details can be downloaded from the project website: http://imlspenticton.uzh.ch/robinson_lab/edgeR_robust/. PMID:24753412

H syndrome: the first 79 patients.

PubMed

Molho-Pessach, Vered; Ramot, Yuval; Camille, Frances; Doviner, Victoria; Babay, Sofia; Luis, Siekavizza Juan; Broshtilova, Valentina; Zlotogorski, Abraham

2014-01-01

H syndrome is an autosomal recessive genodermatosis with multisystem involvement caused by mutations in SLC29A3. We sought to investigate the clinical and molecular findings in 79 patients with this disorder. A total of 79 patients were included, of which 13 are newly reported cases. Because of the phenotypic similarity and molecular overlap with H syndrome, we included 18 patients with allelic disorders. For 31 patients described by others, data were gathered from the medical literature. The most common clinical features (>45% of patients) were hyperpigmentation, phalangeal flexion contractures, hearing loss, and short stature. Insulin-dependent diabetes mellitus and lymphadenopathy mimicking Rosai-Dorfman disease were each found in approximately 20%. Additional systemic features were described in less than 15% of cases. Marked interfamilial and intrafamilial clinical variability exists. Twenty mutations have been identified in SLC29A3, with no genotype-phenotype correlation. In the 31 patients described by others, data were collected from the medical literature. H syndrome is a multisystemic disease with clinical variability. Consequently, all SLC29A3-related diseases should be considered a single entity. Recognition of the pleomorphic nature of H syndrome is important for diagnosis of additional patients. Copyright © 2013 American Academy of Dermatology, Inc. Published by Mosby, Inc. All rights reserved.

Hidden Markov Model and Support Vector Machine based decoding of finger movements using Electrocorticography

PubMed Central

Wissel, Tobias; Pfeiffer, Tim; Frysch, Robert; Knight, Robert T.; Chang, Edward F.; Hinrichs, Hermann; Rieger, Jochem W.; Rose, Georg

2013-01-01

Objective Support Vector Machines (SVM) have developed into a gold standard for accurate classification in Brain-Computer-Interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of Hidden Markov Models (HMM)for online BCIs and discuss strategies to improve their performance. Approach We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from the Electrocorticograms of four subjects doing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. Main results We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. Significance We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online brain-computer interfaces. PMID:24045504

DASS-GUI: a user interface for identification and analysis of significant patterns in non-sequential data.

PubMed

Hollunder, Jens; Friedel, Maik; Kuiper, Martin; Wilhelm, Thomas

2010-04-01

Many large 'omics' datasets have been published and many more are expected in the near future. New analysis methods are needed for best exploitation. We have developed a graphical user interface (GUI) for easy data analysis. Our discovery of all significant substructures (DASS) approach elucidates the underlying modularity, a typical feature of complex biological data. It is related to biclustering and other data mining approaches. Importantly, DASS-GUI also allows handling of multi-sets and calculation of statistical significances. DASS-GUI contains tools for further analysis of the identified patterns: analysis of the pattern hierarchy, enrichment analysis, module validation, analysis of additional numerical data, easy handling of synonymous names, clustering, filtering and merging. Different export options allow easy usage of additional tools such as Cytoscape. Source code, pre-compiled binaries for different systems, a comprehensive tutorial, case studies and many additional datasets are freely available at http://www.ifr.ac.uk/dass/gui/. DASS-GUI is implemented in Qt.

Effects of multiple enzyme-substrate interactions in basic units of cellular signal processing

NASA Astrophysics Data System (ADS)

Seaton, D. D.; Krishnan, J.

2012-08-01

Covalent modification cycles are a ubiquitous feature of cellular signalling networks. In these systems, the interaction of an active enzyme with the unmodified form of its substrate is essential for signalling to occur. However, this interaction is not necessarily the only enzyme-substrate interaction possible. In this paper, we analyse the behaviour of a basic model of signalling in which additional, non-essential enzyme-substrate interactions are possible. These interactions include those between the inactive form of an enzyme and its substrate, and between the active form of an enzyme and its product. We find that these additional interactions can result in increased sensitivity and biphasic responses, respectively. The dynamics of the responses are also significantly altered by the presence of additional interactions. Finally, we evaluate the consequences of these interactions in two variations of our basic model, involving double modification of substrate and scaffold-mediated signalling, respectively. We conclude that the molecular details of protein-protein interactions are important in determining the signalling properties of enzymatic signalling pathways.

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.

Build platform that provides mechanical engagement with additive manufacturing prints

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

Elliott, Amelia M.

A build platform and methods of fabricating an article with such a platform in an extrusion-type additive manufacturing machine are provided. A platform body 202 includes features 204 that extend outward from the body 202. The features 204 define protrusive areas 206 and recessive areas 208 that cooperate to mechanically engage the extruded material that forms the initial layers 220 of an article when the article is being fabricated by a nozzle 12 of the additive manufacturing machine 10.

Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder

PubMed Central

Taniguchi, Tadahiro; Takenaka, Kazuhito; Bando, Takashi

2018-01-01

Data representing driving behavior, as measured by various sensors installed in a vehicle, are collected as multi-dimensional sensor time-series data. These data often include redundant information, e.g., both the speed of wheels and the engine speed represent the velocity of the vehicle. Redundant information can be expected to complicate the data analysis, e.g., more factors need to be analyzed; even varying the levels of redundancy can influence the results of the analysis. We assume that the measured multi-dimensional sensor time-series data of driving behavior are generated from low-dimensional data shared by the many types of one-dimensional data of which multi-dimensional time-series data are composed. Meanwhile, sensor time-series data may be defective because of sensor failure. Therefore, another important function is to reduce the negative effect of defective data when extracting low-dimensional time-series data. This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the experiments, we show that DSAE provides high-performance latent feature extraction for driving behavior, even for defective sensor time-series data. In addition, we show that the negative effect of defects on the driving behavior segmentation task could be reduced using the latent features extracted by DSAE. PMID:29462931

Insights into the dynamics of planetary interiors obtained through the study of global distribution of volcanoes I: Empirical calibration on Earth

NASA Astrophysics Data System (ADS)

Cañon-Tapia, Edgardo; Mendoza-Borunda, Ramón

2014-06-01

The distribution of volcanic features is ultimately controlled by processes taking place beneath the surface of a planet. For this reason, characterization of volcano distribution at a global scale can be used to obtain insights concerning dynamic aspects of planetary interiors. Until present, studies of this type have focused on volcanic features of a specific type, or have concentrated on relatively small regions. In this paper, (the first of a series of three papers) we describe the distribution of volcanic features observed over the entire surface of the Earth, combining an extensive database of submarine and subaerial volcanoes. The analysis is based on spatial density contours obtained with the Fisher kernel. Based on an empirical approach that makes no a priori assumptions concerning the number of modes that should characterize the density distribution of volcanism we identified the most significant modes. Using those modes as a base, the relevant distance for the formation of clusters of volcanoes is constrained to be on the order of 100 to 200 km. In addition, it is noted that the most significant modes lead to the identification of clusters that outline the most important tectonic margins on Earth without the need of making any ad hoc assumptions. Consequently, we suggest that this method has the potential of yielding insights about the probable occurrence of tectonic features within other planets.

Discriminant analysis of multiple cortical changes in mild cognitive impairment

NASA Astrophysics Data System (ADS)

Wu, Congling; Guo, Shengwen; Lai, Chunren; Wu, Yupeng; Zhao, Di; Jiang, Xingjun

2017-02-01

To reveal the differences in brain structures and morphological changes between the mild cognitive impairment (MCI) and the normal control (NC), analyze and predict the risk of MCI conversion. First, the baseline and 2-year longitudinal follow-up magnetic resonance (MR) images of 73 NC, 46 patients with stable MCI (sMCI) and 40 patients with converted MCI (cMCI) were selected. Second, the FreeSurfer was used to extract the cortical features, including the cortical thickness, surface area, gray matter volume and mean curvature. Third, the support vector machine-recursive feature elimination method (SVM-RFE) were adopted to determine salient features for effective discrimination. Finally, the distribution and importance of essential brain regions were described. The experimental results showed that the cortical thickness and gray matter volume exhibited prominent capability in discrimination, and surface area and mean curvature behaved relatively weak. Furthermore, the combination of different morphological features, especially the baseline combined with the longitudinal changes, can be used to evidently improve the performance of classification. In addition, brain regions with high weights predominately located in the temporal lobe and the frontal lobe, which were relative to emotional control and memory functions. It suggests that there were significant different patterns in the brain structure and changes between the compared group, which could not only be effectively applied for classification, but also be used to evaluate and predict the conversion of the patients with MCI.

Morphologic and seismic evidence of rapid submergence offshore Cide-Sinop in the southern Black Sea shelf

NASA Astrophysics Data System (ADS)

Ocakoğlu, Neslihan; İşcan, Yeliz; Kılıç, Fatmagül; Özel, Oğuz

2018-06-01

Multi-beam bathymetric and multi-channel seismic reflection data obtained offshore Cide-Sinop have revealed important records on the latest transgression of the Black Sea for the first time. A relatively large shelf plain within the narrow southern continental shelf characterized by a flat seafloor morphology at -100 water depth followed by a steep continental slope leading to -500 m depth. This area is widely covered by submerged morphological features such as dunes, lagoons, possible aeolianites, an eroded anticline and small channels that developed by aeolian and fluvial processes. These morphological features sit upon an erosional surface that truncates the top of all seismic units and constitutes the seafloor over the whole shelf. The recent prograded delta deposits around the shelf break are also truncated by the similar erosional surface. These results indicate that offshore Cide-Sinop was once a terrestrial landscape that was then submerged. The interpreted paleoshoreline varies from -100 to -120 m. This variation can be explained by not only sea level changes but also the active faults observed on the seismic section. The effective protection of morphological features on the seafloor is the evidence of abrupt submergence rather than gradual. In addition, the absence of coastal onlaps suggests that these morphological features should have developed at low sea level before the latest sea level rise in the Black Sea.

Classification of visual and linguistic tasks using eye-movement features.

PubMed

Coco, Moreno I; Keller, Frank

2014-03-07

The role of the task has received special attention in visual-cognition research because it can provide causal explanations of goal-directed eye-movement responses. The dependency between visual attention and task suggests that eye movements can be used to classify the task being performed. A recent study by Greene, Liu, and Wolfe (2012), however, fails to achieve accurate classification of visual tasks based on eye-movement features. In the present study, we hypothesize that tasks can be successfully classified when they differ with respect to the involvement of other cognitive domains, such as language processing. We extract the eye-movement features used by Greene et al. as well as additional features from the data of three different tasks: visual search, object naming, and scene description. First, we demonstrated that eye-movement responses make it possible to characterize the goals of these tasks. Then, we trained three different types of classifiers and predicted the task participants performed with an accuracy well above chance (a maximum of 88% for visual search). An analysis of the relative importance of features for classification accuracy reveals that just one feature, i.e., initiation time, is sufficient for above-chance performance (a maximum of 79% accuracy in object naming). Crucially, this feature is independent of task duration, which differs systematically across the three tasks we investigated. Overall, the best task classification performance was obtained with a set of seven features that included both spatial information (e.g., entropy of attention allocation) and temporal components (e.g., total fixation on objects) of the eye-movement record. This result confirms the task-dependent allocation of visual attention and extends previous work by showing that task classification is possible when tasks differ in the cognitive processes involved (purely visual tasks such as search vs. communicative tasks such as scene description).

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.

PubMed

Nie, Zhi; Vairavan, Srinivasan; Narayan, Vaibhav A; Ye, Jieping; Li, Qingqin S

2018-01-01

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant depression (TRD) via partition of the data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort into a training and a testing dataset. We also included data from a small yet completely independent cohort RIS-INT-93 as an external test dataset. We used features from enrollment and level 1 treatment (up to week 2 response only) of STAR*D to explore the feature space comprehensively and applied machine learning methods to model TRD outcome at level 2. For TRD defined using QIDS-C16 remission criteria, multiple machine learning models were internally cross-validated in the STAR*D training dataset and externally validated in both the STAR*D testing dataset and RIS-INT-93 independent dataset with an area under the receiver operating characteristic curve (AUC) of 0.70-0.78 and 0.72-0.77, respectively. The upper bound for the AUC achievable with the full set of features could be as high as 0.78 in the STAR*D testing dataset. Model developed using top 30 features identified using feature selection technique (k-means clustering followed by χ2 test) achieved an AUC of 0.77 in the STAR*D testing dataset. In addition, the model developed using overlapping features between STAR*D and RIS-INT-93, achieved an AUC of > 0.70 in both the STAR*D testing and RIS-INT-93 datasets. Among all the features explored in STAR*D and RIS-INT-93 datasets, the most important feature was early or initial treatment response or symptom severity at week 2. These results indicate that prediction of TRD prior to undergoing a second round of antidepressant treatment could be feasible even in the absence of biomarker data.

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

Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping

NASA Astrophysics Data System (ADS)

Yousefi, Mahyar; Carranza, Emmanuel John M.

2015-01-01

Complexities of geological processes portrayed as certain feature in a map (e.g., faults) are natural sources of uncertainties in decision-making for exploration of mineral deposits. Besides natural sources of uncertainties, knowledge-driven (e.g., fuzzy logic) mineral prospectivity mapping (MPM) is also plagued and incurs further uncertainty in subjective judgment of analyst when there is no reliable proven value of evidential scores corresponding to relative importance of geological features that can directly be measured. In this regard, analysts apply expert opinion to assess relative importance of spatial evidences as meaningful decision support. This paper aims for fuzzification of continuous spatial data used as proxy evidence to facilitate and to support fuzzy MPM to generate exploration target areas for further examination of undiscovered deposits. In addition, this paper proposes to adapt the concept of expected value to further improve fuzzy logic MPM because the analysis of uncertain variables can be presented in terms of their expected value. The proposed modified expected value approach to MPM is not only a multi-criteria approach but it also treats uncertainty of geological processes a depicted by maps or spatial data in term of biased weighting more realistically in comparison with classified evidential maps because fuzzy membership scores are defined continuously whereby, for example, there is no need to categorize distances from evidential features to proximity classes using arbitrary intervals. The proposed continuous weighting approach and then integrating the weighted evidence layers by using modified expected value function, described in this paper can be used efficiently in either greenfields or brownfields.

Clinical Features and Related Factors of Poststroke Pathological Laughing and Crying: A Case-Control Study.

PubMed

Wang, Geying; Teng, Fei; Chen, Yuhui; Liu, Yuanhua; Li, Yancheng; Cai, Li; Zhang, Xu; Nie, Zhiyu; Jin, Lingjing

2016-03-01

The purpose of this study was to analyze clinical features and related factors of poststroke pathological laughing and crying (PSPLC) and to differentiate PSPLC patients with and without pseudobulbar signs. We performed a case-control study in which 56 patients with PSPLC were matched to 56 control stroke patients by age and gender. The pathological laughing and crying scale was used to identify patients with PSPLC. Characteristics of PSPLC outbursts, presence of pseudobulbar signs and autonomic symptoms, lesion locations, and different clinical data were analyzed. Mild cognitive impairment (MCI) was evaluated by the Montreal Cognitive Assessment. Poststroke anger proneness (PSAP) was evaluated by comparison of the patients' premorbid states. Significantly more patients in the PSPLC group showed MCI, PSAP, and pseudobulbar signs than those in the control group. Most patients with PSPLC showed bilateral multiple lesions and the pons (especially the bilateral paramedian basal and basal-tegmental areas) stood out as the most important lesion location. Logistic regression analysis showed that pontine lesion, MCI, and PSAP were independently related to PSPLC; however, the presence of pseudobulbar signs was not related. PSPLC patients with pseudobulbar signs showed more recurrent strokes in the previous 2 years, more severe neurological deficits, as well as higher severity of PSPLC. In addition, more patients in the group with pseudobulbar signs showed concomitant autonomic symptoms. PSPLC, MCI, and PSAP could be manifestations of a more general disorder, in which pontine lesion plays an important role. PSPLC patients with pseudobulbar signs and those without show different features. Copyright © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Testing the Prediction of Iron Alteration Minerals on Low Albedo Asteroids

NASA Technical Reports Server (NTRS)

Jarvis, K. S.; Vilas, Faith; Howell, E.; Kelley, M.; Cochran, A.

1999-01-01

Absorption features centered near 0.60 - 0.65 and 0.80 - 0.90 micron were identified in the spectra of three low-albedo main-belt (165, 368, 877) and two low-albedo outer-belt (225, 334) asteroids (Vilas et al., Icarus, v. 109,274,1994). The absorption features were attributed to charge transfer transitions in iron alteration minerals such as goethite, hematite, and jarosite, all products of aqueous alteration. Concurrently, Jarvis et al. (LPSC XXIV, 715, 1993) presented additional spectra of low-albedo asteroids that had absorption features centered near 0.60 - 0.65 micron without the longer wavelength feature. Since these two features in iron oxides originate from the same ground state, and the longer wavelength feature requires less energy to exist, the single shorter wavelength feature cannot be caused by the iron alteration minerals. In addition, spectra of minerals such as hematite and goethite show a rapid increase in reflectance beginning near 0.5 micron absent in the low-albedo asteroid spectra. The absence of this rise has been attributed to its suppresion from opaques in the surface material. Spectra on more than one night were available for only one of these five asteroids, 225 Henrietta, and showed good repeatability of the 0.65-micron feature. We have acquired additional spectra of all five asteroids in order to test the repeatability of the 0.65-micron feature, and the presence and repeatability of the features centered near 0.8 - 0.9 micron. We specifically will test the possibility that longer wavelength features could be caused by incomplete removal of telluric water. Asteroid 877 Walkure is a member of the Nysa-Hertha family, and will be compared to spectra of other members of that family. Data were acquired in 1996 and 1999 on the 2.1-m telescope with a facility cassegrain spectrograph, McDonald Observatory, Univ. Of Texas, and the 1.5-m telescope with facility cassegrain spectrograph at CTIO. This research is supported by the NASA Planetary Astronomy Program.

Proteome-metabolome profiling of ovarian cancer ascites reveals novel components involved in intercellular communication.

PubMed

Shender, Victoria O; Pavlyukov, Marat S; Ziganshin, Rustam H; Arapidi, Georgij P; Kovalchuk, Sergey I; Anikanov, Nikolay A; Altukhov, Ilya A; Alexeev, Dmitry G; Butenko, Ivan O; Shavarda, Alexey L; Khomyakova, Elena B; Evtushenko, Evgeniy; Ashrafyan, Lev A; Antonova, Irina B; Kuznetcov, Igor N; Gorbachev, Alexey Yu; Shakhparonov, Mikhail I; Govorun, Vadim M

2014-12-01

Ovarian cancer ascites is a native medium for cancer cells that allows investigation of their secretome in a natural environment. This medium is of interest as a promising source of potential biomarkers, and also as a medium for cell-cell communication. The aim of this study was to elucidate specific features of the malignant ascites metabolome and proteome. In order to omit components of the systemic response to ascites formation, we compared malignant ascites with cirrhosis ascites. Metabolome analysis revealed 41 components that differed significantly between malignant and cirrhosis ascites. Most of the identified cancer-specific metabolites are known to be important signaling molecules. Proteomic analysis identified 2096 and 1855 proteins in the ovarian cancer and cirrhosis ascites, respectively; 424 proteins were specific for the malignant ascites. Functional analysis of the proteome demonstrated that the major differences between cirrhosis and malignant ascites were observed for the cluster of spliceosomal proteins. Additionally, we demonstrate that several splicing RNAs were exclusively detected in malignant ascites, where they probably existed within protein complexes. This result was confirmed in vitro using an ovarian cancer cell line. Identification of spliceosomal proteins and RNAs in an extracellular medium is of particular interest; the finding suggests that they might play a role in the communication between cancer cells. In addition, malignant ascites contains a high number of exosomes that are known to play an important role in signal transduction. Thus our study reveals the specific features of malignant ascites that are associated with its function as a medium of intercellular communication. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

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.

Discriminative prediction of mammalian enhancers from DNA sequence

PubMed Central

Lee, Dongwon; Karchin, Rachel; Beer, Michael A.

2011-01-01

Accurately predicting regulatory sequences and enhancers in entire genomes is an important but difficult problem, especially in large vertebrate genomes. With the advent of ChIP-seq technology, experimental detection of genome-wide EP300/CREBBP bound regions provides a powerful platform to develop predictive tools for regulatory sequences and to study their sequence properties. Here, we develop a support vector machine (SVM) framework which can accurately identify EP300-bound enhancers using only genomic sequence and an unbiased set of general sequence features. Moreover, we find that the predictive sequence features identified by the SVM classifier reveal biologically relevant sequence elements enriched in the enhancers, but we also identify other features that are significantly depleted in enhancers. The predictive sequence features are evolutionarily conserved and spatially clustered, providing further support of their functional significance. Although our SVM is trained on experimental data, we also predict novel enhancers and show that these putative enhancers are significantly enriched in both ChIP-seq signal and DNase I hypersensitivity signal in the mouse brain and are located near relevant genes. Finally, we present results of comparisons between other EP300/CREBBP data sets using our SVM and uncover sequence elements enriched and/or depleted in the different classes of enhancers. Many of these sequence features play a role in specifying tissue-specific or developmental-stage-specific enhancer activity, but our results indicate that some features operate in a general or tissue-independent manner. In addition to providing a high confidence list of enhancer targets for subsequent experimental investigation, these results contribute to our understanding of the general sequence structure of vertebrate enhancers. PMID:21875935

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.

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

PubMed Central

Roy, Sushmita; Martinez, Diego; Platero, Harriett; Lane, Terran; Werner-Washburne, Margaret

2009-01-01

Background Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information. Results AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins. Conclusion AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains. PMID:19936254

A UWB Radar Signal Processing Platform for Real-Time Human Respiratory Feature Extraction Based on Four-Segment Linear Waveform Model.

PubMed

Hsieh, Chi-Hsuan; Chiu, Yu-Fang; Shen, Yi-Hsiang; Chu, Ta-Shun; Huang, Yuan-Hao

2016-02-01

This paper presents an ultra-wideband (UWB) impulse-radio radar signal processing platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal processing platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.

Guidance of dorsal root ganglion neurites and Schwann cells by isolated Schwann cell topography on poly(dimethyl siloxane) conduits and films

NASA Astrophysics Data System (ADS)

Richardson, J. A.; Rementer, C. W.; Bruder, Jan M.; Hoffman-Kim, D.

2011-08-01

Biomimetic replicas of cellular topography have been utilized to direct neurite outgrowth. Here, we cultured postnatal rat dorsal root ganglion (DRG) explants in the presence of Schwann cell (SC) topography to determine the influence of SC topography on neurite outgrowth. Four distinct poly(dimethyl siloxane) conduits were fabricated within which DRG explants were cultured. To determine the contribution of SC topographical features to neurite guidance, the extent of neurite outgrowth into unpatterned conduits, conduits with randomly oriented SC replicas, and conduits with SC replicas parallel or perpendicular to the conduit long axis was measured. Neurite directionality and outgrowth from DRG were also quantified on two-dimensional SC replicas with orientations corresponding to the four conduit conditions. Additionally, live SC migration and neurite extension from DRG on SC replicas were examined as a first step toward quantification of the interactions between live SC and navigating neurites on SC replicas. DRG neurite outgrowth and morphology within conduits and on two-dimensional SC replicas were directed by the underlying SC topographical features. Maximal neurite outgrowth and alignment to the underlying features were observed into parallel conduits and on parallel two-dimensional substrates, whereas the least extent of outgrowth was observed into perpendicular conduits and on perpendicular two-dimensional replica conditions. Additionally, neurites on perpendicular conditions turned to extend along the direction of underlying SC topography. Neurite outgrowth exceeded SC migration in the direction of the underlying anisotropic SC replica after two days in culture. This finding confirms the critical role that SC have in guiding neurite outgrowth and suggests that the mechanism of neurite alignment to SC replicas depends on direct contact with cellular topography. These results suggest that SC topographical replicas may be used to direct and optimize neurite alignment, and emphasize the importance of SC features in neurite guidance.

A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing.

PubMed

van den Akker, Jeroen; Mishne, Gilad; Zimmer, Anjali D; Zhou, Alicia Y

2018-04-17

Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and software tools, the majority of variants called using NGS alone are in fact accurate and reliable. However, a small subset of difficult-to-call variants that still do require orthogonal confirmation exist. For this reason, many clinical laboratories confirm NGS results using orthogonal technologies such as Sanger sequencing. Here, we report the development of a deterministic machine-learning-based model to differentiate between these two types of variant calls: those that do not require confirmation using an orthogonal technology (high confidence), and those that require additional quality testing (low confidence). This approach allows reliable NGS-based calling in a clinical setting by identifying the few important variant calls that require orthogonal confirmation. We developed and tested the model using a set of 7179 variants identified by a targeted NGS panel and re-tested by Sanger sequencing. The model incorporated several signals of sequence characteristics and call quality to determine if a variant was identified at high or low confidence. The model was tuned to eliminate false positives, defined as variants that were called by NGS but not confirmed by Sanger sequencing. The model achieved very high accuracy: 99.4% (95% confidence interval: +/- 0.03%). It categorized 92.2% (6622/7179) of the variants as high confidence, and 100% of these were confirmed to be present by Sanger sequencing. Among the variants that were categorized as low confidence, defined as NGS calls of low quality that are likely to be artifacts, 92.1% (513/557) were found to be not present by Sanger sequencing. This work shows that NGS data contains sufficient characteristics for a machine-learning-based model to differentiate low from high confidence variants. Additionally, it reveals the importance of incorporating site-specific features as well as variant call features in such a model.

Prospective study of POLG mutations presenting in children with intractable epilepsy: prevalence and clinical features.

PubMed

Uusimaa, Johanna; Gowda, Vasantha; McShane, Anthony; Smith, Conrad; Evans, Julie; Shrier, Annie; Narasimhan, Manisha; O'Rourke, Anthony; Rajabally, Yusuf; Hedderly, Tammy; Cowan, Frances; Fratter, Carl; Poulton, Joanna

2013-06-01

To assess the frequency and clinical features of childhood-onset intractable epilepsy caused by the most common mutations in the POLG gene, which encodes the catalytic subunit of mitochondrial DNA polymerase gamma. Children presenting with nonsyndromic intractable epilepsy of unknown etiology but without documented liver dysfunction at presentation were eligible for this prospective, population-based study. Blood samples were analyzed for the three most common POLG mutations. If any of the three tested mutations were found, all the exons and the exon-intron boundaries of the POLG gene were sequenced. In addition, we retrospectively reviewed the notes of patients presenting with intractable epilepsy in which we had found POLG mutations. All available clinical data were collected by questionnaire and by reviewing the medical records. We analyzed 213 blood DNA samples from patients fulfilling the inclusion criteria of the prospective study. Among these, five patients (2.3%) were found with one of the three common POLG mutations as homozygous or compound heterozygous states. In addition, three patients were retrospectively identified. Seven of the eight patients had either raised cerebrospinal fluid (CSF) lactate (n = 3) or brain magnetic resonance imaging (MRI) changes (n = 4) at presentation with intractable epilepsy. Three patients later developed liver dysfunction, progressing to fatal liver failure in two without previous treatment with sodium valproate (VPA). Furthermore, it is worth mentioning that one patient presented first with an autism spectrum disorder before seizures emerged. Mutations in POLG are an important cause of early and juvenile onset nonsyndromic intractable epilepsy with highly variable associated manifestations including autistic features. This study emphasizes that genetic testing for POLG mutations in patients with nonsyndromic intractable epilepsies is very important for clinical diagnostics, genetic counseling, and treatment decisions because of the increased risk for VPA-induced liver failure in patients with POLG mutations. We recommend POLG gene testing for patients with intractable seizures and at least one elevated CSF lactate or suggestive brain MRI changes (predominantly abnormal T2 -weighted thalamic signal) with or without status epilepticus, epilepsia partialis continua, or liver manifestations typical for Alpers disease, especially when the disease course is progressive. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

Prospective study of POLG mutations presenting in children with intractable epilepsy: Prevalence and clinical features

PubMed Central

Uusimaa, Johanna; Gowda, Vasantha; McShane, Anthony; Smith, Conrad; Evans, Julie; Shrier, Annie; Narasimhan, Manisha; O'Rourke, Anthony; Rajabally, Yusuf; Hedderly, Tammy; Cowan, Frances; Fratter, Carl; Poulton, Joanna

2013-01-01

Purpose To assess the frequency and clinical features of childhood-onset intractable epilepsy caused by the most common mutations in the POLG gene, which encodes the catalytic subunit of mitochondrial DNA polymerase gamma. Methods Children presenting with nonsyndromic intractable epilepsy of unknown etiology but without documented liver dysfunction at presentation were eligible for this prospective, population-based study. Blood samples were analyzed for the three most common POLG mutations. If any of the three tested mutations were found, all the exons and the exon–intron boundaries of the POLG gene were sequenced. In addition, we retrospectively reviewed the notes of patients presenting with intractable epilepsy in which we had found POLG mutations. All available clinical data were collected by questionnaire and by reviewing the medical records. Key Findings We analyzed 213 blood DNA samples from patients fulfilling the inclusion criteria of the prospective study. Among these, five patients (2.3%) were found with one of the three common POLG mutations as homozygous or compound heterozygous states. In addition, three patients were retrospectively identified. Seven of the eight patients had either raised cerebrospinal fluid (CSF) lactate (n = 3) or brain magnetic resonance imaging (MRI) changes (n = 4) at presentation with intractable epilepsy. Three patients later developed liver dysfunction, progressing to fatal liver failure in two without previous treatment with sodium valproate (VPA). Furthermore, it is worth mentioning that one patient presented first with an autism spectrum disorder before seizures emerged. Significance Mutations in POLG are an important cause of early and juvenile onset nonsyndromic intractable epilepsy with highly variable associated manifestations including autistic features. This study emphasizes that genetic testing for POLG mutations in patients with nonsyndromic intractable epilepsies is very important for clinical diagnostics, genetic counseling, and treatment decisions because of the increased risk for VPA-induced liver failure in patients with POLG mutations. We recommend POLG gene testing for patients with intractable seizures and at least one elevated CSF lactate or suggestive brain MRI changes (predominantly abnormal T2-weighted thalamic signal) with or without status epilepticus, epilepsia partialis continua, or liver manifestations typical for Alpers disease, especially when the disease course is progressive. PMID:23448099

Kindler syndrome.

PubMed

Sharma, Ramesh Chander; Mahajan, Vikram; Sharma, Nand Lal; Sharma, Ashok K

2003-09-01

Kindler syndrome is a rare genodermatosis characterized by acral bullae and photosensitivity. The photosensitivity improves with advancing age and results in progressive poikiloderma and cutaneous atrophy, and many additional features have also been described. This report describes two male Kindler syndrome patients with classical features of acral blistering and photosensitivity in childhood, and subsequent development of poikiloderma, leukokeratosis of oro-ano-genital mucosae, phimosis and meatal stenosis. The first patient had additional ophthalmic features of chronic simple conjunctivitis caused by persistent irritation, multiple stromal nebular corneal opacities and thickened corneal nerves. The second patient showed skeletal changes, namely a dome-shaped skull (turri-cephaly), bifid fourth rib, missing fifth rib, short fourth and fifth metacarpals and mandibular abnormalities. This is the first report of such ophthalmic and skeletal features of Kindler syndrome.

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

Holographic vortices in the presence of dark matter sector

NASA Astrophysics Data System (ADS)

Rogatko, Marek; Wysokinski, Karol I.

2015-12-01

The dark matter seem to be an inevitable ingredient of the total matter configuration in the Universe and the knowledge how the dark matter affects the properties of superconductors is of vital importance for the experiments aimed at its direct detection. The homogeneous magnetic field acting perpendicularly to the surface of (2+1) dimensional s-wave holographic superconductor in the theory with dark matter sector has been modeled by the additional U(1)-gauge field representing dark matter and coupled to the Maxwell one. As expected the free energy for the vortex configuration turns out to be negative. Importantly its value is lower in the presence of dark matter sector. This feature can explain why in the Early Universe first the web of dark matter appeared and next on these gratings the ordinary matter forming cluster of galaxies has formed.

Recent Advances in Conotoxin Classification by Using Machine Learning Methods.

PubMed

Dao, Fu-Ying; Yang, Hui; Su, Zhen-Dong; Yang, Wuritu; Wu, Yun; Hui, Ding; Chen, Wei; Tang, Hua; Lin, Hao

2017-06-25

Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

Health Selection, Migration, and HIV Infection in Malawi.

PubMed

Anglewicz, Philip; VanLandingham, Mark; Manda-Taylor, Lucinda; Kohler, Hans-Peter

2018-04-27

Despite its importance in studies of migrant health, selectivity of migrants-also known as migration health selection-has seldom been examined in sub-Saharan Africa (SSA). This neglect is problematic because several features of the context in which migration occurs in SSA-very high levels of HIV, in particular-differ from contextual features in regions that have been studied more thoroughly. To address this important gap, we use longitudinal panel data from Malawi to examine whether migrants differ from nonmigrants in pre-migration health, assessed via SF-12 measures of mental and physical health. In addition to overall health selection, we focus on three more-specific factors that may affect the relationship between migration and health: (1) whether migration health selection differs by destination (rural-rural, rural-town, and rural-urban), (2) whether HIV infection moderates the relationship between migration and health, and (3) whether circular migrants differ in pre-migration health status. We find evidence of the healthy migrant phenomenon in Malawi, where physically healthier individuals are more likely to move. This relationship varies by migration destination, with healthier rural migrants moving to urban and other rural areas. We also find interactions between HIV-infected status and health: HIV-infected women moving to cities are physically healthier than their nonmigrant counterparts.

Design and implementation of a status at a glance user interface for a power distribution expert system

NASA Technical Reports Server (NTRS)

Liberman, Eugene M.; Manner, David B.; Dolce, James L.; Mellor, Pamela A.

1993-01-01

Expert systems are widely used in health monitoring and fault detection applications. One of the key features of an expert system is that it possesses a large body of knowledge about the application for which it was designed. When the user consults this knowledge base, it is essential that the expert system's reasoning process and its conclusions be as concise as possible. If, in addition, an expert system is part of a process monitoring system, the expert system's conclusions must be combined with current events of the process. Under these circumstances, it is difficult for a user to absorb and respond to all the available information. For example, a user can become distracted and confused if two or more unrelated devices in different parts of the system require attention. A human interface designed to integrate expert system diagnoses with process data and to focus the user's attention to the important matters provides a solution to the 'information overload' problem. This paper will discuss a user interface to the power distribution expert system for Space Station Freedom. The importance of features which simplify assessing system status and which minimize navigating through layers of information will be discussed. Design rationale and implementation choices will also be presented.

Distribution of Upper Circumpolar Deep Water on the warming continental shelf of the West Antarctic Peninsula

NASA Astrophysics Data System (ADS)

Couto, Nicole; Martinson, Douglas G.; Kohut, Josh; Schofield, Oscar

2017-07-01

We use autonomous underwater vehicles to characterize the spatial distribution of Upper Circumpolar Deep Water (UCDW) on the continental shelf of the West Antarctic Peninsula (WAP) and present the first near-synoptic measurements of mesoscale features (eddies) containing UCDW on the WAP. Thirty-three subsurface eddies with widths on the order of 10 km were detected during four glider deployments. Each eddy contributed an average of 5.8 × 1016 J to the subpycnocline waters, where a cross-shelf heat flux of 1.37 × 1019 J yr-1 is required to balance the diffusive loss of heat to overlying winter water and to the near-coastal waters. Approximately two-thirds of the heat coming onto the shelf diffuses across the pycnocline and one-third diffuses to the coastal waters; long-term warming of the subpycnocline waters is a small residual of this balance. Sixty percent of the profiles that contained UCDW were part of a coherent eddy. Between 20% and 53% of the lateral onshore heat flux to the WAP can be attributed to eddies entering Marguerite Trough, a feature in the southern part of the shelf which is known to be an important conduit for UCDW. A northern trough is identified as additional important location for eddy intrusion.

Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy

PubMed Central

Ankışhan, Haydar; Yılmaz, Derya

2013-01-01

Snoring, which may be decisive for many diseases, is an important indicator especially for sleep disorders. In recent years, many studies have been performed on the snore related sounds (SRSs) due to producing useful results for detection of sleep apnea/hypopnea syndrome (SAHS). The first important step of these studies is the detection of snore from SRSs by using different time and frequency domain features. The SRSs have a complex nature that is originated from several physiological and physical conditions. The nonlinear characteristics of SRSs can be examined with chaos theory methods which are widely used to evaluate the biomedical signals and systems, recently. The aim of this study is to classify the SRSs as snore/breathing/silence by using the largest Lyapunov exponent (LLE) and entropy with multiclass support vector machines (SVMs) and adaptive network fuzzy inference system (ANFIS). Two different experiments were performed for different training and test data sets. Experimental results show that the multiclass SVMs can produce the better classification results than ANFIS with used nonlinear quantities. Additionally, these nonlinear features are carrying meaningful information for classifying SRSs and are able to be used for diagnosis of sleep disorders such as SAHS. PMID:24194786

Interplay of Electrostatics and Hydrophobic Effects in the Metamorphic Protein Human Lymphotactin.

PubMed

Korkmaz, Elif Nihal; Volkman, Brian F; Cui, Qiang

2015-07-30

The human lymphotactin (hLtn) is a protein that features two native states both of which are physiologically relevant: it is a monomer (hLtn10) at 10 °C with 200 mM salt and a dimer (hLtn40) at 40 °C and without salt. Here we focus on the networks of electrostatic and hydrophobic interactions that display substantial changes upon the conversion from hLtn10 to hLtn40 since they are expected to modulate the relative stability of the two folds. In addition to the Arg 23-Arg 43 interaction discussed in previous work, we find several other like-charge pairs that are likely important to the stability of hLtn10. Free energy perturbation calculations are carried out to explicitly evaluate the contribution of the Arg 23-Arg 43 interaction to the hLtn10 stability. hLtn40 features a larger number of salt bridges, and a set of hydrophobic residues undergo major changes in the solvent accessible surface area between hLtn10 and hLtn40, pointing to their importance to the relative stability of the two folds. We also discuss the use of explicit and implicit solvent simulations for characterizing the conformational ensembles under different solution conditions.

Biographer: web-based editing and rendering of SBGN compliant biochemical networks.

PubMed

Krause, Falko; Schulz, Marvin; Ripkens, Ben; Flöttmann, Max; Krantz, Marcus; Klipp, Edda; Handorf, Thomas

2013-06-01

The rapid accumulation of knowledge in the field of Systems Biology during the past years requires advanced, but simple-to-use, methods for the visualization of information in a structured and easily comprehensible manner. We have developed biographer, a web-based renderer and editor for reaction networks, which can be integrated as a library into tools dealing with network-related information. Our software enables visualizations based on the emerging standard Systems Biology Graphical Notation. It is able to import networks encoded in various formats such as SBML, SBGN-ML and jSBGN, a custom lightweight exchange format. The core package is implemented in HTML5, CSS and JavaScript and can be used within any kind of web-based project. It features interactive graph-editing tools and automatic graph layout algorithms. In addition, we provide a standalone graph editor and a web server, which contains enhanced features like web services for the import and export of models and visualizations in different formats. The biographer tool can be used at and downloaded from the web page http://biographer.biologie.hu-berlin.de/. The different software packages, including a server-independent version as well as a web server for Windows and Linux based systems, are available at http://code.google.com/p/biographer/ under the open-source license LGPL

A geologic analysis of the Side-Looking Airborne Radar imagery of southern New England

USGS Publications Warehouse

Banks, Paul T.

1975-01-01

Analysis of the side looking airborn radar imagery of Massachusetts, Connecticut and Rhode Island indicates that radar shows the topography in great detail. Since bedrock geologic features are frequently expressed in the topography the radar lends itself to geologic interpretation. The radar was studied by comparisons with field mapped geologic data first at a scale of approximately 1:125,000 and then at a scale of 1:500,000. The larger scale comparison revealed that faults, minor faults, joint sets, bedding and foliation attitudes, lithology and lithologic contacts all have a topographic expression interpretable on the imagery. Surficial geologic features were far less visible on the imagery over most of the area studied. The smaller scale comparisons revealed a pervasive, near orthogonal fracture set cutting all types and ages of rock and trending roughly N40?E and N30?W. In certain places the strike of bedding and foliation attitudes and some lithologic Contacts were visible in addition to the fractures. Fracturing in southern New England is apparently far more important than has been previously recognized. This new information, together with the visibility of many bedding and foliation attitudes and lithologic contacts, indicates the importance of radar imagery in improving the geologic interpretation of an area.

Chemical analyses (raw laboratory data) and locality index maps of the Confederate Gulch area, Broadwater and Meagher Counties, Montana

USGS Publications Warehouse

,

1975-01-01

Analysis of the side looking airborn radar imagery of Massachusetts, Connecticut and Rhode Island indicates that radar shows the topography in great detail. Since bedrock geologic features are frequently expressed in the topography the radar lends itself to geologic interpretation. The radar was studied by comparisons with field mapped geologic data first at a scale of approximately 1:125,000 and then at a scale of 1:500,000. The larger scale comparison revealed that faults, minor faults, joint sets, bedding and foliation attitudes, lithology and lithologic contacts all have a topographic expression interpretable on the imagery. Surficial geologic features were far less visible on the imagery over most of the area studied. The smaller scale comparisons revealed a pervasive, near orthogonal fracture set cutting all types and ages of rock and trending roughly N40?E and N30?W. In certain places the strike of bedding and foliation attitudes and some lithologic Contacts were visible in addition to the fractures. Fracturing in southern New England is apparently far more important than has been previously recognized. This new information, together with the visibility of many bedding and foliation attitudes and lithologic contacts, indicates the importance of radar imagery in improving the geologic interpretation of an area.

Monitoring Geothermal Features in Yellowstone National Park with ATLAS Multispectral Imagery

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Berglund, Judith

2000-01-01

The National Park Service (NPS) must produce an Environmental Impact Statement for each proposed development in the vicinity of known geothermal resource areas (KGRAs) in Yellowstone National Park. In addition, the NPS monitors indicator KGRAs for environmental quality and is still in the process of mapping many geothermal areas. The NPS currently maps geothermal features with field survey techniques. High resolution aerial multispectral remote sensing in the visible, NIR, SWIR, and thermal spectral regions could enable YNP geothermal features to be mapped more quickly and in greater detail In response, Yellowstone Ecosystems Studies, in partnership with NASA's Commercial Remote Sensing Program, is conducting a study on the use of Airborne Terrestrial Applications Sensor (ATLAS) multispectral data for monitoring geothermal features in the Upper Geyser Basin. ATLAS data were acquired at 2.5 meter resolution on August 17, 2000. These data were processed into land cover classifications and relative temperature maps. For sufficiently large features, the ATLAS data can map geothermal areas in terms of geyser pools and hot springs, plus multiple categories of geothermal runoff that are apparently indicative of temperature gradients and microbial matting communities. In addition, the ATLAS maps clearly identify geyserite areas. The thermal bands contributed to classification success and to the computation of relative temperature. With masking techniques, one can assess the influence of geothermal features on the Firehole River. Preliminary results appear to confirm ATLAS data utility for mapping and monitoring geothermal features. Future work will include classification refinement and additional validation.

Flexible feature interface for multimedia sources

DOEpatents

Coffland, Douglas R [Livermore, CA

2009-06-09

A flexible feature interface for multimedia sources system that includes a single interface for the addition of features and functions to multimedia sources and for accessing those features and functions from remote hosts. The interface utilizes the export statement: export "C" D11Export void FunctionName(int argc, char ** argv,char * result, SecureSession *ctrl) or the binary equivalent of the export statement.

Selective heart rate variability analysis to account for uterine activity during labor and improve classification of fetal distress.

PubMed

Warmerdam, G J J; Vullings, R; Van Laar, J O E H; Van der Hout-Van der Jagt, M B; Bergmans, J W M; Schmitt, L; Oei, S G

2016-08-01

Cardiotocography (CTG) is currently the most often used technique for detection of fetal distress. Unfortunately, CTG has a poor specificity. Recent studies suggest that, in addition to CTG, information on fetal distress can be obtained from analysis of fetal heart rate variability (HRV). However, uterine contractions can strongly influence fetal HRV. The aim of this study is therefore to investigate whether HRV analysis for detection of fetal distress can be improved by distinguishing contractions from rest periods. Our results from feature selection indicate that HRV features calculated separately during contractions or during rest periods are more informative on fetal distress than HRV features that are calculated over the entire fetal heart rate. Furthermore, classification performance improved from a geometric mean of 69.0% to 79.6% when including the contraction-dependent HRV features, in addition to HRV features calculated over the entire fetal heart rate.

10 CFR 100.10 - Factors to be considered when evaluating sites.

Code of Federal Regulations, 2013 CFR

2013-01-01

... reactor incorporates unique or unusual features having a significant bearing on the probability or consequences of accidental release of radioactive materials; (4) The safety features that are to be engineered... radioactive fission products. In addition, the site location and the engineered features included as...

10 CFR 100.10 - Factors to be considered when evaluating sites.

Code of Federal Regulations, 2012 CFR

2012-01-01

... reactor incorporates unique or unusual features having a significant bearing on the probability or consequences of accidental release of radioactive materials; (4) The safety features that are to be engineered... radioactive fission products. In addition, the site location and the engineered features included as...

10 CFR 100.10 - Factors to be considered when evaluating sites.

Code of Federal Regulations, 2014 CFR

2014-01-01

... reactor incorporates unique or unusual features having a significant bearing on the probability or consequences of accidental release of radioactive materials; (4) The safety features that are to be engineered... radioactive fission products. In addition, the site location and the engineered features included as...

Operator for object recognition and scene analysis by estimation of set occupancy with noisy and incomplete data sets

NASA Astrophysics Data System (ADS)

Rees, S. J.; Jones, Bryan F.

1992-11-01

Once feature extraction has occurred in a processed image, the recognition problem becomes one of defining a set of features which maps sufficiently well onto one of the defined shape/object models to permit a claimed recognition. This process is usually handled by aggregating features until a large enough weighting is obtained to claim membership, or an adequate number of located features are matched to the reference set. A requirement has existed for an operator or measure capable of a more direct assessment of membership/occupancy between feature sets, particularly where the feature sets may be defective representations. Such feature set errors may be caused by noise, by overlapping of objects, and by partial obscuration of features. These problems occur at the point of acquisition: repairing the data would then assume a priori knowledge of the solution. The technique described in this paper offers a set theoretical measure for partial occupancy defined in terms of the set of minimum additions to permit full occupancy and the set of locations of occupancy if such additions are made. As is shown, this technique permits recognition of partial feature sets with quantifiable degrees of uncertainty. A solution to the problems of obscuration and overlapping is therefore available.

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.

Proposition of novel classification approach and features for improved real-time arrhythmia monitoring.

PubMed

Kim, Yoon Jae; Heo, Jeong; Park, Kwang Suk; Kim, Sungwan

2016-08-01

Arrhythmia refers to a group of conditions in which the heartbeat is irregular, fast, or slow due to abnormal electrical activity in the heart. Some types of arrhythmia such as ventricular fibrillation may result in cardiac arrest or death. Thus, arrhythmia detection becomes an important issue, and various studies have been conducted. Additionally, an arrhythmia detection algorithm for portable devices such as mobile phones has recently been developed because of increasing interest in e-health care. This paper proposes a novel classification approach and features, which are validated for improved real-time arrhythmia monitoring. The classification approach that was employed for arrhythmia detection is based on the concept of ensemble learning and the Taguchi method and has the advantage of being accurate and computationally efficient. The electrocardiography (ECG) data for arrhythmia detection was obtained from the MIT-BIH Arrhythmia Database (n=48). A novel feature, namely the heart rate variability calculated from 5s segments of ECG, which was not considered previously, was used. The novel classification approach and feature demonstrated arrhythmia detection accuracy of 89.13%. When the same data was classified using the conventional support vector machine (SVM), the obtained accuracy was 91.69%, 88.14%, and 88.74% for Gaussian, linear, and polynomial kernels, respectively. In terms of computation time, the proposed classifier was 5821.7 times faster than conventional SVM. In conclusion, the proposed classifier and feature showed performance comparable to those of previous studies, while the computational complexity and update interval were highly reduced. Copyright © 2016 Elsevier Ltd. All rights reserved.

Remote sensing of planetary properties and biosignatures on extrasolar terrestrial planets

NASA Technical Reports Server (NTRS)

Des Marais, David J.; Harwit, Martin O.; Jucks, Kenneth W.; Kasting, James F.; Lin, Douglas N C.; Lunine, Jonathan I.; Schneider, Jean; Seager, Sara; Traub, Wesley A.; Woolf, Neville J.

2002-01-01

The major goals of NASA's Terrestrial Planet Finder (TPF) and the European Space Agency's Darwin missions are to detect terrestrial-sized extrasolar planets directly and to seek spectroscopic evidence of habitable conditions and life. Here we recommend wavelength ranges and spectral features for these missions. We assess known spectroscopic molecular band features of Earth, Venus, and Mars in the context of putative extrasolar analogs. The preferred wavelength ranges are 7-25 microns in the mid-IR and 0.5 to approximately 1.1 microns in the visible to near-IR. Detection of O2 or its photolytic product O3 merits highest priority. Liquid H2O is not a bioindicator, but it is considered essential to life. Substantial CO2 indicates an atmosphere and oxidation state typical of a terrestrial planet. Abundant CH4 might require a biological source, yet abundant CH4 also can arise from a crust and upper mantle more reduced than that of Earth. The range of characteristics of extrasolar rocky planets might far exceed that of the Solar System. Planetary size and mass are very important indicators of habitability and can be estimated in the mid-IR and potentially also in the visible to near-IR. Additional spectroscopic features merit study, for example, features created by other biosignature compounds in the atmosphere or on the surface and features due to Rayleigh scattering. In summary, we find that both the mid-IR and the visible to near-IR wavelength ranges offer valuable information regarding biosignatures and planetary properties; therefore both merit serious scientific consideration for TPF and Darwin.

A parametric texture model based on deep convolutional features closely matches texture appearance for humans.

PubMed

Wallis, Thomas S A; Funke, Christina M; Ecker, Alexander S; Gatys, Leon A; Wichmann, Felix A; Bethge, Matthias

2017-10-01

Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parametric model of texture appearance (convolutional neural network [CNN] model) that uses the features encoded by a deep CNN (VGG-19) with two other models: the venerable Portilla and Simoncelli model and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery ("parafoveal") and when observers were able to make eye movements to all three patches ("inspection"). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler Portilla and Simoncelli model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the Portilla and Simoncelli model (nine compared to four textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesize textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions.

Remote sensing of planetary properties and biosignatures on extrasolar terrestrial planets.

PubMed

Des Marais, David J; Harwit, Martin O; Jucks, Kenneth W; Kasting, James F; Lin, Douglas N C; Lunine, Jonathan I; Schneider, Jean; Seager, Sara; Traub, Wesley A; Woolf, Neville J

2002-01-01

The major goals of NASA's Terrestrial Planet Finder (TPF) and the European Space Agency's Darwin missions are to detect terrestrial-sized extrasolar planets directly and to seek spectroscopic evidence of habitable conditions and life. Here we recommend wavelength ranges and spectral features for these missions. We assess known spectroscopic molecular band features of Earth, Venus, and Mars in the context of putative extrasolar analogs. The preferred wavelength ranges are 7-25 microns in the mid-IR and 0.5 to approximately 1.1 microns in the visible to near-IR. Detection of O2 or its photolytic product O3 merits highest priority. Liquid H2O is not a bioindicator, but it is considered essential to life. Substantial CO2 indicates an atmosphere and oxidation state typical of a terrestrial planet. Abundant CH4 might require a biological source, yet abundant CH4 also can arise from a crust and upper mantle more reduced than that of Earth. The range of characteristics of extrasolar rocky planets might far exceed that of the Solar System. Planetary size and mass are very important indicators of habitability and can be estimated in the mid-IR and potentially also in the visible to near-IR. Additional spectroscopic features merit study, for example, features created by other biosignature compounds in the atmosphere or on the surface and features due to Rayleigh scattering. In summary, we find that both the mid-IR and the visible to near-IR wavelength ranges offer valuable information regarding biosignatures and planetary properties; therefore both merit serious scientific consideration for TPF and Darwin.

A novel and efficient technique for identification and classification of GPCRs.

PubMed

Gupta, Ravi; Mittal, Ankush; Singh, Kuldip

2008-07-01

G-protein coupled receptors (GPCRs) play a vital role in different biological processes, such as regulation of growth, death, and metabolism of cells. GPCRs are the focus of significant amount of current pharmaceutical research since they interact with more than 50% of prescription drugs. The dipeptide-based support vector machine (SVM) approach is the most accurate technique to identify and classify the GPCRs. However, this approach has two major disadvantages. First, the dimension of dipeptide-based feature vector is equal to 400. The large dimension makes the classification task computationally and memory wise inefficient. Second, it does not consider the biological properties of protein sequence for identification and classification of GPCRs. In this paper, we present a novel-feature-based SVM classification technique. The novel features are derived by applying wavelet-based time series analysis approach on protein sequences. The proposed feature space summarizes the variance information of seven important biological properties of amino acids in a protein sequence. In addition, the dimension of the feature vector for proposed technique is equal to 35. Experiments were performed on GPCRs protein sequences available at GPCRs Database. Our approach achieves an accuracy of 99.9%, 98.06%, 97.78%, and 94.08% for GPCR superfamily, families, subfamilies, and subsubfamilies (amine group), respectively, when evaluated using fivefold cross-validation. Further, an accuracy of 99.8%, 97.26%, and 97.84% was obtained when evaluated on unseen or recall datasets of GPCR superfamily, families, and subfamilies, respectively. Comparison with dipeptide-based SVM technique shows the effectiveness of our approach.

The Role of Social Capital in Educational Aspirations of Rural Youth*

PubMed Central

Byun, Soo-yong; Meece, Judith L.; Irvin, Matthew J.; Hutchins, Bryan C.

2013-01-01

Drawing on a recent national survey of rural high school students, this study investigated the relationship between social capital and educational aspirations of rural youth. Results showed that various process features of family and school social capital were important to predict rural youth's educational aspirations beyond sociodemographic background. In particular, parents' and teachers' educational expectations for their child and student respectively were positively related to educational aspirations of rural youth. In addition, discussion with parents about college was positively related to educational aspirations of rural youth. On the other hand, there was little evidence to suggest that number of siblings and school proportions of students on free lunch and minority students are related to educational aspirations of rural youth, after controlling for the other variables. The authors highlight unique features of rural families, schools, and communities that may combine to explain the complexity of the role of social capital in shaping educational aspirations of rural youth. PMID:24039302

David Florida Laboratory Thermal Vacuum Data Processing System

NASA Technical Reports Server (NTRS)

Choueiry, Elie

1994-01-01

During 1991, the Space Simulation Facility conducted a survey to assess the requirements and analyze the merits for purchasing a new thermal vacuum data processing system for its facilities. A new, integrated, cost effective PC-based system was purchased which uses commercial off-the-shelf software for operation and control. This system can be easily reconfigured and allows its users to access a local area network. In addition, it provides superior performance compared to that of the former system which used an outdated mini-computer and peripheral hardware. This paper provides essential background on the old data processing system's features, capabilities, and the performance criteria that drove the genesis of its successor. This paper concludes with a detailed discussion of the thermal vacuum data processing system's components, features, and its important role in supporting our space-simulation environment and our capabilities for spacecraft testing. The new system was tested during the ANIK E spacecraft test, and was fully operational in November 1991.

Nearest neighbor 3D segmentation with context features

NASA Astrophysics Data System (ADS)

Hristova, Evelin; Schulz, Heinrich; Brosch, Tom; Heinrich, Mattias P.; Nickisch, Hannes

2018-03-01

Automated and fast multi-label segmentation of medical images is challenging and clinically important. This paper builds upon a supervised machine learning framework that uses training data sets with dense organ annotations and vantage point trees to classify voxels in unseen images based on similarity of binary feature vectors extracted from the data. Without explicit model knowledge, the algorithm is applicable to different modalities and organs, and achieves high accuracy. The method is successfully tested on 70 abdominal CT and 42 pelvic MR images. With respect to ground truth, an average Dice overlap score of 0.76 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. Additionally, we benchmark several variations of the main components of the method and reduce the computation time by up to 47% without significant loss of accuracy. The segmentation results are - for a nearest neighbor method - surprisingly accurate, robust as well as data and time efficient.

Water vapor profiling using microwave radiometry

NASA Technical Reports Server (NTRS)

Wang, J. R.; Wilheit, T. T.

1988-01-01

Water vapor is one of the most important constituents in the Earth's atmosphere. Its spatial and temporal variations affect a wide spectrum of meteorological phenomena ranging from the formation of clouds to the development of severe storms. The passive microwave technique offers an excellent means for water vapor measurements. It can provide both day and night coverage under most cloud conditions. Two water vapor absorption features, at 22 and 183 GHz, were explored in the past years. The line strengths of these features differ by nearly two orders of magnitude. As a consequence, the techniques and the final products of water vapor measurements are also quite different. The research effort in the past few years was to improve and extend the retrieval algorithm to the measurements of water vapor profiles under cloudy conditions. In addition, the retrieval of total precipitable water using 183 GHz measurements, but in a manner analogous to the use of 22 GHz measurements, to increase measurement sensitivity for atmospheres of very low moisture content was also explored.

Plasmonic spectral tunability of conductive ternary nitrides

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

Kassavetis, S.; Patsalas, P., E-mail: ppats@physics.auth.gr; Bellas, D. V.

2016-06-27

Conductive binary transition metal nitrides, such as TiN and ZrN, have emerged as a category of promising alternative plasmonic materials. In this work, we show that ternary transition metal nitrides such as Ti{sub x}Ta{sub 1−x}N, Ti{sub x}Zr{sub 1−x}N, Ti{sub x}Al{sub 1−x}N, and Zr{sub x}Ta{sub 1−x}N share the important plasmonic features with their binary counterparts, while having the additional asset of the exceptional spectral tunability in the entire visible (400–700 nm) and UVA (315–400 nm) spectral ranges depending on their net valence electrons. In particular, we demonstrate that such ternary nitrides can exhibit maximum field enhancement factors comparable with gold in the aforementionedmore » broadband range. We also critically evaluate the structural features that affect the quality factor of the plasmon resonance and we provide rules of thumb for the selection and growth of materials for nitride plasmonics.« less

Serotonin precursor (5-hydroxytryptophan) causes substantial changes in the fighting behavior of male crickets, Gryllus bimaculatus.

PubMed

Dyakonova, V E; Krushinsky, A L

2013-07-01

This study demonstrates that injection of the serotonin precursor 5-HTP causes substantial changes in the behavioral state, fighting behavior and ability to establish winner-loser relationships in male crickets (Gryllus bimaculatus). The characteristic features of 5-HTP-treated crickets include an elevated posture, enhanced general activity, longer duration of fighting, enhanced rival singing and a decreased ability to produce a clear fight loser. In addition, 5-HTP-treated males showed a slightly delayed latency to spread their mandibles, a decreased number of attacks and an equal potential to win in comparison to controls (physiological solution-treated males). The obtained results imply a significant role for serotonin in the regulation of social status-related behaviors in G. bimaculatus. Specifically, these data indicate that a decrease in serotonergic activity may be functionally important for the control of loser behavior and that some behavioral features of dominant male crickets are likely to be connected with the activation of the serotonergic system.

Co-incidence of Turner syndrome and Duchenne muscular dystrophy - an important problem for the clinician.

PubMed

Kaczorowska, Ewa; Zimowski, Janusz; Cichoń-Kotek, Monika; Mrozińska, Agnieszka; Purzycka, Joanna; Wierzba, Jolanta; Limon, Janusz; Lipska-Ziętkiewicz, Beata S

Turner syndrome is a relatively common chromosomal disorder which affects about one in 2000 live born females. Duchenne muscular dystrophy is an X-linked recessive disorder affecting 1:3600 live born males. Considering the above, the coexistence of these two diseases may occur only anecdotally. Here, we report a 4 ½ year-old female with classical 45,X Turner syndrome who also had Duchenne muscular dystrophy caused by a point mutation in the dystrophin gene (c.9055delG). The patient showed the typical phenotype of Turner syndrome including distinctive dysmorphic features (short neck, low posterior hairline, wide position of nipples), aortic coarctation and feet lymphedema. Besides, she presented with an unusually early beginning of muscular dystrophy symptoms with infantile-onset motor developmental delay, intellectual disability and early calf muscular hypertrophy. The coexistence of an X-linked recessive disorder should be considered in women affected by Turner syndrome presenting with additional atypical clinical features.

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

Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging.

PubMed

Kamruzzaman, Mohammed; Elmasry, Gamal; Sun, Da-Wen; Allen, Paul

2013-11-01

The purpose of this study was to develop and test a hyperspectral imaging system (900-1700 nm) to predict instrumental and sensory tenderness of lamb meat. Warner-Bratzler shear force (WBSF) values and sensory scores by trained panellists were collected as the indicator of instrumental and sensory tenderness, respectively. Partial least squares regression models were developed for predicting instrumental and sensory tenderness with reasonable accuracy (Rcv=0.84 for WBSF and 0.69 for sensory tenderness). Overall, the results confirmed that the spectral data could become an interesting screening tool to quickly categorise lamb steaks in good (i.e. tender) and bad (i.e. tough) based on WBSF values and sensory scores with overall accuracy of about 94.51% and 91%, respectively. Successive projections algorithm (SPA) was used to select the most important wavelengths for WBSF prediction. Additionally, textural features from Gray Level Co-occurrence Matrix (GLCM) were extracted to determine the correlation between textural features and WBSF values. Copyright © 2013 Elsevier Ltd. All rights reserved.

Pathview Web: user friendly pathway visualization and data integration

PubMed Central

Pant, Gaurav; Bhavnasi, Yeshvant K.; Blanchard, Steven G.; Brouwer, Cory

2017-01-01

Abstract Pathway analysis is widely used in omics studies. Pathway-based data integration and visualization is a critical component of the analysis. To address this need, we recently developed a novel R package called Pathview. Pathview maps, integrates and renders a large variety of biological data onto molecular pathway graphs. Here we developed the Pathview Web server, as to make pathway visualization and data integration accessible to all scientists, including those without the special computing skills or resources. Pathview Web features an intuitive graphical web interface and a user centered design. The server not only expands the core functions of Pathview, but also provides many useful features not available in the offline R package. Importantly, the server presents a comprehensive workflow for both regular and integrated pathway analysis of multiple omics data. In addition, the server also provides a RESTful API for programmatic access and conveniently integration in third-party software or workflows. Pathview Web is openly and freely accessible at https://pathview.uncc.edu/. PMID:28482075

Controversies in imaging of hepatocellular carcinoma: multidetector CT (MDCT)

PubMed Central

Silverman, Paul M; Szklaruk, Janio

2005-01-01

Primary hepatocellular carcinoma (HCC) is a significant tumor worldwide and represents the most common primary hepatic neoplasm. Staging criteria are important for appreciation of timely work up of these neoplasms in contradiction with surgical colleagues. This article demonstrates the appearance of HCC on multiphasic, multidetector CT (MDCT) and relates these findings to current staging criteria. The variable appearance on different planes of contrast is critical to appreciate in staging this neoplasm. The hypervascular nature of the primary tumor makes MDCT and three-phase imaging a critical feature in the detection and characterization of this tumor. This is especially critical in the patients who are candidates for surgical resection. Additionally, MDCT has allowed arterial phase imaging to define the vascular supply of the tumor. An accurate representation of the size and number of lesions is critical in not only the initial staging but also the follow-up of hepatocellular carcinoma. The post-treatment features including the appearance post-surgically and after radiofrequency ablation can be well appreciated on MDCT. PMID:16361147